system

Abstract

The system according to this embodiment aims to enable the driver to safely and efficiently operate various functions of the vehicle using voice commands. [Solution] The system according to the embodiment comprises a voice recognition unit, a control unit, a monitoring unit, a response unit, and an emergency call unit. The voice recognition unit recognizes the driver's voice commands. The control unit controls the functions of the vehicle based on the voice commands recognized by the voice recognition unit. The monitoring unit monitors the driver's condition and the external environment. The response unit provides an automatic response based on the information monitored by the monitoring unit. The emergency call unit provides an automatic notification and requests for support in the event of an emergency.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when a driver operates each function of a vehicle, it is necessary to do so manually, which may reduce the safety during driving.

[0005] The system according to the embodiment aims to enable a driver to safely and efficiently operate each function of a vehicle using voice commands.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a voice recognition unit, a control unit, a monitoring unit, a response unit, and an emergency call unit. The voice recognition unit recognizes the driver's voice commands. The control unit controls the vehicle's functions based on the voice commands recognized by the voice recognition unit. The monitoring unit monitors the driver's condition and the external environment. The response unit provides an automatic response based on the information monitored by the monitoring unit. The emergency call unit makes an automatic notification and requests support in the event of an emergency. [Effects of the Invention]

[0007] The system according to this embodiment allows the driver to safely and efficiently operate various functions of the vehicle using voice commands. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 AI ​​assistant for assisting car operation via voice commands according to an embodiment of the present invention is a system that recognizes the driver's voice commands and controls various functions of the car (navigation, air conditioning, music, etc.). This system recognizes the driver's voice commands and controls the functions of the car. It also monitors the driver's condition and the external environment and makes automatic responses based on that information. Furthermore, in the event of an emergency, it makes automatic calls and requests for support. For example, it recognizes the driver's voice commands. For example, it recognizes voice commands such as "Turn on the air conditioning" or "Start navigation." These voice commands are analyzed by the AI. Next, it controls the functions of the car based on the recognized voice commands. For example, it changes the settings of the air conditioning or starts navigation. This control is performed by the AI. Furthermore, it monitors the driver's condition and the external environment. For example, it monitors the driver's heart rate and stress level, and monitors the weather and road conditions outside. This monitoring is performed by the AI. It makes automatic responses based on the monitored information. For example, if the driver's heart rate is elevated, it plays relaxing music or automatically adjusts the air conditioning settings to maintain the temperature inside the car at a specific temperature. This automatic response is performed by the AI. Finally, in the event of an emergency, it makes automatic calls and requests for support. For example, in the event of an accident, the system automatically makes an emergency call and requests assistance. This emergency call and assistance request are handled by AI. This system allows the driver to easily control various functions of the car using voice commands, improving safety and comfort while driving. Furthermore, it enables automatic responses and emergency actions that are tailored to the driver's condition and external environment, allowing for safer driving. In this way, the AI ​​assistant that assists with car operation via voice commands can recognize the driver's voice commands and control various functions of the car, thereby improving safety and comfort while driving.

[0029] The AI ​​assistant for assisting car operation via voice commands according to this embodiment comprises a voice recognition unit, a control unit, a monitoring unit, a response unit, and an emergency call unit. The voice recognition unit recognizes the driver's voice commands. The driver's voice commands include, but are not limited to, commands such as "Turn on the air conditioner" or "Start navigation." The voice recognition unit analyzes and recognizes the voice commands using AI, for example. The control unit controls the car's functions based on the voice commands recognized by the voice recognition unit. The control unit, for example, changes the air conditioner settings or starts navigation. The control unit performs control using AI. The monitoring unit monitors the driver's condition and the external environment. The monitoring unit, for example, monitors the driver's heart rate and stress level, and monitors external weather and road conditions. The monitoring unit performs monitoring using AI. The response unit provides an automatic response based on the information monitored by the monitoring unit. For example, if the driver's heart rate is elevated, the response unit may play relaxing music or automatically adjust the air conditioner settings to maintain the interior temperature at a specific temperature. The response unit automatically responds using AI. The emergency call unit automatically makes an emergency call and requests support in the event of an emergency. For example, if an accident occurs, the emergency call unit automatically makes an emergency call and requests support. The emergency call unit makes emergency calls and requests support using AI. As a result, the AI ​​assistant that assists in operating the car using voice commands according to this embodiment can improve safety and comfort while driving by recognizing the driver's voice commands and controlling various functions of the car.

[0030] The voice recognition unit recognizes the driver's voice commands. These commands include, but are not limited to, commands such as "Turn on the air conditioner" or "Start navigation." The voice recognition unit analyzes and recognizes voice commands using AI. Specifically, the voice recognition unit collects the driver's voice using a high-sensitivity microphone and removes in-car noise using noise cancellation technology. Next, the collected voice data is sent to the voice recognition AI, where the voice commands are analyzed. This AI analyzes the voice data using deep learning technology and recognizes the driver's intentions with high accuracy. For example, the voice recognition AI converts the voice data into text data and analyzes that text data using natural language processing (NLP) technology. This allows it to understand the meaning of the voice command and identify the appropriate action. Furthermore, the voice recognition unit can learn the characteristics of the driver's voice and perform voice recognition optimized for each individual driver. This improves the accuracy of voice recognition and reduces the risk of misrecognition. The voice recognition unit also supports multiple languages ​​and dialects, making it possible to accommodate drivers who speak different languages. This allows the voice recognition unit to recognize the driver's voice commands with high accuracy, enabling smooth operation of the vehicle.

[0031] The control unit controls the car's functions based on voice commands recognized by the voice recognition unit. For example, the control unit can change the air conditioning settings or start the navigation system. The control unit is controlled by AI. Specifically, the control unit receives commands from the voice recognition unit and executes the corresponding car functions. For example, if the command "Turn on the air conditioner" is recognized, the control unit turns on the air conditioner and adjusts the temperature and fan speed. If the command "Start navigation" is recognized, the control unit starts the navigation system and begins setting the destination and providing route guidance. The control unit uses AI to understand the driver's intentions and perform optimal control. For example, even if the driver gives a vague command such as "Make it a little cooler," the AI ​​can consider the driver's past settings and the current in-car environment to make appropriate temperature adjustments. Furthermore, the control unit utilizes data from various car sensors to perform real-time, situation-appropriate control. For example, it can optimize the air conditioning settings by considering the outside temperature and humidity, the number of occupants in the car, etc. As a result, the control unit can accurately control the car's functions based on the driver's voice commands and provide a comfortable driving environment.

[0032] The monitoring unit monitors the driver's condition and the external environment. For example, it monitors the driver's heart rate and stress level, as well as external weather and road conditions. The monitoring unit uses AI for monitoring. Specifically, the monitoring unit uses heart rate sensors and stress level sensors to understand the driver's condition. These sensors are attached to the driver's wrist or chest and collect data in real time. The collected data is analyzed by AI to determine whether the driver's heart rate and stress level are within the normal range. The monitoring unit also monitors the external environment using cameras and sensors mounted on the vehicle. For example, cameras photograph road conditions and weather, and sensors measure road surface slipperiness and temperature. This data is analyzed by AI to determine whether warnings or assistance to the driver are necessary. Furthermore, the monitoring unit can learn the driver's driving patterns and behaviors and detect abnormal driving behaviors. For example, if sudden braking or sudden steering maneuvers occur frequently, it can detect driver fatigue or decreased attention and issue appropriate alerts. This allows the monitoring unit to monitor the driver's condition and the external environment in real time, supporting safe driving.

[0033] The response unit provides automatic responses based on information monitored by the monitoring unit. For example, if the driver's heart rate is elevated, the response unit may play relaxing music or automatically adjust the air conditioning settings to maintain a specific temperature inside the vehicle. The response unit uses AI to provide automatic responses. Specifically, the response unit receives data sent from the monitoring unit and provides an appropriate response based on that data. For example, if the driver's heart rate is elevated, the response unit selects relaxing music and plays it through the vehicle's audio system. It also automatically adjusts the air conditioning settings to maintain a comfortable temperature inside the vehicle. Furthermore, if the driver's stress level is high, the response unit can provide voice guidance encouraging deep breathing or suggest that the driver take a break. The response unit uses AI to analyze the driver's condition and provide the optimal response. For example, it can provide individually customized responses considering the driver's past data and current situation. This allows the response unit to provide appropriate responses according to the driver's condition, reduce stress while driving, and provide a comfortable driving environment.

