system

Abstract

The system according to this embodiment aims to analyze the driver's brainwaves and biosignals and provide personalized feedback. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the driver's brain waves and biological signals. The analysis unit analyzes the data collected by the collection unit in real time. The generation unit generates personalized feedback based on the analysis results obtained by the analysis unit. The provision unit provides the feedback generated by the generation unit 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 method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, the state of the driver has not been sufficiently grasped in real time and appropriate feedback has not been provided, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze the brain waves and biological signals of the driver and provide individualized feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects the driver's brain waves and biological signals. The analysis unit analyzes the data collected by the data collection unit in real time. The data generation unit generates personalized feedback based on the analysis results obtained by the analysis unit. The data provision unit provides the feedback generated by the data generation unit to the driver. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the driver's brainwaves and biosignals and provide personalized feedback. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple 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 safety driving support system according to an embodiment of the present invention is a system that monitors the driver's brainwaves and biosignals in real time and provides more advanced safety driving support by combining a brain-computer interface (BCI) and generative AI. This safety driving support system collects biodata such as the driver's brainwaves, heart rate, and skin electrical responses, and analyzes it in real time to evaluate the driver's cognitive state, stress level, and emotional state. The generative AI learns the driver's past driving patterns and current state and provides personalized feedback. For example, if attention is low, it generates a warning message such as "Your concentration is low. Take a deep breath and refresh yourself," and if it determines that fatigue is accumulating, it suggests "We recommend taking a break at the next service area." In addition, the generative AI automatically adjusts the in-car environment according to the driver's state. For example, if the driver feels drowsy, it plays music with an awakening effect and brightens the lights, and if the driver feels stressed, it adjusts the temperature and airflow to have a relaxing effect. Furthermore, the generative AI also analyzes road conditions and the movements of other vehicles and predicts potential dangers. For example, if the risk of collision with another vehicle increases, it warns the driver and automatically applies the brakes if necessary. Furthermore, it proposes the optimal route in real time, taking into account traffic congestion and accident information. The generating AI learns the driver's reactions and preferences, evolving its feedback and suggestions to be more effective. For example, it can be applied to supporting elderly drivers, training new drivers, and managing commercial vehicle drivers. In the future, we envision integration with other vehicles and infrastructure, utilization of multimodal data such as eye tracking and voice recognition, and support for the transition to fully autonomous driving. As a result, the safety driving support system can support safe driving by analyzing the driver's brainwaves and biosignals in real time and providing personalized feedback.

[0029] The safety driving support system according to the embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects the driver's brain waves and biosignals. The data collection unit collects biosignals such as the driver's brain waves, heart rate, and skin electrical responses using, for example, a headset or sensors built into the vehicle. The data collection unit can collect brain waves using, for example, an EEG headset. The data collection unit can also collect heart rate using a heart rate sensor. Furthermore, the data collection unit can collect skin electrical responses using a skin electrical response sensor. The analysis unit analyzes the data collected by the data collection unit in real time. The analysis unit can, for example, analyze the collected brain wave data to evaluate the driver's cognitive state. The analysis unit can also, for example, analyze the collected heart rate data to evaluate the driver's stress level. The analysis unit can also, for example, analyze the collected skin electrical response data to evaluate the driver's emotional state. The data generation unit generates personalized feedback based on the analysis results obtained by the analysis unit. The generation unit generates feedback to enhance concentration when, for example, the driver's cognitive state is impaired. The generation unit can also generate feedback to help the driver relax when, for example, the driver's stress level is high. The generation unit can also generate feedback to help the driver stabilize when, for example, the driver's emotional state is unstable. The provisioning unit provides the feedback generated by the generation unit to the driver. The provisioning unit provides feedback using, for example, an audible alert. The provisioning unit can also provide feedback using, for example, a visual presentation. The provisioning unit can also provide feedback using, for example, vibration feedback. As a result, the safety driving support system according to the embodiment can support safe driving by analyzing the driver's brainwaves and biosignals in real time and providing personalized feedback.

[0030] The data collection unit collects the driver's brainwaves and biosignals. For example, it uses a headset or sensors integrated into the vehicle to collect biometric data such as the driver's brainwaves, heart rate, and skin electrical activity. Specifically, an EEG headset can be used to collect brainwaves. This headset is worn on the driver's head and monitors the brain's electrical activity in real time. The brainwave data is used to assess the driver's concentration and fatigue level. The data collection unit can also collect heart rate using a heart rate sensor. This sensor is attached to the driver's fingertips or wrists and measures heart rate variability in real time. The heart rate data is used to assess the driver's stress level and agitation. Furthermore, the data collection unit can collect skin electrical activity using a skin electrical activity sensor. This sensor is attached to the driver's skin and detects electrical changes in the skin. The skin electrical activity data is used to assess the driver's emotional state and tension level. These sensors can also be integrated into the vehicle's seats or steering wheel, allowing data to be collected while the driver is in a natural driving position. The collected data is transmitted wirelessly to a central database and analyzed in real time. This allows the data collection unit to efficiently and accurately collect the driver's biosignals, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit in real time. For example, the analysis unit analyzes collected electroencephalogram (EEG) data to evaluate the driver's cognitive state. Specifically, it uses advanced signal processing algorithms with AI to evaluate the driver's concentration and fatigue level from the EEG data. For example, by analyzing the ratio of alpha waves to beta waves, the driver's level of concentration can be understood. It can also analyze collected heart rate data to evaluate the driver's stress level. By analyzing heart rate variability and changes in heart rate intervals, the driver's stress and relaxation levels can be evaluated. Furthermore, it can analyze collected electrocutaneous response data to evaluate the driver's emotional state. By analyzing changes in electrocutaneous responses, the driver's level of tension and emotional changes can be understood. The analysis unit integrates this data to perform a comprehensive evaluation and understand the driver's state in real time. In addition, the analysis unit can also predict changes in the driver's state by utilizing past data and statistical information. For example, based on past driving data, it can predict the driver's reaction under specific circumstances and issue a warning in advance. This allows the analysis unit to accurately evaluate the driver's condition and provide a foundation for offering appropriate feedback.

[0032] The generation unit generates personalized feedback based on the analysis results obtained by the analysis unit. For example, if the driver's cognitive state is impaired, the generation unit generates feedback to enhance concentration. Specifically, if it determines that the driver's concentration is impaired, the generation unit generates feedback to draw the driver's attention using voice alerts or visual presentations. It can also generate feedback to help the driver relax if their stress level is high. For example, it can generate feedback that plays relaxing music or provides guidance to encourage deep breathing. Furthermore, if the driver's emotional state is unstable, it can generate feedback to help stabilize it. For example, if it determines that the driver's emotional state is unstable, the generation unit generates feedback that displays relaxing images or messages. The generation unit appropriately generates this feedback according to the driver's state and sends it to the delivery unit. The generation unit can also utilize AI-based natural language processing technology to select appropriate words and expressions for the driver. This allows the generation unit to quickly and accurately generate personalized feedback according to the driver's state and send it to the delivery unit.

