System

Abstract

An object of a system according to an embodiment is to evaluate the degree of fatigue of a driver and appropriately determine whether or not driving is possible.SOLUTION: A system according to an embodiment includes a fatigue degree evaluation unit and an operation determination unit. The tiredness evaluation unit evaluates the tiredness of the driver using the generated AI. The operation determination unit determines whether or not the operation is possible based on the fatigue degree evaluated by the fatigue degree evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately assess a driver's fatigue level and appropriately determine whether or not the vehicle can continue to operate.

[0005] The system according to the embodiment aims to evaluate the driver's fatigue level and appropriately determine whether or not the vehicle can be driven. [Means for solving the problem]

[0006] The system according to the embodiment includes a fatigue level assessment unit and a driving determination unit. The fatigue level assessment unit assesses the driver's fatigue level using a generation AI. The driving determination unit determines whether or not driving is possible based on the fatigue level assessed by the fatigue level assessment unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the driver's fatigue level and appropriately determine whether or not the vehicle can be driven. [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) The fatigue assessment system according to the embodiment of the present invention uses a generation AI to assess the driver's fatigue level and determines whether or not to operate the vehicle based on the assessment results. This ensures the safety of transportation companies and reduces the risk of accidents.

[0029] A fatigue assessment system according to an embodiment includes a fatigue assessment unit and an operation determination unit. The fatigue assessment unit uses a generation AI to assess the driver's fatigue level. For example, the fatigue assessment unit collects data such as the driver's heart rate, body temperature, eye movement, and reaction time, and the generation AI analyzes this data. The fatigue assessment unit can also input the driver's past driving history data into the generation AI and predict the driver's current fatigue level based on past fatigue patterns. Furthermore, the fatigue assessment unit can also estimate the driver's fatigue level from the driver's tone of voice and speaking style by analyzing voice data to evaluate the driver's psychological state. The operation determination unit determines whether or not to operate the vehicle based on the fatigue level assessed by the fatigue assessment unit. For example, the operation determination unit instructs the driver to stop operation and take a break if the driver's fatigue level is high. The operation determination unit can also determine whether or not to operate the vehicle by taking into account external data such as weather and traffic conditions in addition to the fatigue level assessed by the generation AI. Furthermore, the operation determination unit can determine whether or not to operate the vehicle by taking into account the driver's individual health condition (e.g., chronic illnesses and allergies) through the generation AI. As a result, the fatigue assessment system according to the embodiment can accurately assess the driver's fatigue level and appropriately determine whether or not the vehicle can be driven. For example, fatigue caused by long hours of driving can be prevented, reducing the risk of traffic accidents. Furthermore, since the system is useful for managing the health of drivers, it is expected to greatly contribute to ensuring the safety of transportation businesses.

[0030] The fatigue assessment unit collects data on the driver's heart rate, body temperature, eye movement, and reaction time, and the generation AI can analyze this data. The fatigue assessment unit, for example, uses a wearable device to measure the driver's heart rate. For example, a smartwatch or heart rate monitor is worn to collect heart rate data in real time. A thermometer or wearable device is also used to measure body temperature. For example, a thermometer is used to measure body temperature regularly and collect data. Furthermore, an eye-tracking device is used to measure eye movement. For example, a camera is installed inside the vehicle to track the driver's eye movement in real time. A reaction test is conducted to measure reaction time. For example, a simple reaction test is conducted regularly to measure the driver's reaction time. In this way, the driver's biometric data can be collected and analyzed by the generation AI, allowing for an accurate assessment of fatigue level.

[0031] The operation judgment unit can stop operation and instruct the driver to take a rest if the level of fatigue is high. For example, the operation judgment unit instructs the driver to stop operation if the level of fatigue is high. For example, if the level of fatigue assessed by the generation AI exceeds a certain threshold, the operation judgment unit stops operation and instructs the driver to take a rest. The operation judgment unit also gives specific instructions for taking a rest. For example, it suggests the length of the rest period and the location of the rest. Furthermore, the operation judgment unit instructs the timing for resuming operation after the break. For example, it instructs the driver to resume operation after a certain rest period has elapsed. In this way, the risk of an accident can be reduced by stopping operation and taking a rest when the level of fatigue is high.

[0032] The fatigue assessment unit inputs the driver's past driving history data into the generation AI and can predict the current level of fatigue based on past fatigue patterns. The fatigue assessment unit, for example, collects the driver's past driving history data and inputs it into the generation AI. For example, it analyzes fatigue patterns based on data such as past driving time, rest times, and driving routes. The generation AI also learns the past driving history data and predicts fatigue levels by comparing it with the current driving situation. For example, it evaluates the current level of fatigue based on fatigue patterns from past long driving sessions. Furthermore, the generation AI predicts fluctuations in the driver's fatigue level based on the past driving history data. For example, it identifies the timing when fatigue levels increase from past data and evaluates fatigue levels according to the current driving situation. This enables more accurate fatigue assessment by predicting current fatigue levels based on past driving history data.

[0033] The fatigue assessment unit can analyze the driver's voice data and estimate the level of fatigue from the tone of voice and speaking style. For example, the fatigue assessment unit collects the driver's voice data, and the generation AI analyzes the tone of voice and speaking style. For example, it records conversations or monologues while driving and evaluates the level of fatigue based on changes in the voice. The generation AI also analyzes the voice data and detects changes in the tone of voice and speaking style. For example, it determines that the level of fatigue is high if the voice becomes lower or the speaking style becomes slower. Furthermore, the generation AI evaluates the driver's psychological state based on the driver's voice data. For example, it detects signs of stress and fatigue based on changes in the tone of voice and speaking style. This makes it possible to analyze the voice data and estimate the level of fatigue from the tone of voice and speaking style, thereby evaluating the psychological state.