[0034] The emergency call unit automatically makes emergency calls and requests for support in the event of an emergency. For example, in the event of an accident, the emergency call unit automatically makes an emergency call and requests support. The emergency call unit uses AI to make emergency calls and requests for support. Specifically, the emergency call unit uses sensors and cameras mounted on the vehicle to detect the occurrence of an accident. For example, if a collision sensor detects a strong impact or a camera confirms damage to the vehicle, the emergency call unit immediately makes an emergency call. The emergency call unit uses GPS data to determine the current location of the vehicle and transmits it to the emergency call center. It can also monitor the driver's condition and request medical support as needed. For example, if the driver is unconscious or has an abnormally low heart rate, the emergency call unit will make an emergency call to a medical facility to encourage a rapid response. Furthermore, the emergency call unit can record the details of the accident and the condition of the vehicle to help with subsequent responses. This allows the emergency call unit to make quick and appropriate calls and requests for support in emergencies, ensuring the safety of the driver.

[0035] The control unit can control various functions such as navigation, air conditioning, audio, lighting, and driving functions. For example, the control unit can set destinations and provide route guidance for the navigation system. It can also set the temperature and adjust the airflow of the air conditioner. Furthermore, it can adjust the volume and select songs for the audio system. For example, the control unit sets a destination for the navigation system and starts route guidance. The control unit sets the air conditioner temperature to 22 degrees and adjusts the airflow. The control unit adjusts the audio volume to an appropriate level and selects songs. In this way, the control unit's control over various functions of the car improves driver convenience.

[0036] The control unit can change the color of the lighting to match the tone of the driver's voice commands. For example, if the driver's voice commands are in a relaxed tone, the control unit will change the lighting color to blue. The control unit can also change the lighting color to red if the driver's voice commands are in a tense tone. Furthermore, the control unit can change the lighting color to white if the driver's voice commands are in a neutral tone. In this way, by changing the lighting color according to the tone of the driver's voice commands, an environment that matches the driver's emotions can be provided.

[0037] The monitoring unit detects the intensity of sunlight, and the response unit automatically adjusts the air conditioner settings to maintain the interior temperature at 22 degrees Celsius. For example, the monitoring unit uses a light sensor to detect the intensity of sunlight. The response unit automatically adjusts the air conditioner settings according to the intensity of sunlight to maintain the interior temperature at 22 degrees Celsius. For example, the monitoring unit uses a light sensor to detect the intensity of sunlight. If the sunlight is strong, the response unit adjusts the air conditioner setting to a lower temperature to maintain the interior temperature at 22 degrees Celsius. If the sunlight is weak, the response unit adjusts the air conditioner setting to a higher temperature to maintain the interior temperature at 22 degrees Celsius. This automatically adjusts the air conditioner settings according to the intensity of sunlight, improving comfort inside the vehicle.

[0038] The monitoring unit monitors the driver's heart rate when entering a gravel or unpaved road, and the response unit can change the vehicle's driving mode if the driver's heart rate rises to 100 bpm or higher. The monitoring unit monitors the driver's heart rate using, for example, a heart rate sensor. The response unit changes the vehicle's driving mode if the driver's heart rate rises to 100 bpm or higher. For example, the monitoring unit monitors the driver's heart rate using a heart rate sensor. If the driver's heart rate rises to 100 bpm or higher, the response unit changes the vehicle's driving mode from eco mode to sport mode. The response unit can also change the vehicle's driving mode from sport mode to eco mode if the driver's heart rate rises to 100 bpm or higher. This improves driver safety by changing the vehicle's driving mode according to the driver's heart rate.

[0039] The monitoring unit monitors the driver's health condition when the road is frozen, and the response unit can prompt the driver to take a break and guide them to a safe location if their blood pressure is 140 / 90 mmHg or higher. The monitoring unit, for example, uses a temperature sensor to detect whether the road is frozen. The response unit prompts the driver to take a break and guides them to a safe location if their blood pressure is 140 / 90 mmHg or higher. For example, the monitoring unit uses a temperature sensor to detect whether the road is frozen. If the driver's blood pressure is 140 / 90 mmHg or higher, the response unit prompts the driver to take a break using an audio alert and guides them to a safe location. The response unit can also prompt the driver to take a break and guide them to a safe location using a display if their blood pressure is 140 / 90 mmHg or higher. This improves driver safety by prompting breaks and guiding drivers to safe locations according to their health condition.

[0040] The monitoring unit monitors the driver's stress level when surrounding vehicles brake suddenly, and the response unit can play relaxing music when the driver's stress level rises to 50 or higher. For example, the monitoring unit uses a radar sensor to detect whether surrounding vehicles have braked suddenly. If the driver's stress level rises to 50 or higher, the response unit plays relaxing music. For example, the monitoring unit uses a radar sensor to detect whether surrounding vehicles have braked suddenly. If the driver's stress level rises to 50 or higher, the response unit plays classical music. The response unit can also play nature sounds if the driver's stress level rises to 50 or higher. This reduces driver stress by playing relaxing music according to the driver's stress level.

[0041] The monitoring unit monitors the driver's stress level when changing lanes on a highway, and the response unit can temporarily activate the vehicle's autonomous driving function if the stress level is high. For example, the monitoring unit monitors the driver's stress level using a stress sensor. If the driver's stress level is high, the response unit temporarily activates the vehicle's autonomous driving function. For example, the monitoring unit monitors the driver's stress level using a stress sensor. If the driver's stress level is high, the response unit activates the vehicle's autonomous driving function and performs a lane change. The response unit can also adjust the vehicle's speed by activating the autonomous driving function according to the driver's stress level. This reduces the driver's burden by activating the autonomous driving function according to the driver's stress level.

[0042] The speech recognition unit can improve recognition accuracy by referring to the driver's past voice command history during speech recognition. For example, the speech recognition unit refers to past voice command history stored in a database. The speech recognition unit improves recognition accuracy based on past voice command history. For example, the speech recognition unit prioritizes recognizing voice commands that have been used frequently in the past. The speech recognition unit predicts commands used during specific time periods from past voice command history and improves recognition accuracy. The speech recognition unit can also improve recognition accuracy by analyzing past voice command history and learning the driver's pronunciation habits. This improves recognition accuracy by referring to past voice command history.

[0043] The voice recognition unit can dynamically adjust its recognition algorithm during voice recognition, taking into account the noise level inside the vehicle. For example, the voice recognition unit measures the noise level inside the vehicle using a noise sensor. The voice recognition unit dynamically adjusts the recognition algorithm according to the noise level inside the vehicle. For example, when the vehicle is quiet, the voice recognition unit uses the normal recognition algorithm. When the vehicle is noisy, the voice recognition unit uses noise cancellation technology to improve recognition accuracy. The voice recognition unit can also adjust the recognition algorithm in real time when the noise level inside the vehicle fluctuates, maintaining optimal recognition accuracy. As a result, recognition accuracy is improved by adjusting the recognition algorithm according to the noise level inside the vehicle.

[0044] The voice recognition unit can prioritize recognizing commands that are highly relevant to the driver's location during voice recognition. For example, the voice recognition unit obtains the driver's geographical location using GPS data. Based on the driver's geographical location, the voice recognition unit prioritizes recognizing commands that are highly relevant to the driver. For example, if the driver is in a specific area, the voice recognition unit prioritizes recognizing voice commands related to that area. If the driver is on a highway, the voice recognition unit prioritizes recognizing voice commands related to highways. If the driver is in a parking lot, the voice recognition unit can also prioritize recognizing voice commands related to parking. This improves recognition accuracy by prioritizing the recognition of commands that are highly relevant based on the driver's geographical location.

[0045] The speech recognition unit can analyze the driver's social media activity during speech recognition and recognize relevant commands. For example, the speech recognition unit analyzes the content of social media posts. Based on the driver's social media activity, the speech recognition unit recognizes relevant commands. For example, if the driver is participating in a specific event on social media, the speech recognition unit will prioritize recognizing voice commands related to that event. If the driver is checking in to a specific location on social media, the speech recognition unit will prioritize recognizing voice commands related to that location. The speech recognition unit can also prioritize recognizing voice commands related to a specific interest if the driver has shown a particular interest on social media. This improves recognition accuracy by recognizing relevant commands based on the driver's social media activity.

[0046] The control unit can select the optimal control method by referring to the driver's past operation history during control. For example, the control unit refers to past operation history stored in a database. Based on the past operation history, the control unit selects the optimal control method. For example, the control unit prioritizes applying air conditioner settings that have been frequently used in the past. The control unit predicts and applies settings used during specific time periods based on past operation history. The control unit can also analyze past operation history and select a control method that matches the driver's preferences. This makes it possible to perform optimal control tailored to the driver's preferences by referring to past operation history.