[0033] The providing unit provides feedback generated by the generating unit to the driver. The providing unit provides feedback using, for example, voice alerts. Specifically, it plays an audio message to alert the driver through the in-car speakers. Feedback can also be provided using visual presentations. For example, it displays an alert message or image on the in-car display. Furthermore, feedback can also be provided using vibration feedback. For example, it uses vibration devices incorporated into the seat or steering wheel to alert the driver through vibration. The providing unit combines these feedback means to provide the driver with optimal feedback. The providing unit can also adjust the content and intensity of the feedback according to the driver's condition and driving situation. For example, if the driver is driving on a highway, it may use strong voice alerts and vibration feedback to alert the driver, while if the driver is driving in an urban area, it may use gentle voice messages and visual presentations to alert the driver. This allows the providing unit to provide appropriate feedback to the driver and support safe driving. Furthermore, the providing unit can monitor the driver's response to the feedback and evaluate the effectiveness of the feedback. This allows the providing unit to continuously improve the content and method of feedback and improve the overall performance of the system.

[0034] The data collection unit collects biometric data such as the driver's brainwaves, heart rate, and skin electrical responses using a headset or sensors embedded in the vehicle. For example, the data collection unit can collect the driver's brainwaves using a headset. For example, the data collection unit can collect the driver's heart rate using a heart rate sensor embedded in the vehicle. For example, the data collection unit can collect the driver's skin electrical responses using a skin electrical response sensor embedded in the vehicle. This allows for an accurate understanding of the driver's condition by collecting biometric data using a headset or sensors embedded in the vehicle. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input brainwave data acquired by the headset into a generating AI and have the generating AI perform an analysis of the brainwave data.

[0035] The analysis unit analyzes the collected data in real time to evaluate the driver's cognitive state, stress level, and emotional state. For example, the analysis unit can analyze collected electroencephalogram (EEG) data in real time to evaluate the driver's cognitive state. For example, the analysis unit can analyze collected heart rate data in real time to evaluate the driver's stress level. For example, the analysis unit can analyze collected electrocutaneous response (ESD) data in real time to evaluate the driver's emotional state. This allows for immediate evaluation of the driver's condition by analyzing the collected data in real time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected EEG data into a generating AI and have the generating AI perform an evaluation of the driver's cognitive state.

[0036] The generation unit learns the driver's past driving patterns and current state, and generates personalized feedback. For example, the generation unit can learn the driver's past driving patterns and generate feedback based on the current state. For example, the generation unit can learn the driver's current state and generate feedback based on past driving patterns. For example, the generation unit can learn the driver's past driving patterns and current state and generate personalized feedback. This allows for the provision of personalized feedback by learning the driver's past driving patterns and current state. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the driver's past driving patterns and current state into a generation AI and have the generation AI perform the generation of personalized feedback.

[0037] The service provider provides the generated feedback to the driver. The service provider can, for example, provide the generated feedback to the driver as an audio alert. The service provider can, for example, provide the generated feedback to the driver as a visual presentation. The service provider can, for example, provide the generated feedback to the driver as vibration feedback. By providing the generated feedback to the driver, appropriate support can be provided according to the driver's state. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated feedback into a generating AI and have the generating AI execute a method for providing it to the driver.

[0038] The generation unit automatically adjusts the in-car environment according to the driver's condition. For example, if the driver is feeling drowsy, the generation unit can play music with an awakening effect. For example, if the driver is feeling stressed, the generation unit can adjust the temperature and airflow to have a relaxing effect. For example, the generation unit can adjust the lighting according to the driver's condition. In this way, the driver's comfort and safety are improved by automatically adjusting the in-car environment according to the driver's condition. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the driver's condition to the generation AI and have the generation AI perform the adjustment of the in-car environment.

[0039] The generation unit analyzes road conditions and the movements of other vehicles to predict potential hazards. For example, the generation unit can analyze road conditions and predict potential hazards. For example, the generation unit can analyze the movements of other vehicles and predict potential hazards. For example, the generation unit can analyze road conditions and the movements of other vehicles and predict potential hazards. This allows the generation unit to predict potential hazards and provide warnings to drivers by analyzing road conditions and the movements of other vehicles. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input road conditions and the movements of other vehicles into a generation AI and have the generation AI perform the prediction of potential hazards.

[0040] The generation unit proposes the optimal route in real time, taking into account traffic congestion and accident information. For example, the generation unit can propose the optimal route based on traffic congestion information. For example, the generation unit can propose the optimal route based on accident information. For example, the generation unit can propose the optimal route based on both traffic congestion and accident information. This improves the driver's travel efficiency by proposing the optimal route based on traffic congestion and accident information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input traffic congestion information and accident information into a generation AI and have the generation AI propose the optimal route.

[0041] The data collection unit analyzes the driver's past biometric data and selects the optimal sensor placement. For example, the data collection unit can place sensors in areas where heart rate fluctuations are large based on past data. For example, the data collection unit can place sensors in areas where brain wave fluctuations are small based on past data. For example, the data collection unit can place sensors in areas where skin electrical responses are sensitive based on past data. In this way, the optimal sensor placement can be selected by analyzing the driver's past biometric data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's past biometric data into a generating AI and have the generating AI select the optimal sensor placement.

[0042] The data collection unit filters the collected biometric data based on the driver's current driving conditions and environment. For example, the data collection unit can prioritize the collection of heart rate and electroencephalogram (EEG) data when driving on a highway. For example, the data collection unit can prioritize the collection of skin electrical response and heart rate variability data when driving in urban areas. For example, the data collection unit can prioritize the collection of EEG and heart rate data when driving at night. This allows for the priority collection of necessary data by filtering based on the driver's current driving conditions and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's current driving conditions and environmental data into a generating AI and have the generating AI perform the filtering.