[0034] The fatigue assessment unit collects the driver's dietary data and sleep data, and the generation AI can evaluate the driver's fatigue level based on this data. The fatigue assessment unit, for example, collects the driver's dietary data, and the generation AI analyzes it. For example, it records the content and time of meals and evaluates the level of fatigue based on nutritional balance and meal timing. The generation AI also collects the driver's sleep data, and analyzes it. For example, it records the amount of sleep and sleep quality and evaluates the level of fatigue based on sleep deprivation and irregular sleep patterns. Furthermore, the generation AI analyzes the dietary and sleep data comprehensively and evaluates the driver's fatigue level. For example, it predicts fluctuations in fatigue level based on the balance between diet and sleep. This allows for a more accurate assessment of fatigue level by evaluating the level of fatigue based on dietary and sleep data.

[0035] The fatigue assessment unit analyzes the music selection and radio content made by the driver while driving, and the generation AI can estimate the level of fatigue from this information. For example, the fatigue assessment unit collects data on the music selection made by the driver while driving, and the generation AI analyzes this data. For example, the fatigue level of the driver is assessed based on the music genre and tempo. The content of the radio the driver listens to while driving is also analyzed, and the generation AI estimates the level of fatigue based on this data. For example, the content of news and talk shows is analyzed to assess the driver's interest and concentration. Furthermore, the generation AI comprehensively analyzes the music selection and radio content to assess the driver's level of fatigue. For example, fluctuations in fatigue level are predicted based on changes in music and radio content. This makes it possible to estimate the driver's level of fatigue by analyzing the music selection and radio content.

[0036] The operation decision unit can determine whether or not to operate the vehicle based on the fatigue level assessed by the generation AI as well as external data on weather and traffic conditions. For example, the generation AI collects weather data in addition to driver fatigue level data to determine whether or not to operate the vehicle. For example, in bad weather, the operation may be canceled even if the fatigue level is low. The generation AI also collects traffic condition data and determines whether or not to operate the vehicle in conjunction with the driver's fatigue level. For example, if there is traffic congestion, the operation may be canceled even if the fatigue level is low. Furthermore, the generation AI performs an integrated analysis of external data such as weather and traffic conditions to determine whether or not to operate the vehicle. For example, it takes multiple external factors into consideration to make optimal operation decisions. This makes it possible to make safer operation decisions by taking external data into consideration.

[0037] The operation decision unit allows the generation AI to determine whether or not to operate the vehicle based on the individual health condition of the driver. For example, the operation decision unit allows the generation AI to collect health data on the driver and determine whether or not to operate the vehicle taking into account the individual health condition. For example, if the driver has a chronic illness, the operation will be suspended even if the fatigue level is low. The generation AI also collects allergy data on the driver and determines whether or not to operate the vehicle based on this. For example, if the driver has an allergic reaction, the operation will be suspended. Furthermore, the generation AI performs an integrated analysis of the individual health condition and determines whether or not to operate the vehicle. For example, it takes multiple health factors into account to make the optimal operation decision. This allows for safer operation decisions by taking into account the individual health condition.

[0038] In the operation judgment unit, the generation AI can change the operation route or suggest rest points based on the driver's fatigue level. In the operation judgment unit, for example, the generation AI suggests the optimal operation route based on the driver's fatigue level data. For example, if the fatigue level is high, a shorter route will be selected. The generation AI also suggests appropriate rest points based on the driver's fatigue level data. For example, if the fatigue level is high, a nearby rest area will be suggested. Furthermore, the generation AI changes the operation route or suggests rest points based on the driver's fatigue level data. For example, if the fatigue level is high, a safe route will be selected and appropriate rest will be instructed. In this way, by changing the operation route or suggesting rest points based on the fatigue level, safer operation is possible.

[0039] The operation judgment unit allows the generation AI to adjust the operation schedule based on the driver's fatigue level and propose a reasonable operation plan. In the operation judgment unit, for example, the generation AI adjusts the operation schedule based on the driver's fatigue level data. For example, if the fatigue level is high, the operation time is shortened. The generation AI also proposes a reasonable operation plan based on the driver's fatigue level data. For example, if the fatigue level is high, it suggests taking more breaks. Furthermore, the generation AI adjusts the operation schedule based on the driver's fatigue level data and proposes an optimal operation plan. For example, if the fatigue level is high, it may instruct the driver to stop operation and take a break. In this way, by adjusting the operation schedule based on the fatigue level, a reasonable operation plan is possible.

[0040] The fatigue assessment unit uses a device that measures the driver's electrodermal activity, which the generation AI analyzes to assess the level of fatigue. The fatigue assessment unit, for example, has the driver wear a device that measures electrodermal activity and collects data in real time. For example, an electrodermal activity sensor is used to detect signs of stress and fatigue. The generation AI then analyzes the electrodermal activity data to assess the level of fatigue. For example, it predicts the driver's level of fatigue based on fluctuations in electrodermal activity. The generation AI then assesses the driver's level of fatigue based on the electrodermal activity data. For example, if an increase in electrodermal activity is observed, it is determined that the level of fatigue is high. In this way, by measuring electrodermal activity and having the generation AI analyze it, the level of fatigue can be accurately assessed.

[0041] The fatigue assessment unit uses a device to measure the driver's breathing pattern, which the generation AI analyzes to assess the level of fatigue. The fatigue assessment unit, for example, has the driver wear a device that measures their breathing pattern and collects data in real time. For example, a breathing sensor is used to measure the rhythm and depth of breathing. The generation AI then analyzes the breathing pattern data to assess the level of fatigue. For example, if breathing becomes shallow or irregular, it is determined that the level of fatigue is high. Furthermore, the generation AI assesses the driver's level of fatigue based on the breathing pattern data. For example, signs of stress and fatigue can be detected based on fluctuations in breathing. In this way, the breathing pattern can be measured and analyzed by the generation AI to accurately assess the level of fatigue.

[0042] The fatigue assessment unit uses a device to measure the driver's brain waves, which the generation AI analyzes to assess the level of fatigue. The fatigue assessment unit, for example, has the driver wear a device to measure brain waves and collect data in real time. For example, it uses an EEG sensor to measure brain activity. The generation AI then analyzes the EEG data to assess the level of fatigue. For example, it predicts the driver's level of fatigue based on fluctuations in brain waves. The generation AI then assesses the driver's level of fatigue based on the EEG data. For example, if a specific pattern of brain waves is observed, it may determine that the level of fatigue is high. In this way, by measuring brain waves and having the generation AI analyze them, the level of fatigue can be accurately assessed.

