system

Abstract

The system according to the embodiment aims to detect driver drowsiness and dangerous driving and take appropriate measures. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a conversation unit. The collection unit monitors the driver's face or driving behavior. The analysis unit analyzes the data collected by the collection unit. The notification unit notifies the driver when the analysis unit detects drowsiness or dangerous driving. The conversation unit converses with the driver notified by the notification unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of not being able to adequately detect driver drowsiness or dangerous driving in real time and respond appropriately.

[0005] The system according to the embodiment aims to detect driver drowsiness and dangerous driving and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a conversation unit. The collection unit monitors the driver's face or driving behavior. The analysis unit analyzes the data collected by the collection unit. The notification unit notifies the driver when the analysis unit detects drowsiness or dangerous driving. The conversation unit converses with the driver notified by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect driver drowsiness and dangerous driving and respond appropriately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A dashcam system according to an embodiment of the present invention detects driver drowsiness and reckless driving, and engages in conversations with the driver to encourage safe driving. This system monitors the driver's face and driving behavior using a camera, and AI analyzes this data. If the AI ​​detects drowsiness or reckless driving, it speaks to the driver via voice. For example, it provides messages such as, "You seem sleepy. Please take a break" or "You're going too fast. Please slow down." The AI ​​also converses with the driver to alert them and encourage safe driving. For example, it might say, "The next service area is 10 kilometers away. Would you like to take a break?" This mechanism enables early detection of driver drowsiness and reckless driving and prevents accidents. Furthermore, by adding sensors that monitor the driver's heart rate and breathing, identifying dangerous areas using GPS data, and linking with the vehicle's control system, drowsiness detection can be more accurately detected and safety improved. This allows the dashcam system to detect driver drowsiness and reckless driving early and prevent accidents.

[0029] A drive recorder system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a conversation unit. The collection unit monitors the driver's face or driving behavior. The driver's face includes, but is not limited to, for example, whether the eyes are open or closed, and the direction of the face. The driving behavior includes, but is not limited to, for example, steering and braking. The collection unit monitors the driver's face using, for example, a camera. The collection unit can also monitor the driving behavior using a sensor. The analysis unit analyzes the data collected by the collection unit. Examples of the analysis include, but are not limited to, face recognition technology and driving behavior analysis technology using AI. The analysis unit detects drowsiness by analyzing the frequency of the driver's eye opening and closing using AI. The analysis unit can also detect dangerous driving by analyzing abnormal patterns in driving behavior. The notification unit notifies the driver when drowsiness or dangerous driving is detected by the analysis unit. Examples of the notification include, but are not limited to, audio notification, visual notification, and vibration notification. The notification unit may, for example, issue a voice notification saying, "You look sleepy. Please take a break." The notification unit may also warn the driver using visual or vibration notifications. The conversation unit may converse with the driver notified by the notification unit. The conversation may include, for example, content to alert the driver and encourage safe driving, but is not limited to such examples. The conversation unit may, for example, say, "There are 10 kilometers left to the next service area. Would you like to take a break?" The conversation unit may also carry out appropriate conversation depending on the driver's condition. As a result, the drive recorder system according to the embodiment can detect driver drowsiness or dangerous driving at an early stage and prevent accidents.

[0030] The collection unit includes a sensor for monitoring the driver's heart rate or respiration. The collection unit monitors the driver's heart rate using, for example, a heart rate sensor. For example, the heart rate sensor is worn on the driver's wrist and measures the heart rate in real time. The collection unit can also monitor the driver's respiration using a respiration sensor. For example, the respiration sensor is worn on the driver's chest and measures the rhythm and depth of respiration. Furthermore, the collection unit can combine the heart rate sensor and the respiration sensor to comprehensively monitor the driver's physiological state. For example, the heart rate and respiration data can be combined to evaluate the driver's stress level and fatigue level. This allows the collection unit to more accurately detect drowsiness by monitoring the driver's physiological state in detail.

[0031] The analysis unit includes a unit that identifies dangerous areas using GPS data. The analysis unit identifies the driver's current location using GPS data, for example. For example, the GPS data is acquired from the driver's smartphone or a GPS module installed in the vehicle. The analysis unit can also identify dangerous areas based on past accident data and traffic information. For example, it identifies areas where many accidents have occurred in the past or areas with heavy traffic as dangerous areas. Furthermore, the analysis unit can analyze current traffic conditions based on real-time traffic information and identify dangerous areas. For example, it identifies areas where congestion or accidents are occurring as dangerous areas. By identifying dangerous areas, the analysis unit can thereby provide appropriate warnings to the driver.

[0032] The notification unit can notify the driver by voice. The notification unit notifies the driver by voice, for example, using a voice notification system. For example, the notification unit can notify the driver by voice, saying, "You seem sleepy. Please take a break." The notification unit can also warn the driver by voice, saying, "You are driving too fast. Please slow down." Furthermore, the notification unit can provide appropriate voice notifications depending on the driver's condition. For example, if the driver feels sleepy, the notification unit can notify the driver to take a break, and if the driver is driving recklessly, the notification unit can notify the driver to slow down. This makes it easier for the notification unit to draw the driver's attention by voice notifications.