[0047] The control unit can dynamically adjust the control algorithm based on the vehicle's current state and driving conditions during control. For example, the control unit monitors the vehicle's speed and fuel level. The control unit dynamically adjusts the control algorithm based on the vehicle's current state and driving conditions. For example, if the vehicle's fuel level is low, the control unit changes the air conditioning setting to energy-saving mode. If the road conditions are congested, the control unit changes the navigation route to a detour route. The control unit can also automatically adjust the music volume if the vehicle is traveling at high speed. This allows for optimal control by adjusting the control algorithm according to the vehicle's state and driving conditions.

[0048] The control unit can select the optimal control method during control by considering the driver's geographical location information. For example, the control unit obtains the driver's geographical location information using GPS data. Based on the driver's geographical location information, the control unit selects the optimal control method. For example, if the driver is in a specific area, the control unit selects a control method related to that area. If the driver is on a highway, the control unit selects a control method related to highways. If the driver is in a parking lot, the control unit can also select a control method related to parking. By selecting the optimal control method based on the driver's geographical location information, the accuracy of the control is improved.

[0049] The control unit can analyze the driver's social media activity during control and propose relevant control methods. For example, the control unit analyzes the content of social media posts. Based on the driver's social media activity, the control unit proposes relevant control methods. For example, if the driver is participating in a specific event on social media, the control unit proposes a control method related to that event. If the driver is checking in to a specific location on social media, the control unit proposes a control method related to that location. If the driver is showing a specific interest on social media, the control unit can also propose a control method related to that interest. This allows for control tailored to the driver's preferences by proposing relevant control methods based on the driver's social media activity.

[0050] The monitoring unit can improve monitoring accuracy by referring to the driver's past health data during monitoring. For example, the monitoring unit refers to past health data stored in electronic medical records or health management apps. The monitoring unit improves monitoring accuracy based on past health data. For example, the monitoring unit improves monitoring accuracy by predicting the driver's normal heart rate and stress level based on past health data. The monitoring unit improves monitoring accuracy by predicting the driver's health status during specific time periods based on past health data. The monitoring unit can also improve monitoring accuracy by analyzing past health data and predicting fluctuations in the driver's health status. Thus, monitoring accuracy is improved by referring to past health data.

[0051] The monitoring unit can dynamically adjust the monitoring algorithm based on the vehicle's current state and driving conditions during monitoring. For example, the monitoring unit monitors the vehicle's speed and fuel level. It dynamically adjusts the monitoring algorithm based on the vehicle's current state and driving conditions. For example, the monitoring unit improves the accuracy of heart rate and stress level monitoring when the vehicle is moving at high speeds. It quickly adjusts monitoring accuracy when driving conditions are congested. The monitoring unit can also improve the accuracy of monitoring the driver's health when the vehicle's fuel level is low. This improves monitoring accuracy by adjusting the monitoring algorithm according to the vehicle's state and driving conditions.

[0052] The monitoring unit can select the optimal monitoring method during monitoring, taking into account the driver's geographical location information. For example, the monitoring unit acquires the driver's geographical location information using GPS data. The monitoring unit selects the optimal monitoring method based on the driver's geographical location information. For example, if the driver is in a specific area, the monitoring unit selects a monitoring method related to that area. If the driver is on a highway, the monitoring unit selects a monitoring method related to highways. If the driver is in a parking lot, the monitoring unit can also select a monitoring method related to parking. By selecting the optimal monitoring method based on the driver's geographical location information, monitoring accuracy is improved.

[0053] The monitoring unit can analyze the driver's social media activity during monitoring and propose relevant monitoring methods. For example, the monitoring unit analyzes the content of social media posts. Based on the driver's social media activity, the monitoring unit proposes relevant monitoring methods. For example, if the driver participates in a specific event on social media, the monitoring unit proposes monitoring methods related to that event. If the driver checks in to a specific location on social media, the monitoring unit proposes monitoring methods related to that location. If the driver shows a specific interest on social media, the monitoring unit can also propose monitoring methods related to that interest. This improves monitoring accuracy by proposing relevant monitoring methods based on the driver's social media activity.

[0054] The response unit can select the optimal response method by referring to the driver's past response history when responding. For example, the response unit refers to past response history stored in a database. The response unit selects the optimal response method based on past response history. For example, the response unit prioritizes applying response methods that have been frequently used in the past. The response unit predicts and applies response methods used during specific time periods based on past response history. The response unit can also analyze past response history and select a response method that suits the driver's preferences. This makes it possible to provide an optimal response tailored to the driver's preferences by referring to past response history.

[0055] The response unit can dynamically adjust its response algorithm based on the vehicle's current state and driving conditions when responding. For example, the response unit monitors the vehicle's speed and fuel level. It dynamically adjusts the response algorithm based on the vehicle's current state and driving conditions. For example, if the vehicle is moving at high speed, the response unit provides a quick and concise response. If the driving conditions are congested, the response unit provides a detailed response. If the vehicle's fuel level is low, the response unit can also prioritize providing fuel-related information. This allows for an appropriate response by adjusting the response algorithm according to the vehicle's state and driving conditions.

[0056] The response unit can select the optimal response method when responding, taking into account the driver's geographical location information. For example, the response unit obtains the driver's geographical location information using GPS data. The response unit selects the optimal response method based on the driver's geographical location information. For example, if the driver is in a specific area, the response unit selects a response method related to that area. If the driver is on a highway, the response unit selects a response method related to highways. If the driver is in a parking lot, the response unit can also select a response method related to parking. By selecting the optimal response method based on the driver's geographical location information, the accuracy of the response is improved.

[0057] The response unit can analyze the driver's social media activity and suggest relevant response methods when responding. For example, the response unit analyzes the content of social media posts. Based on the driver's social media activity, the response unit suggests relevant response methods. For example, if the driver is participating in a specific event on social media, the response unit suggests a response method related to that event. If the driver is checking in to a specific location on social media, the response unit suggests a response method related to that location. If the driver is showing a specific interest on social media, the response unit can also suggest a response method related to that interest. This improves the accuracy of responses by suggesting relevant response methods based on the driver's social media activity.

[0058] The emergency call unit can select the optimal emergency call method by referring to the driver's past emergency call history when an emergency call is made. For example, the emergency call unit refers to past emergency call history stored in a database. The emergency call unit selects the optimal emergency call method based on past emergency call history. For example, the emergency call unit prioritizes emergency call methods that have been frequently used in the past. The emergency call unit predicts and applies emergency call methods used during specific time periods based on past emergency call history. The emergency call unit can also analyze past emergency call history and select an emergency call method that suits the driver's preferences. This makes it possible to make the optimal emergency call tailored to the driver's preferences by referring to past emergency call history.

[0059] The emergency call unit can dynamically adjust its notification algorithm based on the vehicle's current status and driving conditions during an emergency call. For example, the emergency call unit monitors the vehicle's speed and fuel level. It dynamically adjusts the notification algorithm based on the vehicle's current status and driving conditions. For instance, if the vehicle is traveling at high speed, it will provide a quick and concise notification. If the road is congested, it will provide a more detailed notification. If the vehicle's fuel level is low, it can also prioritize providing fuel-related information. This allows for appropriate emergency notifications by adjusting the notification algorithm according to the vehicle's status and driving conditions.

[0060] The emergency call unit can select the most appropriate reporting method when an emergency call is made, taking into account the driver's geographical location. For example, the emergency call unit can obtain the driver's geographical location using GPS data. Based on the driver's geographical location, the emergency call unit selects the most appropriate reporting method. For example, if the driver is in a specific area, the emergency call unit will select a reporting method relevant to that area. If the driver is on a highway, the emergency call unit will select a reporting method relevant to highways. If the driver is in a parking lot, the emergency call unit can also select a reporting method relevant to parking. This improves the accuracy of emergency calls by selecting the most appropriate reporting method based on the driver's geographical location.

[0061] The emergency call department can analyze the driver's social media activity during an emergency call and suggest relevant reporting methods. For example, the emergency call department analyzes the content of social media posts. Based on the driver's social media activity, the emergency call department suggests relevant reporting methods. For example, if the driver is participating in a specific event on social media, the emergency call department suggests reporting methods related to that event. If the driver is checking in to a specific location on social media, the emergency call department suggests reporting methods related to that location. The emergency call department can also suggest reporting methods related to specific interests if the driver has shown particular interests on social media. This improves the accuracy of emergency calls by suggesting relevant reporting methods based on the driver's social media activity.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The control unit can learn the driver's past driving patterns and provide control tailored to the driver's preferences. For example, if the driver frequently uses a particular route during a specific time period, the control unit can prioritize that route in the navigation system. The control unit can also automatically play music if the driver prefers it. Furthermore, if the driver prefers a specific air conditioning setting, the control unit can automatically apply that setting. This improves driver convenience by providing control based on the driver's past driving patterns.