[0043] The fatigue evaluation unit uses a device to measure the driver's electromyogram, which the generation AI analyzes to assess the level of fatigue. The fatigue evaluation unit, for example, has the driver wear a device to measure the electromyogram and collect data in real time. For example, an electromyogram sensor is used to measure muscle activity. The generation AI then analyzes the electromyogram data to assess the level of fatigue. For example, it predicts the driver's level of fatigue based on fluctuations in the electromyogram. The generation AI then assesses the driver's level of fatigue based on the electromyogram data. For example, if a specific pattern is observed in the electromyogram, it may determine that the level of fatigue is high. In this way, the level of fatigue can be accurately assessed by measuring the electromyogram and having the generation AI analyze it.

[0044] The fatigue assessment unit uses a device that tracks the driver's eye movements while driving, and the generation AI analyzes this to assess the level of fatigue. The fatigue assessment unit, for example, installs an eye-tracking device inside the vehicle to track the driver's eye movements and collects data in real time. For example, it measures eye movements and gaze points. The generation AI then analyzes the eye movement data to assess the level of fatigue. For example, if the eye movements slow down or the gaze point becomes fixed, it determines that the level of fatigue is high. Furthermore, the generation AI assesses the driver's level of fatigue based on the eye movement data. For example, it detects signs of stress and fatigue based on fluctuations in the gaze. This allows eye movements to be tracked and analyzed by the generation AI, allowing for an accurate assessment of fatigue.

[0045] The fatigue assessment unit uses a device that monitors the driver's posture while driving, and the generation AI analyzes this to assess the level of fatigue. For example, the fatigue assessment unit installs a posture monitoring device inside the vehicle to monitor the driver's posture while driving and collects data in real time. For example, it measures the driver's sitting position and body movements. The generation AI then analyzes the posture data to assess the level of fatigue. For example, if the driver's posture deteriorates or their body movements decrease, it will determine that the level of fatigue is high. Furthermore, the generation AI assesses the driver's level of fatigue based on the posture data. For example, it can detect signs of stress or fatigue based on changes in posture. This allows posture to be monitored and analyzed by the generation AI to accurately assess the level of fatigue.

[0046] The fatigue assessment unit analyzes the voice commands given by the driver while driving, and the generation AI can assess the level of fatigue based on this. For example, the fatigue assessment unit installs a microphone inside the vehicle to collect voice commands given by the driver while driving and collects data in real time. For example, it records voice instructions to the navigation system. The generation AI then analyzes the voice command data and assesses the level of fatigue. For example, it assesses the driver's level of fatigue based on changes in the frequency and content of voice commands. Furthermore, the generation AI assesses the driver's level of fatigue based on the voice command data. For example, it detects signs of stress and fatigue based on changes in the tone and speaking style of the voice commands. This allows the generation AI to analyze voice commands and assess the level of fatigue based on this, enabling a more accurate assessment of fatigue.

[0047] The fatigue assessment unit collects smartphone usage data while the driver is driving, and the generation AI analyzes this data to assess the level of fatigue. For example, the fatigue assessment unit develops a smartphone app to collect smartphone usage data while the driver is driving, and collects data in real time. For example, the frequency of calls and messages is recorded. The generation AI then analyzes the smartphone usage data to assess the level of fatigue. For example, the driver's fatigue level is assessed based on changes in the frequency and content of smartphone use. Furthermore, the generation AI assesses the driver's fatigue level based on the smartphone usage data. For example, signs of stress and fatigue can be detected based on fluctuations in smartphone usage patterns. This allows for a more accurate assessment of fatigue level by collecting smartphone usage data and having the generation AI analyze it.

[0048] In the fatigue assessment unit, the generation AI performs an integrated analysis of the driver's biometric data and behavioral data, enabling a more accurate assessment of fatigue levels. In the fatigue assessment unit, for example, the generation AI performs an integrated analysis of the driver's biometric data and behavioral data. For example, it evaluates fatigue levels by combining data such as heart rate, body temperature, eye movement, and posture while driving. Furthermore, by analyzing biometric data and behavioral data in an integrated manner, the generation AI performs a more accurate assessment of fatigue levels. For example, it combines multiple data sources to predict fluctuations in fatigue levels. Furthermore, the generation AI performs an integrated analysis of biometric data and behavioral data to evaluate the driver's fatigue levels. For example, it analyzes correlations in the data to improve the accuracy of fatigue assessments. As a result, the integrated analysis of biometric data and behavioral data enables a more accurate assessment of fatigue levels.

[0049] The fatigue evaluation unit allows the generation AI to compare the driver's past data with current data and evaluate changes in fatigue level. For example, the generation AI collects the driver's past data and current data and performs comparative analysis. For example, it compares past heart rate and body temperature data with current data to evaluate changes in fatigue level. The generation AI also evaluates changes in fatigue level by comparing past data with current data. For example, it compares past driving history with current driving conditions to predict changes in fatigue level. Furthermore, the generation AI evaluates changes in the driver's fatigue level based on past data and current data. For example, it identifies fatigue level patterns from past data and compares them with current data for evaluation. This makes it possible to evaluate changes in fatigue level by comparing past data with current data.

[0050] The fatigue assessment unit allows the generation AI to compare the driver's data with that of other drivers to assess the relative level of fatigue. For example, the generation AI compares the driver's data with that of other drivers to assess the relative level of fatigue. For example, it compares fatigue levels under the same driving conditions. The generation AI also assesses the relative level of fatigue based on the data of other drivers. For example, it compares fatigue levels over the same driving time or route. Furthermore, the generation AI compares the driver's data with that of other drivers to assess the relative level of fatigue. For example, it analyzes the data of multiple drivers in an integrated manner to improve the accuracy of fatigue assessment. This makes it possible to assess the relative level of fatigue by comparing it with the data of other drivers.