[0043] The data collection unit prioritizes the collection of highly relevant data, taking into account the driver's geographical location information, when collecting biometric data. For example, the data collection unit can prioritize the collection of heart rate and electroencephalogram (EEG) data when driving on a highway. For example, the data collection unit can prioritize the collection of skin electrical response and heart rate variability data when driving in urban areas. For example, the data collection unit can prioritize the collection of EEG and heart rate data when driving in mountainous areas. This allows for the efficient collection of necessary data by prioritizing the collection of highly relevant data, taking into account the driver's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0044] The data collection unit analyzes the driver's social media activity when collecting biometric data and collects relevant data. For example, the data collection unit can prioritize collecting heart rate and skin electrophysiological response data when the driver is stressed. For example, the data collection unit can prioritize collecting electroencephalogram (EEG) data when the driver is relaxed. For example, the data collection unit can prioritize collecting heart rate variability data when the driver is tired. This allows for the efficient collection of relevant data by analyzing the driver's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0045] The analysis unit improves analysis accuracy by referring to past analysis data during analysis. For example, the analysis unit can place sensors in areas where heart rate fluctuations are large based on past data. For example, the analysis unit can place sensors in areas where brain wave fluctuations are small based on past data. For example, the analysis unit can place sensors in areas where skin electrical responses are sensitive based on past data. In this way, analysis accuracy is improved by referring to past analysis data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0046] The analysis unit performs the analysis while taking the driver's attribute information into consideration. For example, in the case of an elderly driver, the analysis unit can prioritize heart rate and skin electrical response data. For example, in the case of a novice driver, the analysis unit can prioritize electroencephalogram (EEG) data. For example, in the case of a commercial vehicle driver, the analysis unit can prioritize heart rate variability data. By considering the driver's attribute information during the analysis, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the driver's attribute information into a generating AI and have the generating AI perform the analysis.

[0047] The analysis unit performs its analysis while considering the geographical distribution of drivers. For example, when driving on a highway, the analysis unit can prioritize heart rate and electroencephalogram (EEG) data. For example, when driving in an urban area, the analysis unit can prioritize skin electrical response and heart rate variability data. For example, when driving in a mountainous area, the analysis unit can prioritize EEG and heart rate data. By considering the geographical distribution of drivers, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of drivers into a generating AI and have the generating AI perform the analysis.

[0048] The analysis unit improves the accuracy of the analysis by referring to relevant literature during the analysis. For example, the analysis unit can refer to the latest research on heart rate variability. For example, the analysis unit can refer to the latest research on electroencephalogram (EEG) variability. For example, the analysis unit can refer to the latest research on skin electroreactivity. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0049] The generation unit generates optimal feedback by referring to the driver's past driving patterns when generating feedback. For example, the generation unit can place sensors in areas where heart rate fluctuations are large based on past data. For example, the generation unit can place sensors in areas where brain wave fluctuations are small based on past data. For example, the generation unit can place sensors in areas where skin electrical responses are sensitive based on past data. This allows the generation unit to generate optimal feedback by referring to the driver's past driving patterns. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the driver's past driving pattern data into a generation AI and have the generation AI perform the generation of optimal feedback.

[0050] The generation unit customizes the means of feedback based on the driver's current driving conditions when generating feedback. For example, the generation unit can prioritize voice feedback when driving on a highway. For example, the generation unit can prioritize visual feedback when driving in urban areas. For example, the generation unit can prioritize vibration feedback when driving at night. This allows for more effective feedback by customizing the means of feedback based on the driver's current driving conditions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the driver's current driving condition data into the generation AI and have the generation AI customize the means of feedback.

[0051] The generation unit generates optimal feedback by considering the driver's geographical location information when generating feedback. For example, the generation unit can prioritize voice feedback when driving on a highway. For example, the generation unit can prioritize visual feedback when driving in urban areas. For example, the generation unit can prioritize vibration feedback when driving in mountainous areas. By generating optimal feedback while considering the driver's geographical location information, more effective feedback can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the driver's geographical location information into the generation AI and have the generation AI perform the generation of optimal feedback.

[0052] The generation unit analyzes the driver's social media activity when generating feedback and proposes a means of providing feedback. For example, the generation unit can generate feedback that encourages the driver to take deep breaths to relax if the driver is feeling stressed. For example, the generation unit can generate feedback that draws the driver's attention if the driver is relaxed. For example, the generation unit can generate feedback that encourages the driver to take a break if the driver is tired. By analyzing the driver's social media activity, the optimal means of providing feedback can be proposed. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the driver's social media activity data into a generation AI and have the generation AI propose a means of providing feedback.

[0053] The feedback unit selects the optimal feedback method by referring to the driver's past response history when providing feedback. For example, the feedback unit can prioritize voice feedback if past data shows that voice feedback was effective. For example, the feedback unit can prioritize visual feedback if past data shows that visual feedback was effective. For example, the feedback unit can prioritize vibration feedback if past data shows that vibration feedback was effective. This allows the optimal feedback method to be selected by referring to the driver's past response history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the driver's past response history data into a generating AI and have the generating AI select the optimal feedback method.

[0054] The feedback unit selects the optimal feedback method when providing feedback, taking into account the driver's device information. For example, if the driver is using a smartphone, the feedback unit can provide visual feedback that matches the screen size. For example, if the driver is using a tablet, the feedback unit can provide visual feedback optimized for a larger screen. For example, if the driver is using a smartwatch, the feedback unit can provide concise and highly visible vibration feedback. By selecting the optimal feedback method considering the driver's device information, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the driver's device information into a generating AI and have the generating AI select the optimal feedback method.

[0055] The feedback unit selects the optimal feedback method when providing feedback, taking into account the driver's current driving conditions. For example, the feedback unit may prioritize voice feedback when driving on a highway. For example, the feedback unit may prioritize visual feedback when driving in urban areas. For example, the feedback unit may prioritize vibration feedback when driving at night. By selecting the optimal feedback method considering the driver's current driving conditions, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit may input the driver's current driving condition data into a generating AI and have the generating AI select the optimal feedback method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze the driver's driving history and optimize sensor placement based on specific driving patterns. For example, a pressure sensor can be placed near the brake pedal for drivers who frequently use sudden braking based on past data. For drivers who frequently use highways, the heart rate sensor mounted on the steering wheel can be enhanced. Furthermore, for drivers who frequently drive at night, sensors can be placed around the eyes to detect visual fatigue. This optimizes sensor placement based on the driver's driving history, enabling more accurate data collection.

[0058] The generator can automatically adjust the vehicle's performance settings based on the driver's driving style. For example, a driver who prefers sporty driving can be given settings that improve engine response. A driver who prioritizes eco-driving can be given settings that optimize fuel efficiency. A driver who frequently drives long distances can be given suspension settings that prioritize comfort. In this way, the driving experience can be improved by providing vehicle performance settings that match the driver's driving style.

[0059] The data collection unit can monitor the driver's behavior in real time and adjust the frequency of data collection based on specific behavioral patterns. For example, if the driver frequently changes lanes, the frequency of data collection for steering wheel operation can be increased. If the driver is driving for extended periods, the frequency of data collection for heart rate and skin electrical response can also be increased. Furthermore, if the driver frequently uses sudden braking, the frequency of data collection for brake pedal pressure can be increased. By adjusting the data collection frequency based on the driver's behavioral patterns, more accurate data collection becomes possible.