[0033] The conversation unit can engage in conversation to alert the driver and encourage safe driving. For example, the conversation unit may say to the driver, "There are 10 kilometers to the next service area. Would you like to take a break?" The conversation unit can also provide the driver with a warning message such as, "You're going too fast. Please slow down." Furthermore, the conversation unit can also engage in appropriate conversation depending on the driver's condition. For example, if the driver is feeling drowsy, the conversation unit will encourage the driver to take a break, and if the driver is driving recklessly, the conversation unit will encourage the driver to slow down. In this way, the conversation unit promotes safe driving through conversation with the driver.

[0034] The analysis unit includes a control unit that adjusts the speed in cooperation with the vehicle's control system. The analysis unit, for example, works in cooperation with the vehicle's control system to adjust the speed according to the driver's driving behavior. For example, the analysis unit automatically reduces the vehicle's speed when the driver is driving recklessly. The analysis unit can also adjust the vehicle's speed to encourage safe driving when the driver is feeling drowsy. Furthermore, the analysis unit can also adjust the speed in cooperation with the vehicle's control system in accordance with the driver's driving style. For example, the analysis unit adjusts the vehicle's speed to encourage safe driving when the driver frequently brakes. In this way, the analysis unit prevents reckless driving by automatically adjusting the vehicle's speed.

[0035] The collection unit analyzes the driver's past driving history and selects the optimal monitoring method. The collection unit, for example, acquires the driver's past driving history from a database. For example, the collection unit analyzes the driver's past driving data and understands driving behavior in specific time periods or areas. The collection unit can also select the optimal monitoring method based on the past driving history. For example, the collection unit can identify time periods in which the driver felt drowsy in the past and focus monitoring on those time periods. The collection unit can also analyze the driver's past driving style and perform detailed monitoring if there are many instances of sudden braking or sudden acceleration. This allows the collection unit to select the optimal monitoring method based on the past driving history, enabling efficient data collection.

[0036] During monitoring, the collection unit filters the data based on the driver's current health condition and stress level. The collection unit, for example, monitors the driver's heart rate and breathing data in real time. For example, if the heart rate is high, the collection unit performs detailed monitoring to detect any abnormalities. The collection unit can also determine that shallow breathing indicates a high stress level and strengthen monitoring. Furthermore, if the driver's health condition is good, the collection unit can perform basic monitoring to check for any abnormalities. This enables the collection unit to collect data according to the driver's health condition and stress level.

[0037] During monitoring, the collection unit prioritizes the collection of highly relevant data, taking into account the driver's geographical location information. The collection unit identifies the driver's current location using, for example, GPS data. For example, when the driver is driving on a highway, the collection unit prioritizes the collection of data on speed and following distance. In addition, when the driver is driving in an urban area, the collection unit can also prioritize the collection of behavior data at traffic lights and intersections. Furthermore, when the driver is driving on a mountain road, the collection unit can also prioritize the collection of behavior data on curves. This allows the collection unit to prioritize the collection of highly relevant data based on the geographical location information, enabling more accurate monitoring.

[0038] During monitoring, the collection unit analyzes the driver's social media activities and collects related data. The collection unit, for example, analyzes the driver's social media activities. For example, if the driver posts on social media that he or she is tired, the collection unit may prioritize collecting heart rate and breathing data. In addition, if the driver posts on social media that he or she is feeling stressed, the collection unit may prioritize collecting facial expression data. Furthermore, if the driver posts on social media that he or she is relaxed, the collection unit may prioritize collecting driving behavior data. In this way, the collection unit can collect related data based on social media activities to more accurately understand the driver's condition.

[0039] During analysis, the analysis unit improves the accuracy of the analysis by referring to the driver's past driving data. The analysis unit, for example, acquires the driver's past driving data from a database. For example, the analysis unit compares and analyzes the current driving behavior based on the driver's past driving data. The analysis unit can also extract specific patterns from the driver's past driving data and reflect them in the analysis. Furthermore, the analysis unit can also detect abnormal driving behavior early by referring to the driver's past driving data. In this way, the analysis unit improves the accuracy of the analysis by referring to the past driving data.

[0040] During analysis, the analysis unit applies different analysis methods depending on the driver's driving style. The analysis unit analyzes, for example, the driver's driving style. For example, if the driver frequently brakes suddenly, the analysis unit may focus on braking behavior data in the analysis. Also, if the driver prefers high-speed driving, the analysis unit may focus on speed data in the analysis. Furthermore, if the driver drives carefully, the analysis unit may focus on following distance data in the analysis. In this way, the analysis unit can perform more accurate analysis by applying an analysis method according to the driving style.

[0041] During analysis, the analysis unit adjusts the accuracy of the analysis based on the driver's driving time zone. The analysis unit, for example, acquires the driver's driving time zone from a database. For example, the analysis unit may prioritize visibility data during nighttime driving. The analysis unit may also prioritize traffic volume data during daytime driving. Furthermore, the analysis unit may prioritize driver drowsiness data during early morning driving. This allows the analysis unit to adjust the accuracy of the analysis based on the driving time zone, enabling more accurate analysis.

[0042] During analysis, the analysis unit refers to literature related to the driver to improve the accuracy of the analysis. The analysis unit refers to, for example, research papers on the driver's driving style. For example, the analysis unit reflects the analysis based on research papers on the driver's driving style. The analysis unit can also refer to literature on the driver's emotions and driving behavior and reflect this in the analysis. Furthermore, the analysis unit can refer to literature on the driver's health state and driving performance and reflect this in the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature.