[0051] The fatigue evaluation unit allows the generation AI to analyze the driver's data for different time periods and seasons and evaluate fatigue patterns. In the fatigue evaluation unit, for example, the generation AI collects and analyzes the driver's data for different time periods and seasons. For example, it compares data from daytime and night, and summer and winter, and evaluates fatigue patterns. The generation AI also evaluates fatigue patterns based on data from different time periods and seasons. For example, it analyzes the difference in fatigue levels between daytime driving and nighttime driving. Furthermore, the generation AI analyzes the driver's data for different time periods and seasons and evaluates fatigue patterns. For example, it predicts seasonal fluctuations in fatigue levels and reflects this in operation plans. This makes it possible to evaluate fatigue patterns by analyzing data for different time periods and seasons.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The fatigue assessment unit uses a device that monitors the driver's posture while driving, and the generation AI can analyze this to assess the level of fatigue. For example, to monitor the driver's posture while driving, a posture monitoring device can be installed inside the vehicle and data can be collected in real time. For example, the sitting position and body movements can be measured. The generation AI then analyzes the posture data to assess the level of fatigue. For example, if the posture is poor or the body movements are reduced, it can be determined that the level of fatigue is high. Furthermore, the generation AI evaluates the driver's level of fatigue based on the posture data. For example, it can detect signs of stress and fatigue based on changes in posture. In this way, posture can be monitored and analyzed by the generation AI, allowing for an accurate assessment of fatigue.

[0054] In the operation judgment unit, the generation AI can change the driving route or suggest rest points based on the driver's fatigue level. For example, the generation AI can suggest the optimal driving route based on the driver's fatigue level data. For example, if the fatigue level is high, a shorter route can be selected. The generation AI can also suggest appropriate rest points based on the driver's fatigue level data. For example, if the fatigue level is high, a nearby rest area can be suggested. Furthermore, the generation AI can change the driving route or suggest rest points based on the driver's fatigue level data. For example, if the fatigue level is high, a safe route can be selected and appropriate rest stops can be instructed. This allows for safer driving by changing the driving route or suggesting rest points based on the driver's fatigue level.

[0055] The fatigue assessment unit uses a device to measure the driver's brain waves, which the generation AI analyzes to assess the level of fatigue. For example, the driver wears a device to measure brain waves and collects data in real time. For example, an EEG sensor is used to measure brain activity. The generation AI then analyzes the EEG data to assess the level of fatigue. For example, the driver's level of fatigue is predicted based on fluctuations in brain waves. The generation AI then assesses the driver's level of fatigue based on the EEG data. For example, if a specific pattern of brain waves is observed, it is determined that the level of fatigue is high. In this way, by measuring brain waves and analyzing them with the generation AI, the level of fatigue can be accurately assessed.

[0056] The operation decision unit allows the generation AI to determine whether or not to operate the vehicle based on the individual health condition of the driver. For example, the generation AI collects health data on the driver and determines whether or not to operate the vehicle taking into account the individual health condition. For example, if the driver has a chronic illness, the vehicle may be suspended even if the fatigue level is low. The generation AI also collects allergy data on the driver and uses this information to determine whether or not to operate the vehicle. For example, if the driver has an allergic reaction, the vehicle may be suspended. Furthermore, the generation AI performs an integrated analysis of the individual health condition and determines whether or not to operate the vehicle. For example, the generation AI takes into account multiple health factors to make the optimal operation decision. This allows for safer operation decisions by taking into account the individual health condition.

[0057] The fatigue assessment unit uses a device to measure the driver's breathing pattern, which the generation AI then analyzes to assess the level of fatigue. For example, the driver wears a device to measure the breathing pattern and collects data in real time. For example, a breathing sensor is used to measure the rhythm and depth of breathing. The generation AI then analyzes the breathing pattern data to assess the level of fatigue. For example, if breathing becomes shallow or irregular, it is determined that the level of fatigue is high. Furthermore, the generation AI assesses the driver's level of fatigue based on the breathing pattern data. For example, signs of stress and fatigue can be detected based on fluctuations in breathing. In this way, the breathing pattern can be measured and analyzed by the generation AI, allowing for an accurate assessment of fatigue.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The fatigue assessment unit uses the generation AI to assess the driver's fatigue level. Specifically, data such as the driver's heart rate, body temperature, eye movements, and reaction time are collected, and the generation AI analyzes this data. The generation AI can also input the driver's past driving history data and predict the current level of fatigue based on past fatigue patterns. Furthermore, the generation AI can analyze voice data, evaluate the driver's psychological state from the tone of voice and speaking style, and estimate the level of fatigue. Step 2: The operation decision unit determines whether or not to operate the vehicle based on the fatigue level assessed by the fatigue level assessment unit. Specifically, if the fatigue level is high, the unit will stop operation and instruct the driver to take a rest. In addition to the fatigue level assessed by the generation AI, the unit can also consider external data such as weather and traffic conditions to determine whether or not to operate the vehicle. Furthermore, the unit can also consider the driver's individual health condition (for example, chronic illnesses or allergies) to determine whether or not to operate the vehicle.

[0060] (Example 2) The fatigue assessment system according to the embodiment of the present invention uses a generation AI to assess the driver's fatigue level and determines whether or not to operate the vehicle based on the assessment results. This ensures the safety of transportation companies and reduces the risk of accidents.

[0061] A fatigue assessment system according to an embodiment includes a fatigue assessment unit and an operation determination unit. The fatigue assessment unit uses a generation AI to assess the driver's fatigue level. For example, the fatigue assessment unit collects data such as the driver's heart rate, body temperature, eye movement, and reaction time, and the generation AI analyzes this data. The fatigue assessment unit can also input the driver's past driving history data into the generation AI and predict the driver's current fatigue level based on past fatigue patterns. Furthermore, the fatigue assessment unit can also estimate the driver's fatigue level from the driver's tone of voice and speaking style by analyzing voice data to evaluate the driver's psychological state. The operation determination unit determines whether or not to operate the vehicle based on the fatigue level assessed by the fatigue assessment unit. For example, the operation determination unit instructs the driver to stop operation and take a break if the driver's fatigue level is high. The operation determination unit can also determine whether or not to operate the vehicle by taking into account external data such as weather and traffic conditions in addition to the fatigue level assessed by the generation AI. Furthermore, the operation determination unit can determine whether or not to operate the vehicle by taking into account the driver's individual health condition (e.g., chronic illnesses and allergies) through the generation AI. As a result, the fatigue assessment system according to the embodiment can accurately assess the driver's fatigue level and appropriately determine whether or not the vehicle can be driven. For example, fatigue caused by long hours of driving can be prevented, reducing the risk of traffic accidents. Furthermore, since the system is useful for managing the health of drivers, it is expected to greatly contribute to ensuring the safety of transportation businesses.