[0064] The monitoring unit detects the driver's body temperature, and the response unit can automatically adjust the air conditioning settings if the temperature is high. For example, if the driver's body temperature exceeds 37 degrees Celsius, the monitoring unit can lower the air conditioning temperature. Conversely, if the driver's body temperature is low, the monitoring unit can also raise the air conditioning temperature. This automatically adjusts the air conditioning settings according to the driver's body temperature, improving comfort inside the vehicle.

[0065] The control unit can learn the driver's past driving patterns and provide control tailored to the driver's preferences. For example, if the driver frequently uses a particular route during a specific time period, the control unit can prioritize that route in the navigation system. The control unit can also automatically play music if the driver prefers it. Furthermore, if the driver prefers a specific air conditioning setting, the control unit can automatically apply that setting. This improves driver convenience by providing control based on the driver's past driving patterns.

[0066] The monitoring unit detects the driver's body temperature, and the response unit can automatically adjust the air conditioning settings if the temperature is high. For example, if the driver's body temperature exceeds 37 degrees Celsius, the monitoring unit can lower the air conditioning temperature. Conversely, if the driver's body temperature is low, the monitoring unit can also raise the air conditioning temperature. This automatically adjusts the air conditioning settings according to the driver's body temperature, improving comfort inside the vehicle.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The voice recognition unit recognizes the driver's voice commands. These commands may include, but are not limited to, commands such as "Turn on the air conditioner" or "Start navigation." The voice recognition unit analyzes and recognizes the voice commands using AI, for example. Step 2: The control unit controls the car's functions based on voice commands recognized by the voice recognition unit. For example, the control unit can change the air conditioning settings or start the navigation system. The control unit is controlled by AI. Step 3: The monitoring unit monitors the driver's condition and the external environment. For example, the monitoring unit monitors the driver's heart rate and stress level, as well as external weather and road conditions. The monitoring unit uses AI for monitoring. Step 4: The response unit automatically responds based on the information monitored by the monitoring unit. For example, if the driver's heart rate is elevated, the response unit may play relaxing music or automatically adjust the air conditioning settings to maintain a specific temperature inside the car. The response unit uses AI to automatically respond. Step 5: The emergency call unit automatically makes emergency calls and requests support in the event of an emergency. For example, if an accident occurs, the emergency call unit will automatically make an emergency call and request support. The emergency call unit uses AI to make emergency calls and requests support.

[0069] (Example of form 2) The AI ​​assistant for assisting car operation via voice commands according to an embodiment of the present invention is a system that recognizes the driver's voice commands and controls various functions of the car (navigation, air conditioning, music, etc.). This system recognizes the driver's voice commands and controls the functions of the car. It also monitors the driver's condition and the external environment and makes automatic responses based on that information. Furthermore, in the event of an emergency, it makes automatic calls and requests for support. For example, it recognizes the driver's voice commands. For example, it recognizes voice commands such as "Turn on the air conditioning" or "Start navigation." These voice commands are analyzed by the AI. Next, it controls the functions of the car based on the recognized voice commands. For example, it changes the settings of the air conditioning or starts navigation. This control is performed by the AI. Furthermore, it monitors the driver's condition and the external environment. For example, it monitors the driver's heart rate and stress level, and monitors the weather and road conditions outside. This monitoring is performed by the AI. It makes automatic responses based on the monitored information. For example, if the driver's heart rate is elevated, it plays relaxing music or automatically adjusts the air conditioning settings to maintain the temperature inside the car at a specific temperature. This automatic response is performed by the AI. Finally, in the event of an emergency, it makes automatic calls and requests for support. For example, in the event of an accident, the system automatically makes an emergency call and requests assistance. This emergency call and assistance request are handled by AI. This system allows the driver to easily control various functions of the car using voice commands, improving safety and comfort while driving. Furthermore, it enables automatic responses and emergency actions that are tailored to the driver's condition and external environment, allowing for safer driving. In this way, the AI ​​assistant that assists with car operation via voice commands can recognize the driver's voice commands and control various functions of the car, thereby improving safety and comfort while driving.

[0070] The AI ​​assistant for assisting car operation via voice commands according to this embodiment comprises a voice recognition unit, a control unit, a monitoring unit, a response unit, and an emergency call unit. The voice recognition unit recognizes the driver's voice commands. The driver's voice commands include, but are not limited to, commands such as "Turn on the air conditioner" or "Start navigation." The voice recognition unit analyzes and recognizes the voice commands using AI, for example. The control unit controls the car's functions based on the voice commands recognized by the voice recognition unit. The control unit, for example, changes the air conditioner settings or starts navigation. The control unit performs control using AI. The monitoring unit monitors the driver's condition and the external environment. The monitoring unit, for example, monitors the driver's heart rate and stress level, and monitors external weather and road conditions. The monitoring unit performs monitoring using AI. The response unit provides an automatic response based on the information monitored by the monitoring unit. For example, if the driver's heart rate is elevated, the response unit may play relaxing music or automatically adjust the air conditioner settings to maintain the interior temperature at a specific temperature. The response unit automatically responds using AI. The emergency call unit automatically makes an emergency call and requests support in the event of an emergency. For example, if an accident occurs, the emergency call unit automatically makes an emergency call and requests support. The emergency call unit makes emergency calls and requests support using AI. As a result, the AI ​​assistant that assists in operating the car using voice commands according to this embodiment can improve safety and comfort while driving by recognizing the driver's voice commands and controlling various functions of the car.

[0071] The voice recognition unit recognizes the driver's voice commands. These commands include, but are not limited to, commands such as "Turn on the air conditioner" or "Start navigation." The voice recognition unit analyzes and recognizes voice commands using AI. Specifically, the voice recognition unit collects the driver's voice using a high-sensitivity microphone and removes in-car noise using noise cancellation technology. Next, the collected voice data is sent to the voice recognition AI, where the voice commands are analyzed. This AI analyzes the voice data using deep learning technology and recognizes the driver's intentions with high accuracy. For example, the voice recognition AI converts the voice data into text data and analyzes that text data using natural language processing (NLP) technology. This allows it to understand the meaning of the voice command and identify the appropriate action. Furthermore, the voice recognition unit can learn the characteristics of the driver's voice and perform voice recognition optimized for each individual driver. This improves the accuracy of voice recognition and reduces the risk of misrecognition. The voice recognition unit also supports multiple languages ​​and dialects, making it possible to accommodate drivers who speak different languages. This allows the voice recognition unit to recognize the driver's voice commands with high accuracy, enabling smooth operation of the vehicle.

[0072] The control unit controls the car's functions based on voice commands recognized by the voice recognition unit. For example, the control unit can change the air conditioning settings or start the navigation system. The control unit is controlled by AI. Specifically, the control unit receives commands from the voice recognition unit and executes the corresponding car functions. For example, if the command "Turn on the air conditioner" is recognized, the control unit turns on the air conditioner and adjusts the temperature and fan speed. If the command "Start navigation" is recognized, the control unit starts the navigation system and begins setting the destination and providing route guidance. The control unit uses AI to understand the driver's intentions and perform optimal control. For example, even if the driver gives a vague command such as "Make it a little cooler," the AI ​​can consider the driver's past settings and the current in-car environment to make appropriate temperature adjustments. Furthermore, the control unit utilizes data from various car sensors to perform real-time, situation-appropriate control. For example, it can optimize the air conditioning settings by considering the outside temperature and humidity, the number of occupants in the car, etc. As a result, the control unit can accurately control the car's functions based on the driver's voice commands and provide a comfortable driving environment.

[0073] The monitoring unit monitors the driver's condition and the external environment. For example, it monitors the driver's heart rate and stress level, as well as external weather and road conditions. The monitoring unit uses AI for monitoring. Specifically, the monitoring unit uses heart rate sensors and stress level sensors to understand the driver's condition. These sensors are attached to the driver's wrist or chest and collect data in real time. The collected data is analyzed by AI to determine whether the driver's heart rate and stress level are within the normal range. The monitoring unit also monitors the external environment using cameras and sensors mounted on the vehicle. For example, cameras photograph road conditions and weather, and sensors measure road surface slipperiness and temperature. This data is analyzed by AI to determine whether warnings or assistance to the driver are necessary. Furthermore, the monitoring unit can learn the driver's driving patterns and behaviors and detect abnormal driving behaviors. For example, if sudden braking or sudden steering maneuvers occur frequently, it can detect driver fatigue or decreased attention and issue appropriate alerts. This allows the monitoring unit to monitor the driver's condition and the external environment in real time, supporting safe driving.