[0043] When providing notification, the notification unit selects the optimal notification method taking into account the driver's geographical location information. The notification unit identifies the driver's current location using, for example, GPS data. For example, the notification unit may prioritize audio notification when the driver is driving on a highway. Also, the notification unit may prioritize visual notification when the driver is driving in an urban area. Furthermore, the notification unit may prioritize vibration notification when the driver is driving on a mountain road. This allows the notification unit to select the optimal notification method based on the geographical location information, enabling more effective notification.

[0044] The notification unit analyzes the driver's social media activity at the time of notification and customizes the content of the notification. The notification unit analyzes, for example, the driver's social media activity. For example, if the driver posts on social media that he or she is tired, the notification unit can send a notification encouraging the driver to take a break. In addition, if the driver posts on social media that he or she is feeling stressed, the notification unit can also send a general notification if the driver posts on social media that he or she is relaxing. In this way, the notification unit can customize the content of the notification based on the social media activity, thereby enabling more appropriate notifications.

[0045] During a conversation, the conversation unit selects the most appropriate conversation content by referring to the driver's past conversation history. The conversation unit, for example, acquires the driver's past conversation history from a database. For example, the conversation unit selects the most appropriate conversation content based on the conversation content that the driver has preferred in the past. The conversation unit can also select effective conversation content from the driver's past conversation history. Furthermore, the conversation unit can also adjust the topic of the conversation by referring to the driver's past conversation history. In this way, the conversation unit can provide the most appropriate conversation content for the driver by referring to the past conversation history.

[0046] During a conversation, the conversation unit selects a conversation topic based on the driver's current driving situation. The conversation unit, for example, analyzes the driver's current driving situation in real time. For example, when the driver is driving on a highway, the conversation unit may use a conversation that encourages the driver to relax. Furthermore, when the driver is driving in an urban area, the conversation unit may use a conversation that calls attention. Furthermore, when the driver is driving on a mountain road, the conversation unit may use a conversation that encourages concentration. In this way, the conversation unit can select a conversation topic according to the driving situation, enabling more appropriate conversation.

[0047] The conversation unit selects the most appropriate conversation content during a conversation, taking into consideration the driver's geographical location information. The conversation unit, for example, identifies the driver's current location using GPS data. For example, if the driver is driving on a highway, the conversation unit may use conversation that encourages relaxation. If the driver is driving in an urban area, the conversation unit may also use conversation that calls attention. Furthermore, if the driver is driving on a mountain road, the conversation unit may use conversation that encourages concentration. As a result, the conversation unit can select the most appropriate conversation content based on the geographical location information, enabling more appropriate conversations.

[0048] The conversation unit analyzes the driver's social media activity during the conversation to customize the content of the conversation. The conversation unit analyzes, for example, the driver's social media activity. For example, if the driver posts on social media that he or she is tired, the conversation unit can send a conversation encouraging the driver to take a break. Also, if the driver posts on social media that he or she is feeling stressed, the conversation unit can send a conversation encouraging the driver to relax. Furthermore, if the driver posts on social media that he or she is relaxing, the conversation unit can send a general conversation. In this way, the conversation unit can customize the content of the conversation based on the social media activity, enabling more appropriate conversation.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The analysis unit can also provide individualized driving advice based on the driver's driving style. For example, if the driver frequently brakes suddenly, the analysis unit can provide advice on when and how to brake. If the driver prefers high-speed driving, the analysis unit can provide advice on adjusting speed and maintaining a safe following distance. Furthermore, if the driver drives cautiously, the analysis unit can provide advice on appropriate timing for accelerating and decelerating. In this way, the analysis unit can promote safe driving by providing specific advice according to the driver's driving style.

[0051] The collection unit can also add a blood pressure sensor to monitor the driver's health condition. For example, the blood pressure sensor is attached to the driver's arm and measures blood pressure in real time. The collection unit can also monitor the driver's blood glucose level using a blood glucose sensor. For example, the blood glucose sensor is attached to the driver's fingertip and measures blood glucose level in real time. Furthermore, the collection unit can comprehensively analyze this health data and evaluate the driver's health condition. This allows the collection unit to more accurately detect drowsiness and fatigue by monitoring the driver's health condition in detail.

[0052] The analysis unit can also calculate an individual driving score based on the driver's driving history. For example, the analysis unit can analyze the driver's past driving data and score the degree of safe driving. The analysis unit can also provide feedback to the driver based on the driving score. For example, if the driving score is low, the analysis unit can provide advice for safe driving, and if the driving score is high, the analysis unit can provide a message of praise. Furthermore, the analysis unit can periodically update the driving score and support the driver in improving their driving skills. In this way, the analysis unit can promote safe driving by calculating an individual driving score based on the driver's driving history and providing feedback.

[0053] The notification unit can also combine different notification methods depending on the driver's driving situation. For example, when the driver is driving on a highway, a warning can be given by combining audio and visual notifications. When the driver is driving in an urban area, a warning can be given by combining audio and vibration notifications. Furthermore, when the driver is driving on a mountain road, a warning can be given by combining visual and vibration notifications. In this way, the notification unit can select the optimal notification method depending on the driving situation and effectively attract the driver's attention.