[0062] The fatigue assessment unit collects data on the driver's heart rate, body temperature, eye movement, and reaction time, and the generation AI can analyze this data. The fatigue assessment unit, for example, uses a wearable device to measure the driver's heart rate. For example, a smartwatch or heart rate monitor is worn to collect heart rate data in real time. A thermometer or wearable device is also used to measure body temperature. For example, a thermometer is used to measure body temperature regularly and collect data. Furthermore, an eye-tracking device is used to measure eye movement. For example, a camera is installed inside the vehicle to track the driver's eye movement in real time. A reaction test is conducted to measure reaction time. For example, a simple reaction test is conducted regularly to measure the driver's reaction time. In this way, the driver's biometric data can be collected and analyzed by the generation AI, allowing for an accurate assessment of fatigue level.

[0063] The operation judgment unit can stop operation and instruct the driver to take a rest if the level of fatigue is high. For example, the operation judgment unit instructs the driver to stop operation if the level of fatigue is high. For example, if the level of fatigue assessed by the generation AI exceeds a certain threshold, the operation judgment unit stops operation and instructs the driver to take a rest. The operation judgment unit also gives specific instructions for taking a rest. For example, it suggests the length of the rest period and the location of the rest. Furthermore, the operation judgment unit instructs the timing for resuming operation after the break. For example, it instructs the driver to resume operation after a certain rest period has elapsed. In this way, the risk of an accident can be reduced by stopping operation and taking a rest when the level of fatigue is high.

[0064] The fatigue assessment unit inputs the driver's past driving history data into the generation AI and can predict the current level of fatigue based on past fatigue patterns. The fatigue assessment unit, for example, collects the driver's past driving history data and inputs it into the generation AI. For example, it analyzes fatigue patterns based on data such as past driving time, rest times, and driving routes. The generation AI also learns the past driving history data and predicts fatigue levels by comparing it with the current driving situation. For example, it evaluates the current level of fatigue based on fatigue patterns from past long driving sessions. Furthermore, the generation AI predicts fluctuations in the driver's fatigue level based on the past driving history data. For example, it identifies the timing when fatigue levels increase from past data and evaluates fatigue levels according to the current driving situation. This enables more accurate fatigue assessment by predicting current fatigue levels based on past driving history data.

[0065] The fatigue assessment unit can analyze the driver's voice data and estimate the level of fatigue from the tone of voice and speaking style. For example, the fatigue assessment unit collects the driver's voice data, and the generation AI analyzes the tone of voice and speaking style. For example, it records conversations or monologues while driving and evaluates the level of fatigue based on changes in the voice. The generation AI also analyzes the voice data and detects changes in the tone of voice and speaking style. For example, it determines that the level of fatigue is high if the voice becomes lower or the speaking style becomes slower. Furthermore, the generation AI evaluates the driver's psychological state based on the driver's voice data. For example, it detects signs of stress and fatigue based on changes in the tone of voice and speaking style. This makes it possible to analyze the voice data and estimate the level of fatigue from the tone of voice and speaking style, thereby evaluating the psychological state.

[0066] The fatigue evaluation unit can use the emotion estimation function to analyze the driver's facial expression data and evaluate the level of fatigue from the emotional state. The fatigue evaluation unit, for example, collects the driver's facial expression data, which the generation AI analyzes using the emotion estimation function. For example, a camera is used to photograph the driver's face and detect changes in facial expression. The generation AI then analyzes the facial expression data and evaluates the emotional state. For example, it may determine that the level of fatigue is high if the driver smiles less or if the brow furrows. Furthermore, the emotion estimation function is used to evaluate the level of fatigue based on the driver's facial expression data. For example, it may detect signs of stress or fatigue based on changes in facial expression. This allows for a more accurate assessment of fatigue by analyzing the facial expression data and evaluating the level of fatigue from the emotional state.

[0067] The fatigue assessment unit collects the driver's dietary data and sleep data, and the generation AI can evaluate the driver's fatigue level based on this data. The fatigue assessment unit, for example, collects the driver's dietary data, and the generation AI analyzes it. For example, it records the content and time of meals and evaluates the level of fatigue based on nutritional balance and meal timing. The generation AI also collects the driver's sleep data, and analyzes it. For example, it records the amount of sleep and sleep quality and evaluates the level of fatigue based on sleep deprivation and irregular sleep patterns. Furthermore, the generation AI analyzes the dietary and sleep data comprehensively and evaluates the driver's fatigue level. For example, it predicts fluctuations in fatigue level based on the balance between diet and sleep. This allows for a more accurate assessment of fatigue level by evaluating the level of fatigue based on dietary and sleep data.

[0068] The fatigue assessment unit analyzes the music selection and radio content made by the driver while driving, and the generation AI can estimate the level of fatigue from this information. For example, the fatigue assessment unit collects data on the music selection made by the driver while driving, and the generation AI analyzes this data. For example, the fatigue level of the driver is assessed based on the music genre and tempo. The content of the radio the driver listens to while driving is also analyzed, and the generation AI estimates the level of fatigue based on this data. For example, the content of news and talk shows is analyzed to assess the driver's interest and concentration. Furthermore, the generation AI comprehensively analyzes the music selection and radio content to assess the driver's level of fatigue. For example, fluctuations in fatigue level are predicted based on changes in music and radio content. This makes it possible to estimate the driver's level of fatigue by analyzing the music selection and radio content.