[0060] The generating unit can analyze the driver's driving history and automatically adjust the vehicle's maintenance schedule based on specific driving patterns. For example, drivers who frequently use sudden braking can have their brake pads replaced sooner. Drivers who frequently drive long distances can have their oil changes increased. Drivers who often drive on rough roads can have their suspension inspected more frequently. This optimizes the vehicle's maintenance schedule based on the driver's driving history, maintaining the vehicle's safety and performance.

[0061] The system can monitor the driver's behavior in real time and adjust the feedback delivery method based on specific behavioral patterns. For example, if the driver frequently changes lanes, visual feedback can be prioritized. If the driver is driving for extended periods, audio feedback can be prioritized. Furthermore, if the driver frequently uses sudden braking, vibration feedback can be prioritized. By adjusting the feedback delivery method based on the driver's behavioral patterns, more effective feedback can be provided.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data acquisition unit collects the driver's brain waves and biosignals. The data acquisition unit collects biometric data such as the driver's brain waves, heart rate, and skin electrical responses using, for example, a headset or sensors built into the vehicle. The data acquisition unit can collect brain waves using, for example, an EEG headset. The data acquisition unit can also collect heart rate using a heart rate sensor. Furthermore, the data acquisition unit can collect skin electrical responses using a skin electrical response sensor. Step 2: The analysis unit analyzes the data collected by the acquisition unit in real time. For example, the analysis unit can analyze the collected electroencephalogram (EEG) data to evaluate the driver's cognitive state. The analysis unit can also analyze the collected heart rate data to evaluate the driver's stress level. The analysis unit can also analyze the collected electrocutaneous response (ESD) data to evaluate the driver's emotional state. Step 3: The generation unit generates personalized feedback based on the analysis results obtained by the analysis unit. For example, if the driver's cognitive state is impaired, the generation unit generates feedback to improve concentration. For example, if the driver's stress level is high, the generation unit can also generate feedback to help them relax. For example, if the driver's emotional state is unstable, the generation unit can also generate feedback to help them stabilize. Step 4: The providing unit provides the driver with the feedback generated by the generating unit. The providing unit provides feedback using, for example, an audio alert. The providing unit can also provide feedback using, for example, a visual presentation. The providing unit can also provide feedback using, for example, vibration feedback.

[0064] (Example of form 2) The safety driving support system according to an embodiment of the present invention is a system that monitors the driver's brainwaves and biosignals in real time and provides more advanced safety driving support by combining a brain-computer interface (BCI) and generative AI. This safety driving support system collects biodata such as the driver's brainwaves, heart rate, and skin electrical responses, and analyzes it in real time to evaluate the driver's cognitive state, stress level, and emotional state. The generative AI learns the driver's past driving patterns and current state and provides personalized feedback. For example, if attention is low, it generates a warning message such as "Your concentration is low. Take a deep breath and refresh yourself," and if it determines that fatigue is accumulating, it suggests "We recommend taking a break at the next service area." In addition, the generative AI automatically adjusts the in-car environment according to the driver's state. For example, if the driver feels drowsy, it plays music with an awakening effect and brightens the lights, and if the driver feels stressed, it adjusts the temperature and airflow to have a relaxing effect. Furthermore, the generative AI also analyzes road conditions and the movements of other vehicles and predicts potential dangers. For example, if the risk of collision with another vehicle increases, it warns the driver and automatically applies the brakes if necessary. Furthermore, it proposes the optimal route in real time, taking into account traffic congestion and accident information. The generating AI learns the driver's reactions and preferences, evolving its feedback and suggestions to be more effective. For example, it can be applied to supporting elderly drivers, training new drivers, and managing commercial vehicle drivers. In the future, we envision integration with other vehicles and infrastructure, utilization of multimodal data such as eye tracking and voice recognition, and support for the transition to fully autonomous driving. As a result, the safety driving support system can support safe driving by analyzing the driver's brainwaves and biosignals in real time and providing personalized feedback.

[0065] The safety driving support system according to the embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects the driver's brain waves and biosignals. The data collection unit collects biosignals such as the driver's brain waves, heart rate, and skin electrical responses using, for example, a headset or sensors built into the vehicle. The data collection unit can collect brain waves using, for example, an EEG headset. The data collection unit can also collect heart rate using a heart rate sensor. Furthermore, the data collection unit can collect skin electrical responses using a skin electrical response sensor. The analysis unit analyzes the data collected by the data collection unit in real time. The analysis unit can, for example, analyze the collected brain wave data to evaluate the driver's cognitive state. The analysis unit can also, for example, analyze the collected heart rate data to evaluate the driver's stress level. The analysis unit can also, for example, analyze the collected skin electrical response data to evaluate the driver's emotional state. The data generation unit generates personalized feedback based on the analysis results obtained by the analysis unit. The generation unit generates feedback to enhance concentration when, for example, the driver's cognitive state is impaired. The generation unit can also generate feedback to help the driver relax when, for example, the driver's stress level is high. The generation unit can also generate feedback to help the driver stabilize when, for example, the driver's emotional state is unstable. The provisioning unit provides the feedback generated by the generation unit to the driver. The provisioning unit provides feedback using, for example, an audible alert. The provisioning unit can also provide feedback using, for example, a visual presentation. The provisioning unit can also provide feedback using, for example, vibration feedback. As a result, the safety driving support system according to the embodiment can support safe driving by analyzing the driver's brainwaves and biosignals in real time and providing personalized feedback.

[0066] The data collection unit collects the driver's brainwaves and biosignals. For example, it uses a headset or sensors integrated into the vehicle to collect biometric data such as the driver's brainwaves, heart rate, and skin electrical activity. Specifically, an EEG headset can be used to collect brainwaves. This headset is worn on the driver's head and monitors the brain's electrical activity in real time. The brainwave data is used to assess the driver's concentration and fatigue level. The data collection unit can also collect heart rate using a heart rate sensor. This sensor is attached to the driver's fingertips or wrists and measures heart rate variability in real time. The heart rate data is used to assess the driver's stress level and agitation. Furthermore, the data collection unit can collect skin electrical activity using a skin electrical activity sensor. This sensor is attached to the driver's skin and detects electrical changes in the skin. The skin electrical activity data is used to assess the driver's emotional state and tension level. These sensors can also be integrated into the vehicle's seats or steering wheel, allowing data to be collected while the driver is in a natural driving position. The collected data is transmitted wirelessly to a central database and analyzed in real time. This allows the data collection unit to efficiently and accurately collect the driver's biosignals, improving the overall system performance.