[0054] The conversation unit can also provide individualized driving advice based on the driver's driving history. For example, if the driver has frequently braked suddenly in the past, the conversation unit can provide advice on when and how to brake. If the driver has previously preferred high-speed driving, the conversation unit can provide advice on adjusting speed and maintaining a safe following distance. Furthermore, if the driver has previously driven cautiously, the conversation unit can provide advice on appropriate timing for accelerating and decelerating. In this way, the conversation unit can promote safe driving by providing specific advice based on the driver's driving history.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The collection unit monitors the driver's face or driving behavior. The driver's face includes the opening and closing of the eyes and the direction of the face, and the driving behavior includes the steering operation and braking operation. The collection unit can also monitor the driver's face using a camera and the driving behavior using a sensor. Step 2: The analysis unit analyzes the data collected by the collection unit. This analysis includes AI-based facial recognition technology and driving behavior analysis technology. For example, the analysis unit uses AI to analyze the frequency with which a driver opens and closes their eyes to detect drowsiness. It can also analyze abnormal patterns in driving behavior to detect dangerous driving. Step 3: The notification unit notifies the driver when the analysis unit detects drowsiness or dangerous driving. Notifications include audio, visual, and vibration notifications. For example, the notification unit may issue a voice notification saying, "You look sleepy. Please take a break." It can also warn the driver using visual and vibration notifications. Step 4: The conversation unit converses with the driver who has been notified by the notification unit. The conversation includes content to attract the driver's attention and encourage safe driving. For example, the conversation unit may say, "The next service area is 10 kilometers away. Would you like to take a break?" It can also have appropriate conversations depending on the driver's condition.

[0057] (Example 2) A dashcam system according to an embodiment of the present invention detects driver drowsiness and reckless driving, and engages in conversations with the driver to encourage safe driving. This system monitors the driver's face and driving behavior using a camera, and AI analyzes this data. If the AI ​​detects drowsiness or reckless driving, it speaks to the driver via voice. For example, it provides messages such as, "You seem sleepy. Please take a break" or "You're going too fast. Please slow down." The AI ​​also converses with the driver to alert them and encourage safe driving. For example, it might say, "The next service area is 10 kilometers away. Would you like to take a break?" This mechanism enables early detection of driver drowsiness and reckless driving and prevents accidents. Furthermore, by adding sensors that monitor the driver's heart rate and breathing, identifying dangerous areas using GPS data, and linking with the vehicle's control system, drowsiness detection can be more accurately detected and safety improved. This allows the dashcam system to detect driver drowsiness and reckless driving early and prevent accidents.

[0058] A drive recorder system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a conversation unit. The collection unit monitors the driver's face or driving behavior. The driver's face includes, but is not limited to, for example, whether the eyes are open or closed, and the direction of the face. The driving behavior includes, but is not limited to, for example, steering and braking. The collection unit monitors the driver's face using, for example, a camera. The collection unit can also monitor the driving behavior using a sensor. The analysis unit analyzes the data collected by the collection unit. Examples of the analysis include, but are not limited to, face recognition technology and driving behavior analysis technology using AI. The analysis unit detects drowsiness by analyzing the frequency of the driver's eye opening and closing using AI. The analysis unit can also detect dangerous driving by analyzing abnormal patterns in driving behavior. The notification unit notifies the driver when drowsiness or dangerous driving is detected by the analysis unit. Examples of the notification include, but are not limited to, audio notification, visual notification, and vibration notification. The notification unit may, for example, issue a voice notification saying, "You look sleepy. Please take a break." The notification unit may also warn the driver using visual or vibration notifications. The conversation unit may converse with the driver notified by the notification unit. The conversation may include, for example, content to alert the driver and encourage safe driving, but is not limited to such examples. The conversation unit may, for example, say, "There are 10 kilometers left to the next service area. Would you like to take a break?" The conversation unit may also carry out appropriate conversation depending on the driver's condition. As a result, the drive recorder system according to the embodiment can detect driver drowsiness or dangerous driving at an early stage and prevent accidents.

[0059] The collection unit includes a sensor for monitoring the driver's heart rate or respiration. The collection unit monitors the driver's heart rate using, for example, a heart rate sensor. For example, the heart rate sensor is worn on the driver's wrist and measures the heart rate in real time. The collection unit can also monitor the driver's respiration using a respiration sensor. For example, the respiration sensor is worn on the driver's chest and measures the rhythm and depth of respiration. Furthermore, the collection unit can combine the heart rate sensor and the respiration sensor to comprehensively monitor the driver's physiological state. For example, the heart rate and respiration data can be combined to evaluate the driver's stress level and fatigue level. This allows the collection unit to more accurately detect drowsiness by monitoring the driver's physiological state in detail.

[0060] The analysis unit includes a unit that identifies dangerous areas using GPS data. The analysis unit identifies the driver's current location using GPS data, for example. For example, the GPS data is acquired from the driver's smartphone or a GPS module installed in the vehicle. The analysis unit can also identify dangerous areas based on past accident data and traffic information. For example, it identifies areas where many accidents have occurred in the past or areas with heavy traffic as dangerous areas. Furthermore, the analysis unit can analyze current traffic conditions based on real-time traffic information and identify dangerous areas. For example, it identifies areas where congestion or accidents are occurring as dangerous areas. By identifying dangerous areas, the analysis unit can thereby provide appropriate warnings to the driver.

[0061] The notification unit can notify the driver by voice. The notification unit notifies the driver by voice, for example, using a voice notification system. For example, the notification unit can notify the driver by voice, saying, "You seem sleepy. Please take a break." The notification unit can also warn the driver by voice, saying, "You are driving too fast. Please slow down." Furthermore, the notification unit can provide appropriate voice notifications depending on the driver's condition. For example, if the driver feels sleepy, the notification unit can notify the driver to take a break, and if the driver is driving recklessly, the notification unit can notify the driver to slow down. This makes it easier for the notification unit to draw the driver's attention by voice notifications.