[0069] The fatigue evaluation unit can use the emotion estimation function to analyze the content of the driver's speech while driving and evaluate the level of fatigue from the emotional state. For example, the fatigue evaluation unit collects the content of the driver's speech while driving, and the generation AI analyzes it using the emotion estimation function. For example, conversations or monologue while driving are recorded and changes in the content of the speech are detected. The generation AI also analyzes the content of the speech and evaluates the emotional state. For example, the driver's fatigue level is evaluated based on changes in the frequency and content of the speech. Furthermore, the emotion estimation function is used to evaluate the level of fatigue based on the content of the driver's speech. For example, signs of stress and fatigue are detected based on changes in the tone and content of the speech. This enables a more accurate fatigue evaluation by analyzing the content of the speech and evaluating the level of fatigue from the emotional state.

[0070] The operation decision unit can determine whether or not to operate the vehicle based on the fatigue level assessed by the generation AI as well as external data on weather and traffic conditions. For example, the generation AI collects weather data in addition to driver fatigue level data to determine whether or not to operate the vehicle. For example, in bad weather, the operation may be canceled even if the fatigue level is low. The generation AI also collects traffic condition data and determines whether or not to operate the vehicle in conjunction with the driver's fatigue level. For example, if there is traffic congestion, the operation may be canceled even if the fatigue level is low. Furthermore, the generation AI performs an integrated analysis of external data such as weather and traffic conditions to determine whether or not to operate the vehicle. For example, it takes multiple external factors into consideration to make optimal operation decisions. This makes it possible to make safer operation decisions by taking external data into consideration.

[0071] The operation decision unit allows the generation AI to determine whether or not to operate the vehicle based on the individual health condition of the driver. For example, the operation decision unit allows the generation AI to collect health data on the driver and determine whether or not to operate the vehicle taking into account the individual health condition. For example, if the driver has a chronic illness, the operation will be suspended even if the fatigue level is low. The generation AI also collects allergy data on the driver and determines whether or not to operate the vehicle based on this. For example, if the driver has an allergic reaction, the operation will be suspended. Furthermore, the generation AI performs an integrated analysis of the individual health condition and determines whether or not to operate the vehicle. For example, it takes multiple health factors into account to make the optimal operation decision. This allows for safer operation decisions by taking into account the individual health condition.

[0072] The operation judgment unit can use the emotion estimation function to evaluate the driver's emotional state and instruct the driver to stop driving if the driver's emotions are unstable. The operation judgment unit, for example, uses the emotion estimation function to evaluate the driver's emotional state in real time. For example, it can analyze the driver's facial expressions and voice using a camera or microphone and halt driving if the driver's emotions are unstable. The generation AI also analyzes the emotional data and instructs the driver to halt driving if the driver's emotions are unstable. For example, it can halt driving if stress or anxiety is high. Furthermore, the emotion estimation function is used to determine whether or not to drive based on the driver's emotional state. For example, it can instruct the driver to take a break if the driver's emotions are unstable. In this way, by evaluating the driver's emotional state and halting driving if the driver's emotions are unstable, the risk of an accident can be reduced.

[0073] In the operation judgment unit, the generation AI can change the operation route or suggest rest points based on the driver's fatigue level. In the operation judgment unit, for example, the generation AI suggests the optimal operation route based on the driver's fatigue level data. For example, if the fatigue level is high, a shorter route will be selected. The generation AI also suggests appropriate rest points based on the driver's fatigue level data. For example, if the fatigue level is high, a nearby rest area will be suggested. Furthermore, the generation AI changes the operation route or suggests rest points based on the driver's fatigue level data. For example, if the fatigue level is high, a safe route will be selected and appropriate rest will be instructed. In this way, by changing the operation route or suggesting rest points based on the fatigue level, safer operation is possible.

[0074] The operation judgment unit allows the generation AI to adjust the operation schedule based on the driver's fatigue level and propose a reasonable operation plan. In the operation judgment unit, for example, the generation AI adjusts the operation schedule based on the driver's fatigue level data. For example, if the fatigue level is high, the operation time is shortened. The generation AI also proposes a reasonable operation plan based on the driver's fatigue level data. For example, if the fatigue level is high, it suggests taking more breaks. Furthermore, the generation AI adjusts the operation schedule based on the driver's fatigue level data and proposes an optimal operation plan. For example, if the fatigue level is high, it may instruct the driver to stop operation and take a break. In this way, by adjusting the operation schedule based on the fatigue level, a reasonable operation plan is possible.

[0075] The operation determination unit can use the emotion estimation function to evaluate the driver's emotional state and instruct the driver to continue driving if the emotion is stable. The operation determination unit, for example, uses the emotion estimation function to evaluate the driver's emotional state in real time. For example, it can analyze the driver's facial expressions and voice using a camera or microphone, and continue driving if the emotion is stable. In addition, the generation AI analyzes the emotion data and instructs the driver to continue driving if the emotion is stable. For example, it continues driving if stress and anxiety are low. Furthermore, it uses the emotion estimation function to determine whether or not to drive based on the driver's emotional state. For example, it instructs the driver to continue driving if the emotion is stable. This enables efficient driving by continuing driving if the emotional state is stable.

[0076] The fatigue assessment unit uses a device that measures the driver's electrodermal activity, which the generation AI analyzes to assess the level of fatigue. The fatigue assessment unit, for example, has the driver wear a device that measures electrodermal activity and collects data in real time. For example, an electrodermal activity sensor is used to detect signs of stress and fatigue. The generation AI then analyzes the electrodermal activity data to assess the level of fatigue. For example, it predicts the driver's level of fatigue based on fluctuations in electrodermal activity. The generation AI then assesses the driver's level of fatigue based on the electrodermal activity data. For example, if an increase in electrodermal activity is observed, it is determined that the level of fatigue is high. In this way, by measuring electrodermal activity and having the generation AI analyze it, the level of fatigue can be accurately assessed.