[0067] The analysis unit analyzes the data collected by the data collection unit in real time. For example, the analysis unit analyzes collected electroencephalogram (EEG) data to evaluate the driver's cognitive state. Specifically, it uses advanced signal processing algorithms with AI to evaluate the driver's concentration and fatigue level from the EEG data. For example, by analyzing the ratio of alpha waves to beta waves, the driver's level of concentration can be understood. It can also analyze collected heart rate data to evaluate the driver's stress level. By analyzing heart rate variability and changes in heart rate intervals, the driver's stress and relaxation levels can be evaluated. Furthermore, it can analyze collected electrocutaneous response data to evaluate the driver's emotional state. By analyzing changes in electrocutaneous responses, the driver's level of tension and emotional changes can be understood. The analysis unit integrates this data to perform a comprehensive evaluation and understand the driver's state in real time. In addition, the analysis unit can also predict changes in the driver's state by utilizing past data and statistical information. For example, based on past driving data, it can predict the driver's reaction under specific circumstances and issue a warning in advance. This allows the analysis unit to accurately evaluate the driver's condition and provide a foundation for offering appropriate feedback.

[0068] The generation unit generates personalized feedback based on the analysis results obtained by the analysis unit. For example, if the driver's cognitive state is impaired, the generation unit generates feedback to enhance concentration. Specifically, if it determines that the driver's concentration is impaired, the generation unit generates feedback to draw the driver's attention using voice alerts or visual presentations. It can also generate feedback to help the driver relax if their stress level is high. For example, it can generate feedback that plays relaxing music or provides guidance to encourage deep breathing. Furthermore, if the driver's emotional state is unstable, it can generate feedback to help stabilize it. For example, if it determines that the driver's emotional state is unstable, the generation unit generates feedback that displays relaxing images or messages. The generation unit appropriately generates this feedback according to the driver's state and sends it to the delivery unit. The generation unit can also utilize AI-based natural language processing technology to select appropriate words and expressions for the driver. This allows the generation unit to quickly and accurately generate personalized feedback according to the driver's state and send it to the delivery unit.

[0069] The providing unit provides feedback generated by the generating unit to the driver. The providing unit provides feedback using, for example, voice alerts. Specifically, it plays an audio message to alert the driver through the in-car speakers. Feedback can also be provided using visual presentations. For example, it displays an alert message or image on the in-car display. Furthermore, feedback can also be provided using vibration feedback. For example, it uses vibration devices incorporated into the seat or steering wheel to alert the driver through vibration. The providing unit combines these feedback means to provide the driver with optimal feedback. The providing unit can also adjust the content and intensity of the feedback according to the driver's condition and driving situation. For example, if the driver is driving on a highway, it may use strong voice alerts and vibration feedback to alert the driver, while if the driver is driving in an urban area, it may use gentle voice messages and visual presentations to alert the driver. This allows the providing unit to provide appropriate feedback to the driver and support safe driving. Furthermore, the providing unit can monitor the driver's response to the feedback and evaluate the effectiveness of the feedback. This allows the providing unit to continuously improve the content and method of feedback and improve the overall performance of the system.

[0070] The data collection unit collects biometric data such as the driver's brainwaves, heart rate, and skin electrical responses using a headset or sensors embedded in the vehicle. For example, the data collection unit can collect the driver's brainwaves using a headset. For example, the data collection unit can collect the driver's heart rate using a heart rate sensor embedded in the vehicle. For example, the data collection unit can collect the driver's skin electrical responses using a skin electrical response sensor embedded in the vehicle. This allows for an accurate understanding of the driver's condition by collecting biometric data using a headset or sensors embedded in the vehicle. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input brainwave data acquired by the headset into a generating AI and have the generating AI perform an analysis of the brainwave data.

[0071] The analysis unit analyzes the collected data in real time to evaluate the driver's cognitive state, stress level, and emotional state. For example, the analysis unit can analyze collected electroencephalogram (EEG) data in real time to evaluate the driver's cognitive state. For example, the analysis unit can analyze collected heart rate data in real time to evaluate the driver's stress level. For example, the analysis unit can analyze collected electrocutaneous response (ESD) data in real time to evaluate the driver's emotional state. This allows for immediate evaluation of the driver's condition by analyzing the collected data in real time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected EEG data into a generating AI and have the generating AI perform an evaluation of the driver's cognitive state.

[0072] The generation unit learns the driver's past driving patterns and current state, and generates personalized feedback. For example, the generation unit can learn the driver's past driving patterns and generate feedback based on the current state. For example, the generation unit can learn the driver's current state and generate feedback based on past driving patterns. For example, the generation unit can learn the driver's past driving patterns and current state and generate personalized feedback. This allows for the provision of personalized feedback by learning the driver's past driving patterns and current state. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the driver's past driving patterns and current state into a generation AI and have the generation AI perform the generation of personalized feedback.

[0073] The service provider provides the generated feedback to the driver. The service provider can, for example, provide the generated feedback to the driver as an audio alert. The service provider can, for example, provide the generated feedback to the driver as a visual presentation. The service provider can, for example, provide the generated feedback to the driver as vibration feedback. By providing the generated feedback to the driver, appropriate support can be provided according to the driver's state. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated feedback into a generating AI and have the generating AI execute a method for providing it to the driver.

[0074] The generation unit automatically adjusts the in-car environment according to the driver's condition. For example, if the driver is feeling drowsy, the generation unit can play music with an awakening effect. For example, if the driver is feeling stressed, the generation unit can adjust the temperature and airflow to have a relaxing effect. For example, the generation unit can adjust the lighting according to the driver's condition. In this way, the driver's comfort and safety are improved by automatically adjusting the in-car environment according to the driver's condition. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the driver's condition to the generation AI and have the generation AI perform the adjustment of the in-car environment.

[0075] The generation unit analyzes road conditions and the movements of other vehicles to predict potential hazards. For example, the generation unit can analyze road conditions and predict potential hazards. For example, the generation unit can analyze the movements of other vehicles and predict potential hazards. For example, the generation unit can analyze road conditions and the movements of other vehicles and predict potential hazards. This allows the generation unit to predict potential hazards and provide warnings to drivers by analyzing road conditions and the movements of other vehicles. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input road conditions and the movements of other vehicles into a generation AI and have the generation AI perform the prediction of potential hazards.

[0076] The generation unit proposes the optimal route in real time, taking into account traffic congestion and accident information. For example, the generation unit can propose the optimal route based on traffic congestion information. For example, the generation unit can propose the optimal route based on accident information. For example, the generation unit can propose the optimal route based on both traffic congestion and accident information. This improves the driver's travel efficiency by proposing the optimal route based on traffic congestion and accident information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input traffic congestion information and accident information into a generation AI and have the generation AI propose the optimal route.

[0077] The data collection unit estimates the driver's emotions and adjusts the frequency of biometric data collection based on the estimated emotions. For example, the data collection unit can increase the frequency of heart rate and skin electrical responses when the driver is stressed. For example, the data collection unit can decrease the frequency of electroencephalogram (EEG) collection when the driver is relaxed. For example, the data collection unit can increase the frequency of heart rate variability collection when the driver is tired. By adjusting the frequency of biometric data collection based on the driver's emotions, more accurate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the driver's emotion data into a generative AI and have the generative AI adjust the frequency of biometric data collection.