[0074] The response unit provides automatic responses based on information monitored by the monitoring unit. For example, if the driver's heart rate is elevated, the response unit may play relaxing music or automatically adjust the air conditioning settings to maintain a specific temperature inside the vehicle. The response unit uses AI to provide automatic responses. Specifically, the response unit receives data sent from the monitoring unit and provides an appropriate response based on that data. For example, if the driver's heart rate is elevated, the response unit selects relaxing music and plays it through the vehicle's audio system. It also automatically adjusts the air conditioning settings to maintain a comfortable temperature inside the vehicle. Furthermore, if the driver's stress level is high, the response unit can provide voice guidance encouraging deep breathing or suggest that the driver take a break. The response unit uses AI to analyze the driver's condition and provide the optimal response. For example, it can provide individually customized responses considering the driver's past data and current situation. This allows the response unit to provide appropriate responses according to the driver's condition, reduce stress while driving, and provide a comfortable driving environment.

[0075] The emergency call unit automatically makes emergency calls and requests for support in the event of an emergency. For example, in the event of an accident, the emergency call unit automatically makes an emergency call and requests support. The emergency call unit uses AI to make emergency calls and requests for support. Specifically, the emergency call unit uses sensors and cameras mounted on the vehicle to detect the occurrence of an accident. For example, if a collision sensor detects a strong impact or a camera confirms damage to the vehicle, the emergency call unit immediately makes an emergency call. The emergency call unit uses GPS data to determine the current location of the vehicle and transmits it to the emergency call center. It can also monitor the driver's condition and request medical support as needed. For example, if the driver is unconscious or has an abnormally low heart rate, the emergency call unit will make an emergency call to a medical facility to encourage a rapid response. Furthermore, the emergency call unit can record the details of the accident and the condition of the vehicle to help with subsequent responses. This allows the emergency call unit to make quick and appropriate calls and requests for support in emergencies, ensuring the safety of the driver.

[0076] The control unit can control various functions such as navigation, air conditioning, audio, lighting, and driving functions. For example, the control unit can set destinations and provide route guidance for the navigation system. It can also set the temperature and adjust the airflow of the air conditioner. Furthermore, it can adjust the volume and select songs for the audio system. For example, the control unit sets a destination for the navigation system and starts route guidance. The control unit sets the air conditioner temperature to 22 degrees and adjusts the airflow. The control unit adjusts the audio volume to an appropriate level and selects songs. In this way, the control unit's control over various functions of the car improves driver convenience.

[0077] The control unit can change the color of the lighting to match the tone of the driver's voice commands. For example, if the driver's voice commands are in a relaxed tone, the control unit will change the lighting color to blue. The control unit can also change the lighting color to red if the driver's voice commands are in a tense tone. Furthermore, the control unit can change the lighting color to white if the driver's voice commands are in a neutral tone. In this way, by changing the lighting color according to the tone of the driver's voice commands, an environment that matches the driver's emotions can be provided.

[0078] The monitoring unit detects the intensity of sunlight, and the response unit automatically adjusts the air conditioner settings to maintain the interior temperature at 22 degrees Celsius. For example, the monitoring unit uses a light sensor to detect the intensity of sunlight. The response unit automatically adjusts the air conditioner settings according to the intensity of sunlight to maintain the interior temperature at 22 degrees Celsius. For example, the monitoring unit uses a light sensor to detect the intensity of sunlight. If the sunlight is strong, the response unit adjusts the air conditioner setting to a lower temperature to maintain the interior temperature at 22 degrees Celsius. If the sunlight is weak, the response unit adjusts the air conditioner setting to a higher temperature to maintain the interior temperature at 22 degrees Celsius. This automatically adjusts the air conditioner settings according to the intensity of sunlight, improving comfort inside the vehicle.

[0079] The monitoring unit monitors the driver's heart rate when entering a gravel or unpaved road, and the response unit can change the vehicle's driving mode if the driver's heart rate rises to 100 bpm or higher. The monitoring unit monitors the driver's heart rate using, for example, a heart rate sensor. The response unit changes the vehicle's driving mode if the driver's heart rate rises to 100 bpm or higher. For example, the monitoring unit monitors the driver's heart rate using a heart rate sensor. If the driver's heart rate rises to 100 bpm or higher, the response unit changes the vehicle's driving mode from eco mode to sport mode. The response unit can also change the vehicle's driving mode from sport mode to eco mode if the driver's heart rate rises to 100 bpm or higher. This improves driver safety by changing the vehicle's driving mode according to the driver's heart rate.

[0080] The monitoring unit monitors the driver's health condition when the road is frozen, and the response unit can prompt the driver to take a break and guide them to a safe location if their blood pressure is 140 / 90 mmHg or higher. The monitoring unit, for example, uses a temperature sensor to detect whether the road is frozen. The response unit prompts the driver to take a break and guides them to a safe location if their blood pressure is 140 / 90 mmHg or higher. For example, the monitoring unit uses a temperature sensor to detect whether the road is frozen. If the driver's blood pressure is 140 / 90 mmHg or higher, the response unit prompts the driver to take a break using an audio alert and guides them to a safe location. The response unit can also prompt the driver to take a break and guide them to a safe location using a display if their blood pressure is 140 / 90 mmHg or higher. This improves driver safety by prompting breaks and guiding drivers to safe locations according to their health condition.

[0081] The monitoring unit monitors the driver's stress level when surrounding vehicles brake suddenly, and the response unit can play relaxing music when the driver's stress level rises to 50 or higher. For example, the monitoring unit uses a radar sensor to detect whether surrounding vehicles have braked suddenly. If the driver's stress level rises to 50 or higher, the response unit plays relaxing music. For example, the monitoring unit uses a radar sensor to detect whether surrounding vehicles have braked suddenly. If the driver's stress level rises to 50 or higher, the response unit plays classical music. The response unit can also play nature sounds if the driver's stress level rises to 50 or higher. This reduces driver stress by playing relaxing music according to the driver's stress level.

[0082] The monitoring unit monitors the driver's stress level when changing lanes on a highway, and the response unit can temporarily activate the vehicle's autonomous driving function if the stress level is high. For example, the monitoring unit monitors the driver's stress level using a stress sensor. If the driver's stress level is high, the response unit temporarily activates the vehicle's autonomous driving function. For example, the monitoring unit monitors the driver's stress level using a stress sensor. If the driver's stress level is high, the response unit activates the vehicle's autonomous driving function and performs a lane change. The response unit can also adjust the vehicle's speed by activating the autonomous driving function according to the driver's stress level. This reduces the driver's burden by activating the autonomous driving function according to the driver's stress level.

[0083] The speech recognition unit can estimate the driver's emotions and adjust the accuracy of voice command recognition based on the estimated emotions. For example, the speech recognition unit might use facial recognition technology to estimate the driver's emotions. The speech recognition unit adjusts the accuracy of voice command recognition based on the driver's emotions. For example, if the driver is tense, the speech recognition unit might request clearer pronunciation to improve the accuracy of voice command recognition. If the driver is relaxed, the speech recognition unit might ease the accuracy of voice command recognition, allowing for recognition even with natural pronunciation. If the driver is in a hurry, the speech recognition unit can also quickly adjust the accuracy of voice command recognition and react immediately. This improves recognition accuracy by adjusting the accuracy of voice command recognition according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The speech recognition unit can improve recognition accuracy by referring to the driver's past voice command history during speech recognition. For example, the speech recognition unit refers to past voice command history stored in a database. The speech recognition unit improves recognition accuracy based on past voice command history. For example, the speech recognition unit prioritizes recognizing voice commands that have been used frequently in the past. The speech recognition unit predicts commands used during specific time periods from past voice command history and improves recognition accuracy. The speech recognition unit can also improve recognition accuracy by analyzing past voice command history and learning the driver's pronunciation habits. This improves recognition accuracy by referring to past voice command history.

[0085] The voice recognition unit can dynamically adjust its recognition algorithm during voice recognition, taking into account the noise level inside the vehicle. For example, the voice recognition unit measures the noise level inside the vehicle using a noise sensor. The voice recognition unit dynamically adjusts the recognition algorithm according to the noise level inside the vehicle. For example, when the vehicle is quiet, the voice recognition unit uses the normal recognition algorithm. When the vehicle is noisy, the voice recognition unit uses noise cancellation technology to improve recognition accuracy. The voice recognition unit can also adjust the recognition algorithm in real time when the noise level inside the vehicle fluctuates, maintaining optimal recognition accuracy. As a result, recognition accuracy is improved by adjusting the recognition algorithm according to the noise level inside the vehicle.