[0077] The fatigue assessment unit uses a device to measure the driver's breathing pattern, which the generation AI analyzes to assess the level of fatigue. The fatigue assessment unit, for example, has the driver wear a device that measures their breathing pattern and collects data in real time. For example, a breathing sensor is used to measure the rhythm and depth of breathing. The generation AI then analyzes the breathing pattern data to assess the level of fatigue. For example, if breathing becomes shallow or irregular, it is determined that the level of fatigue is high. Furthermore, the generation AI assesses the driver's level of fatigue based on the breathing pattern data. For example, signs of stress and fatigue can be detected based on fluctuations in breathing. In this way, the breathing pattern can be measured and analyzed by the generation AI to accurately assess the level of fatigue.

[0078] The fatigue assessment unit can use the emotion estimation function to collect data on the driver's facial expression and evaluate the level of fatigue from the driver's emotional state. For example, the fatigue assessment unit installs a camera inside the vehicle to collect data on the driver's facial expression in real time. For example, it uses facial recognition technology to detect changes in facial expression. The generative AI then analyzes the facial expression data and evaluates the emotional state. For example, if the driver smiles less or the brow furrows, it determines that the driver is highly fatigued. Furthermore, the emotion estimation function is used to evaluate the level of fatigue based on the driver's facial expression data. For example, it detects signs of stress or fatigue based on changes in facial expression. This allows for more accurate fatigue assessment by collecting facial expression data and evaluating the level of fatigue from the driver's emotional state.

[0079] The fatigue assessment unit uses a device to measure the driver's brain waves, which the generation AI analyzes to assess the level of fatigue. The fatigue assessment unit, for example, has the driver wear a device to measure brain waves and collect data in real time. For example, it uses an EEG sensor to measure brain activity. The generation AI then analyzes the EEG data to assess the level of fatigue. For example, it predicts the driver's level of fatigue based on fluctuations in brain waves. The generation AI then assesses the driver's level of fatigue based on the EEG data. For example, if a specific pattern of brain waves is observed, it may determine that the level of fatigue is high. In this way, by measuring brain waves and having the generation AI analyze them, the level of fatigue can be accurately assessed.

[0080] The fatigue evaluation unit uses a device to measure the driver's electromyogram, which the generation AI analyzes to assess the level of fatigue. The fatigue evaluation unit, for example, has the driver wear a device to measure the electromyogram and collect data in real time. For example, an electromyogram sensor is used to measure muscle activity. The generation AI then analyzes the electromyogram data to assess the level of fatigue. For example, it predicts the driver's level of fatigue based on fluctuations in the electromyogram. The generation AI then assesses the driver's level of fatigue based on the electromyogram data. For example, if a specific pattern is observed in the electromyogram, it may determine that the level of fatigue is high. In this way, the level of fatigue can be accurately assessed by measuring the electromyogram and having the generation AI analyze it.

[0081] The fatigue assessment unit can use the emotion estimation function to collect the driver's voice data and evaluate the level of fatigue from their emotional state. For example, the fatigue assessment unit installs a microphone inside the vehicle to collect the driver's voice data and collects data in real time. For example, it records conversations and monologues while driving. The generative AI then analyzes the voice data and evaluates the emotional state. For example, it evaluates the driver's level of fatigue based on changes in voice tone and speaking style. Furthermore, it uses the emotion estimation function to evaluate the level of fatigue based on the driver's voice data. For example, it detects signs of stress and fatigue based on changes in voice tone and speaking style. This allows for more accurate fatigue assessment by collecting voice data and evaluating the level of fatigue from their emotional state.

[0082] The fatigue assessment unit uses a device that tracks the driver's eye movements while driving, and the generation AI analyzes this to assess the level of fatigue. The fatigue assessment unit, for example, installs an eye-tracking device inside the vehicle to track the driver's eye movements and collects data in real time. For example, it measures eye movements and gaze points. The generation AI then analyzes the eye movement data to assess the level of fatigue. For example, if the eye movements slow down or the gaze point becomes fixed, it determines that the level of fatigue is high. Furthermore, the generation AI assesses the driver's level of fatigue based on the eye movement data. For example, it detects signs of stress and fatigue based on fluctuations in the gaze. This allows eye movements to be tracked and analyzed by the generation AI, allowing for an accurate assessment of fatigue.

[0083] The fatigue assessment unit uses a device that monitors the driver's posture while driving, and the generation AI analyzes this to assess the level of fatigue. For example, the fatigue assessment unit installs a posture monitoring device inside the vehicle to monitor the driver's posture while driving and collects data in real time. For example, it measures the driver's sitting position and body movements. The generation AI then analyzes the posture data to assess the level of fatigue. For example, if the driver's posture deteriorates or their body movements decrease, it will determine that the level of fatigue is high. Furthermore, the generation AI assesses the driver's level of fatigue based on the posture data. For example, it can detect signs of stress or fatigue based on changes in posture. This allows posture to be monitored and analyzed by the generation AI to accurately assess the level of fatigue.

[0084] The fatigue assessment unit can use the emotion estimation function to collect facial expression data of the driver while driving and evaluate the level of fatigue from the emotional state. For example, the fatigue assessment unit installs a camera inside the vehicle to collect data on the driver's facial expression while driving and collects data in real time. For example, facial recognition technology is used to detect changes in facial expression. The generative AI then analyzes the facial expression data and evaluates the emotional state. For example, if the driver smiles less or the brow furrows, it is determined that the level of fatigue is high. Furthermore, the emotion estimation function is used to evaluate the level of fatigue based on the driver's facial expression data. For example, signs of stress and fatigue are detected based on changes in facial expression. This allows for more accurate fatigue assessment by collecting facial expression data while driving and evaluating the level of fatigue from the emotional state.

[0085] The fatigue assessment unit analyzes the voice commands given by the driver while driving, and the generation AI can assess the level of fatigue based on this. For example, the fatigue assessment unit installs a microphone inside the vehicle to collect voice commands given by the driver while driving and collects data in real time. For example, it records voice instructions to the navigation system. The generation AI then analyzes the voice command data and assesses the level of fatigue. For example, it assesses the driver's level of fatigue based on changes in the frequency and content of voice commands. Furthermore, the generation AI assesses the driver's level of fatigue based on the voice command data. For example, it detects signs of stress and fatigue based on changes in the tone and speaking style of the voice commands. This allows the generation AI to analyze voice commands and assess the level of fatigue based on this, enabling a more accurate assessment of fatigue.