[0078] The data collection unit analyzes the driver's past biometric data and selects the optimal sensor placement. For example, the data collection unit can place sensors in areas where heart rate fluctuations are large based on past data. For example, the data collection unit can place sensors in areas where brain wave fluctuations are small based on past data. For example, the data collection unit can place sensors in areas where skin electrical responses are sensitive based on past data. In this way, the optimal sensor placement can be selected by analyzing the driver's past biometric data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's past biometric data into a generating AI and have the generating AI select the optimal sensor placement.

[0079] The data collection unit filters the collected biometric data based on the driver's current driving conditions and environment. For example, the data collection unit can prioritize the collection of heart rate and electroencephalogram (EEG) data when driving on a highway. For example, the data collection unit can prioritize the collection of skin electrical response and heart rate variability data when driving in urban areas. For example, the data collection unit can prioritize the collection of EEG and heart rate data when driving at night. This allows for the priority collection of necessary data by filtering based on the driver's current driving conditions and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's current driving conditions and environmental data into a generating AI and have the generating AI perform the filtering.

[0080] The data collection unit estimates the driver's emotions and determines the priority of biometric data to collect based on the estimated emotions. For example, the data collection unit can prioritize the collection of heart rate and skin electrochemistry data when the driver is stressed. For example, the data collection unit can prioritize the collection of electroencephalogram (EEG) data when the driver is relaxed. For example, the data collection unit can prioritize the collection of heart rate variability data when the driver is tired. This allows for the priority collection of important data by determining the priority of biometric data to collect based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the driver's emotion data into a generative AI and have the generative AI determine the priority of biometric data.

[0081] The data collection unit prioritizes the collection of highly relevant data, taking into account the driver's geographical location information, when collecting biometric data. For example, the data collection unit can prioritize the collection of heart rate and electroencephalogram (EEG) data when driving on a highway. For example, the data collection unit can prioritize the collection of skin electrical response and heart rate variability data when driving in urban areas. For example, the data collection unit can prioritize the collection of EEG and heart rate data when driving in mountainous areas. This allows for the efficient collection of necessary data by prioritizing the collection of highly relevant data, taking into account the driver's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0082] The data collection unit analyzes the driver's social media activity when collecting biometric data and collects relevant data. For example, the data collection unit can prioritize collecting heart rate and skin electrophysiological response data when the driver is stressed. For example, the data collection unit can prioritize collecting electroencephalogram (EEG) data when the driver is relaxed. For example, the data collection unit can prioritize collecting heart rate variability data when the driver is tired. This allows for the efficient collection of relevant data by analyzing the driver's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0083] The analysis unit estimates the driver's emotions and adjusts the analysis algorithm based on the estimated emotions. For example, the analysis unit may prioritize heart rate and skin electrophysiological response data when the driver is tense. For example, the analysis unit may prioritize electroencephalogram (EEG) data when the driver is relaxed. For example, the analysis unit may prioritize heart rate variability data when the driver is tired. This improves the accuracy of the analysis by adjusting the analysis algorithm based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit may input the driver's emotion data into the generative AI and have the generative AI adjust the analysis algorithm.

[0084] The analysis unit improves analysis accuracy by referring to past analysis data during analysis. For example, the analysis unit can place sensors in areas where heart rate fluctuations are large based on past data. For example, the analysis unit can place sensors in areas where brain wave fluctuations are small based on past data. For example, the analysis unit can place sensors in areas where skin electrical responses are sensitive based on past data. In this way, analysis accuracy is improved by referring to past analysis data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0085] The analysis unit performs the analysis while taking the driver's attribute information into consideration. For example, in the case of an elderly driver, the analysis unit can prioritize heart rate and skin electrical response data. For example, in the case of a novice driver, the analysis unit can prioritize electroencephalogram (EEG) data. For example, in the case of a commercial vehicle driver, the analysis unit can prioritize heart rate variability data. By considering the driver's attribute information during the analysis, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the driver's attribute information into a generating AI and have the generating AI perform the analysis.

[0086] The analysis unit estimates the driver's emotions and adjusts the display method of the analysis results based on the estimated emotions of the driver. For example, the analysis unit can provide a simple and highly visible display method when the driver is tense. For example, the analysis unit can provide a display method that includes detailed information when the driver is relaxed. For example, the analysis unit can provide a display method that gets to the point when the driver is in a hurry. By adjusting the display method of the analysis results based on the driver's emotions, it becomes possible to provide a display that is easy for the driver to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the driver's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0087] The analysis unit performs its analysis while considering the geographical distribution of drivers. For example, when driving on a highway, the analysis unit can prioritize heart rate and electroencephalogram (EEG) data. For example, when driving in an urban area, the analysis unit can prioritize skin electrical response and heart rate variability data. For example, when driving in a mountainous area, the analysis unit can prioritize EEG and heart rate data. By considering the geographical distribution of drivers, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of drivers into a generating AI and have the generating AI perform the analysis.

[0088] The analysis unit improves the accuracy of the analysis by referring to relevant literature during the analysis. For example, the analysis unit can refer to the latest research on heart rate variability. For example, the analysis unit can refer to the latest research on electroencephalogram (EEG) variability. For example, the analysis unit can refer to the latest research on skin electroreactivity. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0089] The generation unit estimates the driver's emotions and adjusts the content of the feedback based on the estimated emotions of the driver. For example, the generation unit can generate feedback that encourages the driver to take deep breaths to relax if the driver is tense. For example, the generation unit can generate feedback that draws the driver's attention if the driver is relaxed. For example, the generation unit can generate feedback that encourages the driver to take a break if the driver is tired. This allows for more appropriate feedback to be provided by adjusting the content of the feedback based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input the driver's emotion data into a generative AI and have the generative AI adjust the content of the feedback.

[0090] The generation unit generates optimal feedback by referring to the driver's past driving patterns when generating feedback. For example, the generation unit can place sensors in areas where heart rate fluctuations are large based on past data. For example, the generation unit can place sensors in areas where brain wave fluctuations are small based on past data. For example, the generation unit can place sensors in areas where skin electrical responses are sensitive based on past data. This allows the generation unit to generate optimal feedback by referring to the driver's past driving patterns. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the driver's past driving pattern data into a generation AI and have the generation AI perform the generation of optimal feedback.

[0091] The generation unit customizes the means of feedback based on the driver's current driving conditions when generating feedback. For example, the generation unit can prioritize voice feedback when driving on a highway. For example, the generation unit can prioritize visual feedback when driving in urban areas. For example, the generation unit can prioritize vibration feedback when driving at night. This allows for more effective feedback by customizing the means of feedback based on the driver's current driving conditions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the driver's current driving condition data into the generation AI and have the generation AI customize the means of feedback.