[0062] The conversation unit can engage in conversation to alert the driver and encourage safe driving. For example, the conversation unit may say to the driver, "There are 10 kilometers to the next service area. Would you like to take a break?" The conversation unit can also provide the driver with a warning message such as, "You're going too fast. Please slow down." Furthermore, the conversation unit can also engage in appropriate conversation depending on the driver's condition. For example, if the driver is feeling drowsy, the conversation unit will encourage the driver to take a break, and if the driver is driving recklessly, the conversation unit will encourage the driver to slow down. In this way, the conversation unit promotes safe driving through conversation with the driver.

[0063] The analysis unit includes a control unit that adjusts the speed in cooperation with the vehicle's control system. The analysis unit, for example, works in cooperation with the vehicle's control system to adjust the speed according to the driver's driving behavior. For example, the analysis unit automatically reduces the vehicle's speed when the driver is driving recklessly. The analysis unit can also adjust the vehicle's speed to encourage safe driving when the driver is feeling drowsy. Furthermore, the analysis unit can also adjust the speed in cooperation with the vehicle's control system in accordance with the driver's driving style. For example, the analysis unit adjusts the vehicle's speed to encourage safe driving when the driver frequently brakes. In this way, the analysis unit prevents reckless driving by automatically adjusting the vehicle's speed.

[0064] The collection unit estimates the driver's emotions and adjusts the monitoring frequency based on the estimated driver's emotions. The collection unit estimates the driver's emotions using, for example, AI. For example, the collection unit analyzes the driver's facial expressions and voice data to estimate the driver's emotions. The collection unit can also estimate emotions by analyzing the driver's heart rate and breathing data. Furthermore, the collection unit adjusts the monitoring frequency based on the estimated driver's emotions. For example, if the driver is nervous, the monitoring frequency can be increased to collect detailed data. On the other hand, if the driver is relaxed, the monitoring frequency can be decreased to collect the minimum necessary data. In this way, the collection unit can adjust the monitoring frequency according to the driver's emotions, enabling more appropriate data collection.

[0065] The collection unit analyzes the driver's past driving history and selects the optimal monitoring method. The collection unit, for example, acquires the driver's past driving history from a database. For example, the collection unit analyzes the driver's past driving data and understands driving behavior in specific time periods or areas. The collection unit can also select the optimal monitoring method based on the past driving history. For example, the collection unit can identify time periods in which the driver felt drowsy in the past and focus monitoring on those time periods. The collection unit can also analyze the driver's past driving style and perform detailed monitoring if there are many instances of sudden braking or sudden acceleration. This allows the collection unit to select the optimal monitoring method based on the past driving history, enabling efficient data collection.

[0066] During monitoring, the collection unit filters the data based on the driver's current health condition and stress level. The collection unit, for example, monitors the driver's heart rate and breathing data in real time. For example, if the heart rate is high, the collection unit performs detailed monitoring to detect any abnormalities. The collection unit can also determine that shallow breathing indicates a high stress level and strengthen monitoring. Furthermore, if the driver's health condition is good, the collection unit can perform basic monitoring to check for any abnormalities. This enables the collection unit to collect data according to the driver's health condition and stress level.

[0067] The collection unit estimates the driver's emotions and determines the priority of data to be collected based on the estimated driver's emotions. The collection unit estimates the driver's emotions using, for example, AI. For example, the collection unit analyzes the driver's facial expressions and voice data to estimate the driver's emotions. The collection unit can also estimate emotions by analyzing the driver's heart rate and breathing data. Furthermore, the collection unit determines the priority of data to be collected based on the estimated driver's emotions. For example, if the driver is nervous, the collection unit prioritizes collection of heart rate and breathing data. Also, if the driver is relaxed, the collection unit can prioritize collection of driving behavior data. In this way, the collection unit can prioritize collection of important data by determining the priority of data to be collected according to the driver's emotions.

[0068] During monitoring, the collection unit prioritizes the collection of highly relevant data, taking into account the driver's geographical location information. The collection unit identifies the driver's current location using, for example, GPS data. For example, when the driver is driving on a highway, the collection unit prioritizes the collection of data on speed and following distance. In addition, when the driver is driving in an urban area, the collection unit can also prioritize the collection of behavior data at traffic lights and intersections. Furthermore, when the driver is driving on a mountain road, the collection unit can also prioritize the collection of behavior data on curves. This allows the collection unit to prioritize the collection of highly relevant data based on the geographical location information, enabling more accurate monitoring.

[0069] During monitoring, the collection unit analyzes the driver's social media activities and collects related data. The collection unit, for example, analyzes the driver's social media activities. For example, if the driver posts on social media that he or she is tired, the collection unit may prioritize collecting heart rate and breathing data. In addition, if the driver posts on social media that he or she is feeling stressed, the collection unit may prioritize collecting facial expression data. Furthermore, if the driver posts on social media that he or she is relaxed, the collection unit may prioritize collecting driving behavior data. In this way, the collection unit can collect related data based on social media activities to more accurately understand the driver's condition.

[0070] The analysis unit estimates the driver's emotions and adjusts the analysis algorithm based on the estimated driver's emotions. The analysis unit estimates the driver's emotions using, for example, AI. For example, the analysis unit may analyze the driver's facial expressions and voice data to estimate the driver's emotions. The analysis unit may also estimate emotions by analyzing the driver's heart rate and breathing data. Furthermore, the analysis unit adjusts the analysis algorithm based on the estimated driver's emotions. For example, if the driver is nervous, the analysis may place emphasis on heart rate and breathing data. Alternatively, if the driver is relaxed, the analysis may place emphasis on driving behavior data. This allows the analysis unit to adjust the analysis algorithm according to the driver's emotions, enabling more accurate analysis.