[0086] The voice recognition unit can estimate the driver's emotions and determine the priority of voice commands based on the estimated emotions. For example, the voice recognition unit might use facial recognition technology to estimate the driver's emotions. Based on the driver's emotions, the voice recognition unit prioritizes important voice commands. For example, if the driver is stressed, the voice recognition unit prioritizes recognizing important voice commands. If the driver is relaxed, the voice recognition unit recognizes all voice commands equally. If the driver is in a hurry, the voice recognition unit can also prioritize recognizing urgent voice commands. This allows for the priority of important commands by determining the priority of voice commands according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The voice recognition unit can prioritize recognizing commands that are highly relevant to the driver's location during voice recognition. For example, the voice recognition unit obtains the driver's geographical location using GPS data. Based on the driver's geographical location, the voice recognition unit prioritizes recognizing commands that are highly relevant to the driver. For example, if the driver is in a specific area, the voice recognition unit prioritizes recognizing voice commands related to that area. If the driver is on a highway, the voice recognition unit prioritizes recognizing voice commands related to highways. If the driver is in a parking lot, the voice recognition unit can also prioritize recognizing voice commands related to parking. This improves recognition accuracy by prioritizing the recognition of commands that are highly relevant based on the driver's geographical location.

[0088] The speech recognition unit can analyze the driver's social media activity during speech recognition and recognize relevant commands. For example, the speech recognition unit analyzes the content of social media posts. Based on the driver's social media activity, the speech recognition unit recognizes relevant commands. For example, if the driver is participating in a specific event on social media, the speech recognition unit will prioritize recognizing voice commands related to that event. If the driver is checking in to a specific location on social media, the speech recognition unit will prioritize recognizing voice commands related to that location. The speech recognition unit can also prioritize recognizing voice commands related to a specific interest if the driver has shown a particular interest on social media. This improves recognition accuracy by recognizing relevant commands based on the driver's social media activity.

[0089] The control unit can estimate the driver's emotions and adjust the control method based on the estimated emotions. The control unit can estimate the driver's emotions, for example, using facial recognition technology. The control unit adjusts the control method based on the driver's emotions. For example, if the driver is tense, the control unit adjusts the air conditioning to a relaxing temperature. If the driver is relaxed, the control unit adjusts the music volume appropriately. If the driver is in a hurry, the control unit can also change the navigation route to the shortest route. This improves driver comfort by adjusting the control method according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The control unit can select the optimal control method by referring to the driver's past operation history during control. For example, the control unit refers to past operation history stored in a database. Based on the past operation history, the control unit selects the optimal control method. For example, the control unit prioritizes applying air conditioner settings that have been frequently used in the past. The control unit predicts and applies settings used during specific time periods based on past operation history. The control unit can also analyze past operation history and select a control method that matches the driver's preferences. This makes it possible to perform optimal control tailored to the driver's preferences by referring to past operation history.

[0091] The control unit can dynamically adjust the control algorithm based on the vehicle's current state and driving conditions during control. For example, the control unit monitors the vehicle's speed and fuel level. The control unit dynamically adjusts the control algorithm based on the vehicle's current state and driving conditions. For example, if the vehicle's fuel level is low, the control unit changes the air conditioning setting to energy-saving mode. If the road conditions are congested, the control unit changes the navigation route to a detour route. The control unit can also automatically adjust the music volume if the vehicle is traveling at high speed. This allows for optimal control by adjusting the control algorithm according to the vehicle's state and driving conditions.

[0092] The control unit can estimate the driver's emotions and determine control priorities based on the estimated emotions. The control unit may, for example, use facial recognition technology to estimate the driver's emotions. The control unit then determines control priorities based on the driver's emotions. For example, if the driver is stressed, the control unit will prioritize important controls. If the driver is relaxed, the control unit will perform all controls equally. If the driver is in a hurry, the control unit may also prioritize urgent controls. This allows for prioritizing important controls by determining control priorities according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The control unit can select the optimal control method during control by considering the driver's geographical location information. For example, the control unit obtains the driver's geographical location information using GPS data. Based on the driver's geographical location information, the control unit selects the optimal control method. For example, if the driver is in a specific area, the control unit selects a control method related to that area. If the driver is on a highway, the control unit selects a control method related to highways. If the driver is in a parking lot, the control unit can also select a control method related to parking. By selecting the optimal control method based on the driver's geographical location information, the accuracy of the control is improved.

[0094] The control unit can analyze the driver's social media activity during control and propose relevant control methods. For example, the control unit analyzes the content of social media posts. Based on the driver's social media activity, the control unit proposes relevant control methods. For example, if the driver is participating in a specific event on social media, the control unit proposes a control method related to that event. If the driver is checking in to a specific location on social media, the control unit proposes a control method related to that location. If the driver is showing a specific interest on social media, the control unit can also propose a control method related to that interest. This allows for control tailored to the driver's preferences by proposing relevant control methods based on the driver's social media activity.

[0095] The monitoring unit can estimate the driver's emotions and adjust the monitoring accuracy based on the estimated emotions. For example, the monitoring unit estimates the driver's emotions using facial recognition technology. The monitoring unit adjusts the monitoring accuracy based on the driver's emotions. For example, if the driver is tense, the monitoring unit increases the accuracy of heart rate and stress level monitoring. If the driver is relaxed, the monitoring unit reduces the monitoring accuracy to maintain a natural state. If the driver is in a hurry, the monitoring unit can also quickly adjust the monitoring accuracy and react immediately. This improves monitoring accuracy by adjusting it according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The monitoring unit can improve monitoring accuracy by referring to the driver's past health data during monitoring. For example, the monitoring unit refers to past health data stored in electronic medical records or health management apps. The monitoring unit improves monitoring accuracy based on past health data. For example, the monitoring unit improves monitoring accuracy by predicting the driver's normal heart rate and stress level based on past health data. The monitoring unit improves monitoring accuracy by predicting the driver's health status during specific time periods based on past health data. The monitoring unit can also improve monitoring accuracy by analyzing past health data and predicting fluctuations in the driver's health status. Thus, monitoring accuracy is improved by referring to past health data.

[0097] The monitoring unit can dynamically adjust the monitoring algorithm based on the vehicle's current state and driving conditions during monitoring. For example, the monitoring unit monitors the vehicle's speed and fuel level. It dynamically adjusts the monitoring algorithm based on the vehicle's current state and driving conditions. For example, the monitoring unit improves the accuracy of heart rate and stress level monitoring when the vehicle is moving at high speeds. It quickly adjusts monitoring accuracy when driving conditions are congested. The monitoring unit can also improve the accuracy of monitoring the driver's health when the vehicle's fuel level is low. This improves monitoring accuracy by adjusting the monitoring algorithm according to the vehicle's state and driving conditions.

[0098] The monitoring unit can estimate the driver's emotions and determine monitoring priorities based on the estimated emotions. For example, the monitoring unit might use facial recognition technology to estimate the driver's emotions. The monitoring unit then determines monitoring priorities based on the driver's emotions. For example, if the driver is stressed, the monitoring unit will prioritize monitoring important items. If the driver is relaxed, the monitoring unit will monitor all items equally. If the driver is in a hurry, the monitoring unit can also prioritize monitoring items of high urgency. This allows for prioritizing important monitoring items by determining monitoring priorities according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The monitoring unit can select the optimal monitoring method during monitoring, taking into account the driver's geographical location information. For example, the monitoring unit acquires the driver's geographical location information using GPS data. The monitoring unit selects the optimal monitoring method based on the driver's geographical location information. For example, if the driver is in a specific area, the monitoring unit selects a monitoring method related to that area. If the driver is on a highway, the monitoring unit selects a monitoring method related to highways. If the driver is in a parking lot, the monitoring unit can also select a monitoring method related to parking. By selecting the optimal monitoring method based on the driver's geographical location information, monitoring accuracy is improved.

[0100] The monitoring unit can analyze the driver's social media activity during monitoring and propose relevant monitoring methods. For example, the monitoring unit analyzes the content of social media posts. Based on the driver's social media activity, the monitoring unit proposes relevant monitoring methods. For example, if the driver participates in a specific event on social media, the monitoring unit proposes monitoring methods related to that event. If the driver checks in to a specific location on social media, the monitoring unit proposes monitoring methods related to that location. If the driver shows a specific interest on social media, the monitoring unit can also propose monitoring methods related to that interest. This improves monitoring accuracy by proposing relevant monitoring methods based on the driver's social media activity.

[0101] The response unit can estimate the driver's emotions and adjust its response method based on the estimated emotions. For example, the response unit might use facial recognition technology to estimate the driver's emotions. The response unit adjusts its response method based on the driver's emotions. For example, if the driver is tense, the response unit will respond in a calm voice. If the driver is relaxed, the response unit will respond in a cheerful voice. If the driver is in a hurry, the response unit can also provide a quick and concise response. This allows for an appropriate response by adjusting the response method according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The response unit can select the optimal response method by referring to the driver's past response history when responding. For example, the response unit refers to past response history stored in a database. The response unit selects the optimal response method based on past response history. For example, the response unit prioritizes applying response methods that have been frequently used in the past. The response unit predicts and applies response methods used during specific time periods based on past response history. The response unit can also analyze past response history and select a response method that suits the driver's preferences. This makes it possible to provide an optimal response tailored to the driver's preferences by referring to past response history.