[0092] The generation unit estimates the driver's emotions and determines the priority of feedback based on the estimated emotions. For example, if the driver is tense, the generation unit may prioritize feedback that encourages deep breathing to relax. For example, if the driver is relaxed, the generation unit may prioritize feedback that draws attention. For example, if the driver is tired, the generation unit may prioritize feedback that encourages rest. This ensures that important feedback is provided preferentially by determining the priority of feedback based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit may input driver emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0093] The generation unit generates optimal feedback by considering the driver's geographical location information when generating feedback. For example, the generation unit can prioritize voice feedback when driving on a highway. For example, the generation unit can prioritize visual feedback when driving in urban areas. For example, the generation unit can prioritize vibration feedback when driving in mountainous areas. By generating optimal feedback while considering the driver's geographical location information, more effective feedback can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the driver's geographical location information into the generation AI and have the generation AI perform the generation of optimal feedback.

[0094] The generation unit analyzes the driver's social media activity when generating feedback and proposes a means of providing feedback. For example, the generation unit can generate feedback that encourages the driver to take deep breaths to relax if the driver is feeling stressed. For example, the generation unit can generate feedback that draws the driver's attention if the driver is relaxed. For example, the generation unit can generate feedback that encourages the driver to take a break if the driver is tired. By analyzing the driver's social media activity, the optimal means of providing feedback can be proposed. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the driver's social media activity data into a generation AI and have the generation AI propose a means of providing feedback.

[0095] The service provider estimates the driver's emotions and adjusts the method of providing feedback based on the estimated emotions. For example, the service provider can provide feedback in a calm voice if the driver is tense. For example, the service provider can provide feedback in a cheerful voice if the driver is relaxed. For example, the service provider can provide quick and concise feedback if the driver is in a hurry. This allows for more effective feedback by adjusting the method of providing feedback based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input driver emotion data into a generative AI and have the generative AI adjust the method of providing feedback.

[0096] The feedback unit selects the optimal feedback method by referring to the driver's past response history when providing feedback. For example, the feedback unit can prioritize voice feedback if past data shows that voice feedback was effective. For example, the feedback unit can prioritize visual feedback if past data shows that visual feedback was effective. For example, the feedback unit can prioritize vibration feedback if past data shows that vibration feedback was effective. This allows the optimal feedback method to be selected by referring to the driver's past response history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the driver's past response history data into a generating AI and have the generating AI select the optimal feedback method.

[0097] The service provider estimates the driver's emotions and adjusts the timing of feedback delivery based on the estimated emotions. For example, the service provider can provide immediate feedback if the driver is tense. For example, the service provider can provide feedback at an appropriate time if the driver is relaxed. For example, the service provider can provide rapid feedback if the driver is in a hurry. By adjusting the timing of feedback delivery based on the driver's emotions, feedback can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input driver emotion data into a generative AI and have the generative AI adjust the timing of feedback delivery.

[0098] The feedback unit selects the optimal feedback method when providing feedback, taking into account the driver's device information. For example, if the driver is using a smartphone, the feedback unit can provide visual feedback that matches the screen size. For example, if the driver is using a tablet, the feedback unit can provide visual feedback optimized for a larger screen. For example, if the driver is using a smartwatch, the feedback unit can provide concise and highly visible vibration feedback. By selecting the optimal feedback method considering the driver's device information, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the driver's device information into a generating AI and have the generating AI select the optimal feedback method.

[0099] The feedback unit selects the optimal feedback method when providing feedback, taking into account the driver's current driving conditions. For example, the feedback unit may prioritize voice feedback when driving on a highway. For example, the feedback unit may prioritize visual feedback when driving in urban areas. For example, the feedback unit may prioritize vibration feedback when driving at night. By selecting the optimal feedback method considering the driver's current driving conditions, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit may input the driver's current driving condition data into a generating AI and have the generating AI select the optimal feedback method.

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

[0101] The analysis unit can estimate the driver's emotions and adjust the tone and content of the voice assistant during driving based on the estimated emotions. For example, if the driver is tense, it can provide advice in a calm tone to encourage relaxation. If the driver is relaxed, it can provide driving-related information in a bright tone. If the driver is tired, it can provide advice in a gentle tone to encourage rest. This enables appropriate voice assistant feedback that is tailored to the driver's emotions.

[0102] The data collection unit can analyze the driver's driving history and optimize sensor placement based on specific driving patterns. For example, a pressure sensor can be placed near the brake pedal for drivers who frequently use sudden braking based on past data. For drivers who frequently use highways, the heart rate sensor mounted on the steering wheel can be enhanced. Furthermore, for drivers who frequently drive at night, sensors can be placed around the eyes to detect visual fatigue. This optimizes sensor placement based on the driver's driving history, enabling more accurate data collection.

[0103] The analysis unit can estimate the driver's emotions and adjust the music selection during driving based on those estimated emotions. For example, if the driver is tense, it can play music with a relaxing effect. If the driver is relaxed, it can play music to enhance concentration. If the driver is tired, it can play music with an alerting effect. By providing appropriate music according to the driver's emotions, it can improve driving comfort and safety.

[0104] The generator can automatically adjust the vehicle's performance settings based on the driver's driving style. For example, a driver who prefers sporty driving can be given settings that improve engine response. A driver who prioritizes eco-driving can be given settings that optimize fuel efficiency. A driver who frequently drives long distances can be given suspension settings that prioritize comfort. In this way, the driving experience can be improved by providing vehicle performance settings that match the driver's driving style.

[0105] The feedback system can estimate the driver's emotions and adjust the feedback delivery method based on those estimates. For example, if the driver is tense, feedback can be delivered in a calm voice. If the driver is relaxed, feedback can be delivered in a cheerful voice. If the driver is in a hurry, quick and concise feedback can be delivered. By adjusting the feedback delivery method based on the driver's emotions, more effective feedback can be provided.

[0106] The data collection unit can monitor the driver's behavior in real time and adjust the frequency of data collection based on specific behavioral patterns. For example, if the driver frequently changes lanes, the frequency of data collection for steering wheel operation can be increased. If the driver is driving for extended periods, the frequency of data collection for heart rate and skin electrical response can also be increased. Furthermore, if the driver frequently uses sudden braking, the frequency of data collection for brake pedal pressure can be increased. By adjusting the data collection frequency based on the driver's behavioral patterns, more accurate data collection becomes possible.

[0107] The analysis unit can estimate the driver's emotions and adjust the navigation instructions during driving based on those estimated emotions. For example, if the driver is tense, it can provide simple and clear navigation instructions. If the driver is relaxed, it can provide detailed navigation instructions. If the driver is in a hurry, it can provide navigation instructions that prioritize the shortest route. In this way, by providing appropriate navigation instructions according to the driver's emotions, driving comfort and safety can be improved.