[0071] During analysis, the analysis unit improves the accuracy of the analysis by referring to the driver's past driving data. The analysis unit, for example, acquires the driver's past driving data from a database. For example, the analysis unit compares and analyzes the current driving behavior based on the driver's past driving data. The analysis unit can also extract specific patterns from the driver's past driving data and reflect them in the analysis. Furthermore, the analysis unit can also detect abnormal driving behavior early by referring to the driver's past driving data. In this way, the analysis unit improves the accuracy of the analysis by referring to the past driving data.

[0072] During analysis, the analysis unit applies different analysis methods depending on the driver's driving style. The analysis unit analyzes, for example, the driver's driving style. For example, if the driver frequently brakes suddenly, the analysis unit may focus on braking behavior data in the analysis. Also, if the driver prefers high-speed driving, the analysis unit may focus on speed data in the analysis. Furthermore, if the driver drives carefully, the analysis unit may focus on following distance data in the analysis. In this way, the analysis unit can perform more accurate analysis by applying an analysis method according to the driving style.

[0073] The analysis unit estimates the driver's emotions and determines the priority of analysis based on the estimated driver's emotions. The analysis unit estimates the driver's emotions using, for example, AI. For example, the analysis unit analyzes the driver's facial expressions and voice data to estimate the driver's emotions. The analysis unit can also estimate emotions by analyzing the driver's heart rate and breathing data. Furthermore, the analysis unit determines the priority of analysis based on the estimated driver's emotions. For example, if the driver is nervous, the analysis unit can prioritize analysis of heart rate and breathing data. Also, if the driver is relaxed, the analysis unit can prioritize analysis of driving behavior data. In this way, the analysis unit can prioritize analysis of important data by determining the priority of analysis based on the driver's emotions.

[0074] During analysis, the analysis unit adjusts the accuracy of the analysis based on the driver's driving time zone. The analysis unit, for example, acquires the driver's driving time zone from a database. For example, the analysis unit may prioritize visibility data during nighttime driving. The analysis unit may also prioritize traffic volume data during daytime driving. Furthermore, the analysis unit may prioritize driver drowsiness data during early morning driving. This allows the analysis unit to adjust the accuracy of the analysis based on the driving time zone, enabling more accurate analysis.

[0075] During analysis, the analysis unit refers to literature related to the driver to improve the accuracy of the analysis. The analysis unit refers to, for example, research papers on the driver's driving style. For example, the analysis unit reflects the analysis based on research papers on the driver's driving style. The analysis unit can also refer to literature on the driver's emotions and driving behavior and reflect this in the analysis. Furthermore, the analysis unit can refer to literature on the driver's health state and driving performance and reflect this in the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature.

[0076] The notification unit estimates the driver's emotions and adjusts the notification expression method based on the estimated driver's emotions. The notification unit estimates the driver's emotions using, for example, AI. For example, the notification unit analyzes the driver's facial expressions and voice data to estimate the driver's emotions. The notification unit can also estimate emotions by analyzing the driver's heart rate and breathing data. Furthermore, the notification unit adjusts the notification expression method based on the estimated driver's emotions. For example, if the driver is nervous, the notification can be made in a calm voice. On the other hand, if the driver is relaxed, the notification can be made in a bright voice. In this way, the notification unit can adjust the notification expression method according to the driver's emotions, enabling more effective notifications.

[0077] When providing notification, the notification unit selects the optimal notification method taking into account the driver's geographical location information. The notification unit identifies the driver's current location using, for example, GPS data. For example, the notification unit may prioritize audio notification when the driver is driving on a highway. Also, the notification unit may prioritize visual notification when the driver is driving in an urban area. Furthermore, the notification unit may prioritize vibration notification when the driver is driving on a mountain road. This allows the notification unit to select the optimal notification method based on the geographical location information, enabling more effective notification.

[0078] The notification unit analyzes the driver's social media activity at the time of notification and customizes the content of the notification. The notification unit analyzes, for example, the driver's social media activity. For example, if the driver posts on social media that he or she is tired, the notification unit can send a notification encouraging the driver to take a break. In addition, if the driver posts on social media that he or she is feeling stressed, the notification unit can also send a general notification if the driver posts on social media that he or she is relaxing. In this way, the notification unit can customize the content of the notification based on the social media activity, thereby enabling more appropriate notifications.

[0079] The conversation unit estimates the driver's emotions and adjusts the content of the conversation based on the estimated driver's emotions. The conversation unit estimates the driver's emotions using, for example, AI. For example, the conversation unit analyzes the driver's facial expressions and voice data to estimate the driver's emotions. The conversation unit can also estimate emotions by analyzing the driver's heart rate and breathing data. Furthermore, the conversation unit adjusts the content of the conversation based on the estimated driver's emotions. For example, if the driver is nervous, the conversation unit can encourage the driver to relax. Also, if the driver is relaxed, the conversation unit can engage in general conversation. This allows the conversation unit to adjust the content of the conversation according to the driver's emotions, enabling more effective conversation.