[0103] The response unit can dynamically adjust its response algorithm based on the vehicle's current state and driving conditions when responding. For example, the response unit monitors the vehicle's speed and fuel level. It dynamically adjusts the response algorithm based on the vehicle's current state and driving conditions. For example, if the vehicle is moving at high speed, the response unit provides a quick and concise response. If the driving conditions are congested, the response unit provides a detailed response. If the vehicle's fuel level is low, the response unit can also prioritize providing fuel-related information. This allows for an appropriate response by adjusting the response algorithm according to the vehicle's state and driving conditions.

[0104] The response unit can estimate the driver's emotions and determine the priority of responses based on the estimated emotions. The response unit estimates the driver's emotions, for example, using facial recognition technology. The response unit determines the priority of responses based on the driver's emotions. For example, if the driver is stressed, the response unit prioritizes important responses. If the driver is relaxed, the response unit provides all responses equally. If the driver is in a hurry, the response unit may also prioritize urgent responses. This allows for prioritizing important responses by determining the priority of responses according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The response unit can select the optimal response method when responding, taking into account the driver's geographical location information. For example, the response unit obtains the driver's geographical location information using GPS data. The response unit selects the optimal response method based on the driver's geographical location information. For example, if the driver is in a specific area, the response unit selects a response method related to that area. If the driver is on a highway, the response unit selects a response method related to highways. If the driver is in a parking lot, the response unit can also select a response method related to parking. By selecting the optimal response method based on the driver's geographical location information, the accuracy of the response is improved.

[0106] The response unit can analyze the driver's social media activity and suggest relevant response methods when responding. For example, the response unit analyzes the content of social media posts. Based on the driver's social media activity, the response unit suggests relevant response methods. For example, if the driver is participating in a specific event on social media, the response unit suggests a response method related to that event. If the driver is checking in to a specific location on social media, the response unit suggests a response method related to that location. If the driver is showing a specific interest on social media, the response unit can also suggest a response method related to that interest. This improves the accuracy of responses by suggesting relevant response methods based on the driver's social media activity.

[0107] The emergency call unit can estimate the driver's emotions and adjust the method of emergency call based on the estimated emotions. For example, the emergency call unit might use facial recognition technology to estimate the driver's emotions. The emergency call unit adjusts the method of emergency call based on the driver's emotions. For example, if the driver is tense, the emergency call unit will make a quick and concise call. If the driver is relaxed, the emergency call unit will make a detailed call. If the driver is in a hurry, the emergency call unit can also make an immediate call. This allows for appropriate emergency calls by adjusting the method of emergency call according to the driver's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The emergency call unit can select the optimal emergency call method by referring to the driver's past emergency call history when an emergency call is made. For example, the emergency call unit refers to past emergency call history stored in a database. The emergency call unit selects the optimal emergency call method based on past emergency call history. For example, the emergency call unit prioritizes emergency call methods that have been frequently used in the past. The emergency call unit predicts and applies emergency call methods used during specific time periods based on past emergency call history. The emergency call unit can also analyze past emergency call history and select an emergency call method that suits the driver's preferences. This makes it possible to make the optimal emergency call tailored to the driver's preferences by referring to past emergency call history.

[0109] The emergency call unit can dynamically adjust its notification algorithm based on the vehicle's current status and driving conditions during an emergency call. For example, the emergency call unit monitors the vehicle's speed and fuel level. It dynamically adjusts the notification algorithm based on the vehicle's current status and driving conditions. For instance, if the vehicle is traveling at high speed, it will provide a quick and concise notification. If the road is congested, it will provide a more detailed notification. If the vehicle's fuel level is low, it can also prioritize providing fuel-related information. This allows for appropriate emergency notifications by adjusting the notification algorithm according to the vehicle's status and driving conditions.

[0110] The emergency call unit can estimate the driver's emotions and determine the priority of emergency calls based on the estimated emotions. The emergency call unit can estimate the driver's emotions, for example, using facial recognition technology. The emergency call unit determines the priority of emergency calls based on the driver's emotions. For example, if the driver is stressed, the emergency call unit will prioritize important calls. If the driver is relaxed, the emergency call unit will distribute all calls equally. If the driver is in a hurry, the emergency call unit can also prioritize highly urgent calls. This allows for prioritizing important calls by determining the priority of emergency calls according to the driver's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The emergency call unit can select the most appropriate reporting method when an emergency call is made, taking into account the driver's geographical location. For example, the emergency call unit can obtain the driver's geographical location using GPS data. Based on the driver's geographical location, the emergency call unit selects the most appropriate reporting method. For example, if the driver is in a specific area, the emergency call unit will select a reporting method relevant to that area. If the driver is on a highway, the emergency call unit will select a reporting method relevant to highways. If the driver is in a parking lot, the emergency call unit can also select a reporting method relevant to parking. This improves the accuracy of emergency calls by selecting the most appropriate reporting method based on the driver's geographical location.

[0112] The emergency call department can analyze the driver's social media activity during an emergency call and suggest relevant reporting methods. For example, the emergency call department analyzes the content of social media posts. Based on the driver's social media activity, the emergency call department suggests relevant reporting methods. For example, if the driver is participating in a specific event on social media, the emergency call department suggests reporting methods related to that event. If the driver is checking in to a specific location on social media, the emergency call department suggests reporting methods related to that location. The emergency call department can also suggest reporting methods related to specific interests if the driver has shown particular interests on social media. This improves the accuracy of emergency calls by suggesting relevant reporting methods based on the driver's social media activity.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The voice recognition unit can analyze the driver's voice tone and speed to estimate the driver's fatigue level. For example, if the driver's voice is lower than usual and the speed is slow, the voice recognition unit can estimate that the driver is tired. Furthermore, if the driver's voice is higher than usual and the speed is fast, the voice recognition unit can estimate that the driver is stressed. By estimating the driver's fatigue level based on the driver's voice tone and speed, the driver's condition can be understood more accurately.

[0115] The control unit can learn the driver's past driving patterns and provide control tailored to the driver's preferences. For example, if the driver frequently uses a particular route during a specific time period, the control unit can prioritize that route in the navigation system. The control unit can also automatically play music if the driver prefers it. Furthermore, if the driver prefers a specific air conditioning setting, the control unit can automatically apply that setting. This improves driver convenience by providing control based on the driver's past driving patterns.

[0116] The monitoring unit detects the driver's body temperature, and the response unit can automatically adjust the air conditioning settings if the temperature is high. For example, if the driver's body temperature exceeds 37 degrees Celsius, the monitoring unit can lower the air conditioning temperature. Conversely, if the driver's body temperature is low, the monitoring unit can also raise the air conditioning temperature. This automatically adjusts the air conditioning settings according to the driver's body temperature, improving comfort inside the vehicle.

[0117] The response unit can estimate the driver's emotions and provide advice based on those emotions. For example, if the driver is stressed, the response unit can advise them to take a deep breath. If the driver is relaxed, the response unit can advise them to continue driving. Furthermore, if the driver is tired, the response unit can advise them to take a break. By providing advice tailored to the driver's emotions, this improves driver safety and comfort.

[0118] The emergency call unit can estimate the driver's emotions and adjust the content of the emergency call based on those estimates. For example, if the driver is in a state of panic, the emergency call unit can make a quick and concise call. If the driver is calm, the emergency call unit can also make a detailed call. Furthermore, if the driver is confused, the emergency call unit can make a call that provides a detailed explanation of the driver's condition. This allows for an appropriate response by making an emergency call that is tailored to the driver's emotions.

[0119] The voice recognition unit can analyze the driver's voice tone and speed to estimate the driver's fatigue level. For example, if the driver's voice is lower than usual and the speed is slow, the voice recognition unit can estimate that the driver is tired. Furthermore, if the driver's voice is higher than usual and the speed is fast, the voice recognition unit can estimate that the driver is stressed. By estimating the driver's fatigue level based on the driver's voice tone and speed, the driver's condition can be understood more accurately.

[0120] The control unit can learn the driver's past driving patterns and provide control tailored to the driver's preferences. For example, if the driver frequently uses a particular route during a specific time period, the control unit can prioritize that route in the navigation system. The control unit can also automatically play music if the driver prefers it. Furthermore, if the driver prefers a specific air conditioning setting, the control unit can automatically apply that setting. This improves driver convenience by providing control based on the driver's past driving patterns.