[0108] The generating unit can analyze the driver's driving history and automatically adjust the vehicle's maintenance schedule based on specific driving patterns. For example, drivers who frequently use sudden braking can have their brake pads replaced sooner. Drivers who frequently drive long distances can have their oil changes increased. Drivers who often drive on rough roads can have their suspension inspected more frequently. This optimizes the vehicle's maintenance schedule based on the driver's driving history, maintaining the vehicle's safety and performance.

[0109] The feedback system can estimate the driver's emotions and adjust the timing of feedback delivery based on those emotions. For example, if the driver is tense, feedback can be provided immediately. If the driver is relaxed, feedback can be provided at an appropriate time. Furthermore, if the driver is in a hurry, feedback can be provided quickly. This allows for more timely feedback delivery by adjusting the timing based on the driver's emotions.

[0110] The system can monitor the driver's behavior in real time and adjust the feedback delivery method based on specific behavioral patterns. For example, if the driver frequently changes lanes, visual feedback can be prioritized. If the driver is driving for extended periods, audio feedback can be prioritized. Furthermore, if the driver frequently uses sudden braking, vibration feedback can be prioritized. By adjusting the feedback delivery method based on the driver's behavioral patterns, more effective feedback can be provided.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data acquisition unit collects the driver's brain waves and biosignals. The data acquisition unit collects biometric data such as the driver's brain waves, heart rate, and skin electrical responses using, for example, a headset or sensors built into the vehicle. The data acquisition unit can collect brain waves using, for example, an EEG headset. The data acquisition unit can also collect heart rate using a heart rate sensor. Furthermore, the data acquisition unit can collect skin electrical responses using a skin electrical response sensor. Step 2: The analysis unit analyzes the data collected by the acquisition unit in real time. For example, the analysis unit can analyze the collected electroencephalogram (EEG) data to evaluate the driver's cognitive state. The analysis unit can also analyze the collected heart rate data to evaluate the driver's stress level. The analysis unit can also analyze the collected electrocutaneous response (ESD) data to evaluate the driver's emotional state. Step 3: The generation unit generates personalized feedback based on the analysis results obtained by the analysis unit. For example, if the driver's cognitive state is impaired, the generation unit generates feedback to improve concentration. For example, if the driver's stress level is high, the generation unit can also generate feedback to help them relax. For example, if the driver's emotional state is unstable, the generation unit can also generate feedback to help them stabilize. Step 4: The providing unit provides the driver with the feedback generated by the generating unit. The providing unit provides feedback using, for example, an audio alert. The providing unit can also provide feedback using, for example, a visual presentation. The providing unit can also provide feedback using, for example, vibration feedback.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect the driver's brain waves and biosignals using the sensors and headset of the smart device 14. The analysis unit analyzes the collected data in real time by, for example, the specific processing unit 290 of the data processing unit 12 to evaluate the driver's cognitive state and stress level. The generation unit generates personalized feedback based on the analysis results by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the driver with the feedback generated by, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0130] 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.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect the driver's brain waves and biosignals using the sensors of the smart glasses 214 and a headset. The analysis unit, for example, analyzes the data collected by the specific processing unit 290 of the data processing unit 12 in real time to evaluate the driver's cognitive state and stress level. The generation unit, for example, generates personalized feedback based on the analysis results by the specific processing unit 290 of the data processing unit 12. The provision unit, for example, provides the driver with the feedback generated by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect the driver's brain waves and biosignals using the sensors and headset of the headset terminal 314. The analysis unit analyzes the collected data in real time by, for example, the specific processing unit 290 of the data processing unit 12 to evaluate the driver's cognitive state and stress level. The generation unit generates personalized feedback based on the analysis results by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the driver with the feedback generated by, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0163] 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.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect the driver's brain waves and biosignals using the robot 414's sensors and headset. The analysis unit analyzes the collected data in real time by the specific processing unit 290 of the data processing unit 12 to evaluate the driver's cognitive state and stress level. The generation unit generates personalized feedback based on the analysis results by the specific processing unit 290 of the data processing unit 12. The provision unit provides the driver with the feedback generated by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection unit that collects the driver's brainwaves and biosignals, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A generation unit that generates individualized feedback based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides feedback generated by the generation unit to the driver. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects biometric data such as the driver's brainwaves, heart rate, and skin electrical responses using headsets and sensors integrated into the vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed in real time to evaluate the driver's cognitive state, stress level, and emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It learns the driver's past driving patterns and current state, and generates personalized feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated feedback to the driver. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The system automatically adjusts the in-car environment according to the driver's condition. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is Analyze road conditions and the movements of other vehicles to predict potential hazards. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Based on traffic congestion and accident information, the system suggests the optimal route in real time. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the driver's emotions and adjusts the frequency of biometric data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the driver's past biometric data to select the optimal sensor placement. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting biometric data, filtering is performed based on the driver's current driving status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The system estimates the driver's emotions and prioritizes the biometric data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting biometric data, the system prioritizes the collection of highly relevant data, taking into account the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting biometric data, analyze the driver's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the driver's emotions and adjusts the analysis algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, past analysis data is referenced to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the driver's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the driver's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the geographical distribution of drivers will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is The system estimates the driver's emotions and adjusts the feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating feedback, the system references the driver's past driving patterns to generate optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating feedback, the feedback method is customized based on the driver's current driving situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the driver's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating feedback, the system takes into account the driver's geographical location to generate optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating feedback, we analyze the driver's social media activity and suggest methods for providing feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the driver's emotions and adjusts how feedback is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing feedback, the system selects the optimal method of delivery by referring to the driver's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the driver's emotions and adjusts the timing of feedback delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing feedback, the optimal method of delivery will be selected, taking into account the driver's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing feedback, the optimal method of delivery is selected, taking into account the driver's current driving situation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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 collection unit that collects the driver's brainwaves and biosignals, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A generation unit that generates individualized feedback based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides feedback generated by the generation unit to the driver. A system characterized by the following features.

2. The aforementioned collection unit is The system collects biometric data such as the driver's brainwaves, heart rate, and skin electrical responses using headsets and sensors integrated into the vehicle. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed in real time to evaluate the driver's cognitive state, stress level, and emotional state. The system according to feature 1.

4. The generating unit is It learns the driver's past driving patterns and current state, and generates personalized feedback. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated feedback to the driver. The system according to feature 1.

6. The generating unit is The system automatically adjusts the in-car environment according to the driver's condition. The system according to feature 1.

7. The generating unit is Analyze road conditions and the movements of other vehicles to predict potential hazards. The system according to feature 1.

8. The generating unit is Based on traffic congestion and accident information, the system suggests the optimal route in real time. The system according to feature 1.

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

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