[0080] During a conversation, the conversation unit selects the most appropriate conversation content by referring to the driver's past conversation history. The conversation unit, for example, acquires the driver's past conversation history from a database. For example, the conversation unit selects the most appropriate conversation content based on the conversation content that the driver has preferred in the past. The conversation unit can also select effective conversation content from the driver's past conversation history. Furthermore, the conversation unit can also adjust the topic of the conversation by referring to the driver's past conversation history. In this way, the conversation unit can provide the most appropriate conversation content for the driver by referring to the past conversation history.

[0081] During a conversation, the conversation unit selects a conversation topic based on the driver's current driving situation. The conversation unit, for example, analyzes the driver's current driving situation in real time. For example, when the driver is driving on a highway, the conversation unit may use a conversation that encourages the driver to relax. Furthermore, when the driver is driving in an urban area, the conversation unit may use a conversation that calls attention. Furthermore, when the driver is driving on a mountain road, the conversation unit may use a conversation that encourages concentration. In this way, the conversation unit can select a conversation topic according to the driving situation, enabling more appropriate conversation.

[0082] The conversation unit estimates the driver's emotions and determines the priority of conversations based on the estimated driver's emotions. The conversation unit estimates the driver's emotions using, for example, AI. For example, the conversation unit analyzes the driver's facial expressions and voice data to estimate the driver's emotions. The conversation unit can also estimate emotions by analyzing the driver's heart rate and breathing data. Furthermore, the conversation unit determines the priority of conversations based on the estimated driver's emotions. For example, if the driver is nervous, the conversation unit can prioritize conversations that encourage relaxation. Also, if the driver is relaxed, the conversation unit can prioritize general conversations. In this way, the conversation unit can prioritize important conversations by determining the priority of conversations based on the driver's emotions.

[0083] The conversation unit selects the most appropriate conversation content during a conversation, taking into consideration the driver's geographical location information. The conversation unit, for example, identifies the driver's current location using GPS data. For example, if the driver is driving on a highway, the conversation unit may use conversation that encourages relaxation. If the driver is driving in an urban area, the conversation unit may also use conversation that calls attention. Furthermore, if the driver is driving on a mountain road, the conversation unit may use conversation that encourages concentration. As a result, the conversation unit can select the most appropriate conversation content based on the geographical location information, enabling more appropriate conversations.

[0084] The conversation unit analyzes the driver's social media activity during the conversation to customize the content of the conversation. The conversation unit analyzes, for example, the driver's social media activity. For example, if the driver posts on social media that he or she is tired, the conversation unit can send a conversation encouraging the driver to take a break. Also, if the driver posts on social media that he or she is feeling stressed, the conversation unit can send a conversation encouraging the driver to relax. Furthermore, if the driver posts on social media that he or she is relaxing, the conversation unit can send a general conversation. In this way, the conversation unit can customize the content of the conversation based on the social media activity, enabling more appropriate conversation. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and conversation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit monitors the driver's face and driving behavior using the camera 42 and sensors of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12 and detects the driver's drowsiness and dangerous driving. The notification unit provides audio notification using the output device 40 of the smart device 14. The conversation unit converses with the driver via the control unit 46A of the smart device 14 to encourage safe driving. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, and conversation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit monitors the driver's face and driving behavior using the camera 42 and sensors of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12 and detects the driver's drowsiness and dangerous driving. The notification unit provides audio notifications using the speaker 240 of the smart glasses 214. The conversation unit converses with the driver via the control unit 46A of the smart glasses 214 to encourage safe driving. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and conversation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit monitors the driver's face and driving behavior using the camera 42 and sensors of the headset-type terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12 and detects the driver's drowsiness and dangerous driving. The notification unit provides audio notification using the speaker 240 of the headset-type terminal 314. The conversation unit converses with the driver via the control unit 46A of the headset-type terminal 314 to encourage safe driving. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and conversation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit monitors the driver's face and driving behavior using the camera 42 and sensors of the robot 414. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and detects the driver's drowsiness and dangerous driving. The notification unit issues audio notifications using the speaker 240 of the robot 414. The conversation unit converses with the driver via the control unit 46A of the robot 414 to encourage safe driving.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The analysis unit can also provide individualized driving advice based on the driver's driving style. For example, if the driver frequently brakes suddenly, the analysis unit can provide advice on when and how to brake. If the driver prefers high-speed driving, the analysis unit can provide advice on adjusting speed and maintaining a safe following distance. Furthermore, if the driver drives cautiously, the analysis unit can provide advice on appropriate timing for accelerating and decelerating. In this way, the analysis unit can promote safe driving by providing specific advice according to the driver's driving style.

[0087] The collection unit can also add a blood pressure sensor to monitor the driver's health condition. For example, the blood pressure sensor is attached to the driver's arm and measures blood pressure in real time. The collection unit can also monitor the driver's blood glucose level using a blood glucose sensor. For example, the blood glucose sensor is attached to the driver's fingertip and measures blood glucose level in real time. Furthermore, the collection unit can comprehensively analyze this health data and evaluate the driver's health condition. This allows the collection unit to more accurately detect drowsiness and fatigue by monitoring the driver's health condition in detail.

[0088] The analysis unit can also calculate an individual driving score based on the driver's driving history. For example, the analysis unit can analyze the driver's past driving data and score the degree of safe driving. The analysis unit can also provide feedback to the driver based on the driving score. For example, if the driving score is low, the analysis unit can provide advice for safe driving, and if the driving score is high, the analysis unit can provide a message of praise. Furthermore, the analysis unit can periodically update the driving score and support the driver in improving their driving skills. In this way, the analysis unit can promote safe driving by calculating an individual driving score based on the driver's driving history and providing feedback.