[0121] The monitoring unit detects the driver's body temperature, and the response unit can automatically adjust the air conditioning settings if the temperature is high. For example, if the driver's body temperature exceeds 37 degrees Celsius, the monitoring unit can lower the air conditioning temperature. Conversely, if the driver's body temperature is low, the monitoring unit can also raise the air conditioning temperature. This automatically adjusts the air conditioning settings according to the driver's body temperature, improving comfort inside the vehicle.

[0122] The response unit can estimate the driver's emotions and provide advice based on those emotions. For example, if the driver is stressed, the response unit can advise them to take a deep breath. If the driver is relaxed, the response unit can advise them to continue driving. Furthermore, if the driver is tired, the response unit can advise them to take a break. By providing advice tailored to the driver's emotions, this improves driver safety and comfort.

[0123] The emergency call unit can estimate the driver's emotions and adjust the content of the emergency call based on those estimates. For example, if the driver is in a state of panic, the emergency call unit can make a quick and concise call. If the driver is calm, the emergency call unit can also make a detailed call. Furthermore, if the driver is confused, the emergency call unit can make a call that provides a detailed explanation of the driver's condition. This allows for an appropriate response by making an emergency call that is tailored to the driver's emotions.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The voice recognition unit recognizes the driver's voice commands. These commands may include, but are not limited to, commands such as "Turn on the air conditioner" or "Start navigation." The voice recognition unit analyzes and recognizes the voice commands using AI, for example. Step 2: The control unit controls the car's functions based on voice commands recognized by the voice recognition unit. For example, the control unit can change the air conditioning settings or start the navigation system. The control unit is controlled by AI. Step 3: The monitoring unit monitors the driver's condition and the external environment. For example, the monitoring unit monitors the driver's heart rate and stress level, as well as external weather and road conditions. The monitoring unit uses AI for monitoring. Step 4: The response unit automatically responds based on the information monitored by the monitoring unit. For example, if the driver's heart rate is elevated, the response unit may play relaxing music or automatically adjust the air conditioning settings to maintain a specific temperature inside the car. The response unit uses AI to automatically respond. Step 5: The emergency call unit automatically makes emergency calls and requests support in the event of an emergency. For example, if an accident occurs, the emergency call unit will automatically make an emergency call and request support. The emergency call unit uses AI to make emergency calls and requests support.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] For example, the voice recognition unit is implemented by the microphone 38B and control unit 46A of the smart device 14. For example, the control unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is implemented by the camera 42 and control unit 46A of the smart device 14. For example, the response unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the emergency notification unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] For example, the voice recognition unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214. For example, the control unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is implemented by the camera 42 and control unit 46A of the smart glasses 214. For example, the response unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the emergency notification unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the voice recognition unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314. For example, the control unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is implemented by the camera 42 and control unit 46A of the headset terminal 314. For example, the response unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the emergency notification unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] For example, the voice recognition unit is implemented by the microphone 238 and control unit 46A of the robot 414. For example, the control unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is implemented by the camera 42 and control unit 46A of the robot 414. For example, the response unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the emergency notification unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) A voice recognition unit that recognizes the driver's voice commands, A control unit controls the functions of the vehicle based on the voice commands recognized by the voice recognition unit, A monitoring unit that monitors the driver's condition and the external environment, A response unit that performs an automatic response based on the information monitored by the monitoring unit, It includes an emergency notification unit that automatically makes notifications and requests for support in emergencies. A system characterized by the following features. (Note 2) The control unit, Controls the functions of navigation, air conditioning, audio, lighting, and driving functions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The control unit, The color of the lighting is changed to a color corresponding to the tone of the driver's voice command. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, It detects the intensity of sunlight, The response unit is The air conditioner settings will automatically adjust to maintain the interior temperature at 22 degrees Celsius. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, When entering a gravel road or unpaved road, the driver's heart rate is monitored. The response unit is The vehicle's driving mode is changed if the driver's heart rate rises to 100 bpm or higher. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, When the road is frozen, monitor the driver's health condition. The response unit is If the driver's health condition is such that their blood pressure is 140 / 90 mmHg or higher, they should be encouraged to take a break and guided to a safe location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, The system monitors the driver's stress level when surrounding vehicles suddenly brake, The response unit is When the driver's stress level rises to 50 or higher, relaxing music is played. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, The system monitors the driver's stress level when changing lanes on highways. The response unit is Temporarily enable the vehicle's autonomous driving function when stress levels are high. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned speech recognition unit, The system estimates the driver's emotions and adjusts the accuracy of voice command recognition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned speech recognition unit, During voice recognition, the system improves recognition accuracy by referencing the driver's past voice command history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned speech recognition unit, During voice recognition, the recognition algorithm is dynamically adjusted to take into account the noise level inside the vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned speech recognition unit, The system estimates the driver's emotions and prioritizes voice commands based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned speech recognition unit, During voice recognition, the system prioritizes recognizing commands that are highly relevant, taking into account the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned speech recognition unit, During voice recognition, the system analyzes the driver's social media activity and recognizes relevant commands. The system described in Appendix 1, characterized by the features described herein. (Note 15) The control unit, The system estimates the driver's emotions and adjusts the control method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The control unit, During control, the optimal control method is selected by referring to the driver's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The control unit, During control, the control algorithm is dynamically adjusted based on the vehicle's current state and driving conditions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The control unit, The system estimates the driver's emotions and determines control priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, During control, the optimal control method is selected by considering the driver's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, During control, the system analyzes the driver's social media activity and proposes relevant control methods. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, The system estimates the driver's emotions and adjusts the monitoring accuracy based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, During monitoring, the driver's past health data is referenced to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, During monitoring, the monitoring algorithm is dynamically adjusted based on the vehicle's current state and driving conditions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, The system estimates the driver's emotions and prioritizes monitoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, During monitoring, the optimal monitoring method is selected considering the driver's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, we analyze the driver's social media activity and propose relevant monitoring methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The response unit is It estimates the driver's emotions and adjusts the response method based on the estimated emotions of the driver. The system described in Appendix 1, characterized by the features described herein. (Note 28) The response unit is When responding, the system selects the optimal response method by referring to the driver's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The response unit is During response, the response algorithm is dynamically adjusted based on the vehicle's current state and driving conditions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The response unit is The system estimates the driver's emotions and prioritizes responses based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The response unit is When responding, the system selects the optimal response method considering the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The response unit is When responding, the system analyzes the driver's social media activity and suggests appropriate response methods. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned emergency call unit, The system estimates the driver's emotions and adjusts the emergency call method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned emergency call unit, When an emergency call is made, the system will refer to the driver's past emergency call history to select the most appropriate method of reporting. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned emergency call unit, During an emergency call, the notification algorithm is dynamically adjusted based on the vehicle's current status and driving conditions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned emergency call unit, The system estimates the driver's emotions and prioritizes emergency calls based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned emergency call unit, When an emergency call is made, the system selects the most appropriate reporting method by considering the driver's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned emergency call unit, When an emergency call is made, the system analyzes the driver's social media activity and suggests appropriate methods for making the call. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 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 voice recognition unit that recognizes the driver's voice commands, A control unit controls the functions of the vehicle based on the voice commands recognized by the voice recognition unit, A monitoring unit that monitors the driver's condition and the external environment, A response unit that performs an automatic response based on the information monitored by the monitoring unit, It includes an emergency notification unit that automatically makes notifications and requests for support in emergencies. A system characterized by the following features.

2. The control unit, Controls the functions of navigation, air conditioning, audio, lighting, and driving functions. The system according to feature 1.

3. The control unit, The color of the lighting is changed to a color corresponding to the tone of the driver's voice command. The system according to feature 2.

4. The monitoring unit, It detects the intensity of sunlight, The response unit is The air conditioner settings will automatically adjust to maintain the interior temperature at 22 degrees Celsius. The system according to feature 1.

5. The monitoring unit, When entering a gravel road or unpaved road, the driver's heart rate is monitored. The response unit is The vehicle's driving mode is changed if the driver's heart rate rises to 100 bpm or higher. The system according to feature 1.

6. The monitoring unit, When the road is frozen, monitor the driver's health condition, The response unit is If the driver's health condition is such that their blood pressure is 140 / 90 mmHg or higher, they should be encouraged to take a break and guided to a safe location. The system according to feature 1.

7. The monitoring unit, The stress level of the driver is monitored when surrounding vehicles brake suddenly. The response unit is When the driver's stress level rises to 50 or higher, relaxing music is played. The system according to feature 1.

8. The monitoring unit, The stress level of the aforementioned driver is monitored when changing lanes on a highway. The response unit is Temporarily enable the vehicle's autonomous driving function when stress levels are high. The system according to feature 1.

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