[0089] The notification unit can also combine different notification methods depending on the driver's driving situation. For example, when the driver is driving on a highway, a warning can be given by combining audio and visual notifications. When the driver is driving in an urban area, a warning can be given by combining audio and vibration notifications. Furthermore, when the driver is driving on a mountain road, a warning can be given by combining visual and vibration notifications. In this way, the notification unit can select the optimal notification method depending on the driving situation and effectively attract the driver's attention.

[0090] The conversation unit can also provide individualized driving advice based on the driver's driving history. For example, if the driver has frequently braked suddenly in the past, the conversation unit can provide advice on when and how to brake. If the driver has previously preferred high-speed driving, the conversation unit can provide advice on adjusting speed and maintaining a safe following distance. Furthermore, if the driver has previously driven cautiously, the conversation unit can provide advice on appropriate timing for accelerating and decelerating. In this way, the conversation unit can promote safe driving by providing specific advice based on the driver's driving history.

[0091] The analysis unit can also estimate the driver's emotions and provide driving advice based on the estimated driver's emotions. For example, if the driver is nervous, the analysis unit can provide advice encouraging relaxation. Also, if the driver is relaxed, the analysis unit can provide advice encouraging concentration. Furthermore, if the driver is feeling stressed, the analysis unit can provide advice to reduce stress. In this way, the analysis unit can more effectively promote safe driving by providing driving advice according to the driver's emotions.

[0092] The collection unit can also estimate the driver's emotions and provide driving advice based on the estimated driver's emotions. For example, if the driver is nervous, the collection unit can provide advice encouraging relaxation. Also, if the driver is relaxed, the collection unit can provide advice encouraging concentration. Furthermore, if the driver is feeling stressed, the collection unit can provide advice to reduce stress. In this way, the collection unit can more effectively promote safe driving by providing driving advice according to the driver's emotions.

[0093] The notification unit can also estimate the driver's emotions and adjust the timing of the notification based on the estimated driver's emotions. For example, if the driver is nervous, the timing of the notification can be delayed to encourage relaxation. Also, if the driver is relaxed, the timing of the notification can be advanced to encourage concentration. Furthermore, if the driver is feeling stressed, the timing of the notification can be adjusted to reduce stress. In this way, the notification unit can more effectively attract the driver's attention by adjusting the timing of the notification according to the driver's emotions.

[0094] The conversation unit can also estimate the driver's emotions and adjust the tone of the conversation based on the estimated driver's emotions. For example, if the driver is nervous, the conversation can be in a calm tone. If the driver is relaxed, the conversation can be in a bright tone. Furthermore, if the driver is feeling stressed, the conversation can be in a gentle tone. In this way, the conversation unit can communicate more effectively by adjusting the tone of the conversation according to the driver's emotions.

[0095] The analysis unit can also estimate the driver's emotions and adjust the content of driving advice based on the estimated driver's emotions. For example, if the driver is nervous, the analysis unit can provide advice encouraging relaxation. If the driver is relaxed, the analysis unit can provide advice encouraging concentration. Furthermore, if the driver is feeling stressed, the analysis unit can provide advice to reduce stress. In this way, the analysis unit can more effectively promote safe driving by providing driving advice according to the driver's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The collection unit monitors the driver's face or driving behavior. The driver's face includes the opening and closing of the eyes and the direction of the face, and the driving behavior includes the steering operation and braking operation. The collection unit can also monitor the driver's face using a camera and the driving behavior using a sensor. Step 2: The analysis unit analyzes the data collected by the collection unit. This analysis includes AI-based facial recognition technology and driving behavior analysis technology. For example, the analysis unit uses AI to analyze the frequency with which a driver opens and closes their eyes to detect drowsiness. It can also analyze abnormal patterns in driving behavior to detect dangerous driving. Step 3: The notification unit notifies the driver when the analysis unit detects drowsiness or dangerous driving. Notifications include audio, visual, and vibration notifications. For example, the notification unit may issue a voice notification saying, "You look sleepy. Please take a break." It can also warn the driver using visual and vibration notifications. Step 4: The conversation unit converses with the driver who has been notified by the notification unit. The conversation includes content to attract the driver's attention and encourage safe driving. For example, the conversation unit may say, "The next service area is 10 kilometers away. Would you like to take a break?" It can also have appropriate conversations depending on the driver's condition.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0169] [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that monitors the driver's face or driving behavior; an analysis unit that analyzes the data collected by the collection unit; a notification unit that notifies a driver when drowsiness or dangerous driving is detected by the analysis unit; a conversation unit that converses with the driver notified by the notification unit; Equipped with A system characterized by:

2. The collecting unit Equipped with sensors to monitor the driver's heart rate or breathing The system of claim 1 .

3. The analysis unit Equipped with a section that uses GPS data to identify dangerous areas The system of claim 1 .

4. The notification unit Notify the driver by voice The system of claim 1 .

5. The conversation unit is Communicate with drivers to keep them alert and encourage safe driving The system of claim 1 .

6. The analysis unit Equipped with a control unit that adjusts speed in cooperation with the vehicle's control system The system of claim 1 .

7. The collecting unit Estimate the driver's emotions and adjust the monitoring frequency based on the estimated driver's emotions. The system of claim 1 .

8. The collecting unit Analyze the driver's past driving history and select the most appropriate monitoring method The system of claim 1 .

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