[0086] The fatigue assessment unit collects smartphone usage data while the driver is driving, and the generation AI analyzes this data to assess the level of fatigue. For example, the fatigue assessment unit develops a smartphone app to collect smartphone usage data while the driver is driving, and collects data in real time. For example, the frequency of calls and messages is recorded. The generation AI then analyzes the smartphone usage data to assess the level of fatigue. For example, the driver's fatigue level is assessed based on changes in the frequency and content of smartphone use. Furthermore, the generation AI assesses the driver's fatigue level based on the smartphone usage data. For example, signs of stress and fatigue can be detected based on fluctuations in smartphone usage patterns. This allows for a more accurate assessment of fatigue level by collecting smartphone usage data and having the generation AI analyze it.

[0087] The fatigue assessment unit can use the emotion estimation function to collect voice data of the driver while driving and evaluate the level of fatigue from the emotional state. For example, the fatigue assessment unit installs a microphone inside the vehicle to collect voice data of the driver while driving and collects data in real time. For example, it records conversations and monologues while driving. The generative AI then analyzes the voice data and evaluates the emotional state. For example, it evaluates the driver's level of fatigue based on changes in voice tone and speaking style. Furthermore, it uses the emotion estimation function to evaluate the level of fatigue based on the driver's voice data. For example, it detects signs of stress and fatigue based on changes in voice tone and speaking style. This allows for more accurate fatigue assessment by collecting voice data while driving and evaluating the level of fatigue from the emotional state.

[0088] In the fatigue assessment unit, the generation AI performs an integrated analysis of the driver's biometric data and behavioral data, enabling a more accurate assessment of fatigue levels. In the fatigue assessment unit, for example, the generation AI performs an integrated analysis of the driver's biometric data and behavioral data. For example, it evaluates fatigue levels by combining data such as heart rate, body temperature, eye movement, and posture while driving. Furthermore, by analyzing biometric data and behavioral data in an integrated manner, the generation AI performs a more accurate assessment of fatigue levels. For example, it combines multiple data sources to predict fluctuations in fatigue levels. Furthermore, the generation AI performs an integrated analysis of biometric data and behavioral data to evaluate the driver's fatigue levels. For example, it analyzes correlations in the data to improve the accuracy of fatigue assessments. As a result, the integrated analysis of biometric data and behavioral data enables a more accurate assessment of fatigue levels.

[0089] The fatigue evaluation unit allows the generation AI to compare the driver's past data with current data and evaluate changes in fatigue level. For example, the generation AI collects the driver's past data and current data and performs comparative analysis. For example, it compares past heart rate and body temperature data with current data to evaluate changes in fatigue level. The generation AI also evaluates changes in fatigue level by comparing past data with current data. For example, it compares past driving history with current driving conditions to predict changes in fatigue level. Furthermore, the generation AI evaluates changes in the driver's fatigue level based on past data and current data. For example, it identifies fatigue level patterns from past data and compares them with current data for evaluation. This makes it possible to evaluate changes in fatigue level by comparing past data with current data.

[0090] The fatigue level assessment unit can use the emotion estimation function to analyze the driver's emotional data and assess the level of fatigue from the emotional state. For example, the fatigue level assessment unit uses the emotion estimation function to collect the driver's emotional data, which the generation AI analyzes. For example, the emotional state is assessed based on facial expressions and voice data. The generation AI also analyzes the emotional data and assesses the level of fatigue based on the emotional state. For example, it determines that the level of fatigue is high when emotions are unstable. Furthermore, the emotion estimation function is used to assess the level of fatigue based on the driver's emotional data. For example, signs of stress and fatigue are detected based on emotional fluctuations. This enables a more accurate assessment of fatigue by analyzing the emotional data and assessing the level of fatigue from the emotional state.

[0091] The fatigue assessment unit allows the generation AI to compare the driver's data with that of other drivers to assess the relative level of fatigue. For example, the generation AI compares the driver's data with that of other drivers to assess the relative level of fatigue. For example, it compares fatigue levels under the same driving conditions. The generation AI also assesses the relative level of fatigue based on the data of other drivers. For example, it compares fatigue levels over the same driving time or route. Furthermore, the generation AI compares the driver's data with that of other drivers to assess the relative level of fatigue. For example, it analyzes the data of multiple drivers in an integrated manner to improve the accuracy of fatigue assessment. This makes it possible to assess the relative level of fatigue by comparing it with the data of other drivers.

[0092] The fatigue evaluation unit allows the generation AI to analyze the driver's data for different time periods and seasons and evaluate fatigue patterns. In the fatigue evaluation unit, for example, the generation AI collects and analyzes the driver's data for different time periods and seasons. For example, it compares data from daytime and night, and summer and winter, and evaluates fatigue patterns. The generation AI also evaluates fatigue patterns based on data from different time periods and seasons. For example, it analyzes the difference in fatigue levels between daytime driving and nighttime driving. Furthermore, the generation AI analyzes the driver's data for different time periods and seasons and evaluates fatigue patterns. For example, it predicts seasonal fluctuations in fatigue levels and reflects this in operation plans. This makes it possible to evaluate fatigue patterns by analyzing data for different time periods and seasons.

[0093] The fatigue level assessment unit can use the emotion estimation function to analyze the driver's emotional data and assess the level of fatigue from the emotional state. For example, the fatigue level assessment unit uses the emotion estimation function to collect the driver's emotional data, which the generation AI analyzes. For example, the emotional state is assessed based on facial expressions and voice data. The generation AI also analyzes the emotional data and assesses the level of fatigue based on the emotional state. For example, it determines that the level of fatigue is high when emotions are unstable. Furthermore, the emotion estimation function is used to assess the level of fatigue based on the driver's emotional data. For example, signs of stress and fatigue are detected based on emotional fluctuations. This enables a more accurate assessment of fatigue by analyzing the emotional data and assessing the level of fatigue from the emotional state.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The fatigue assessment unit uses a device that monitors the driver's posture while driving, and the generation AI can analyze this to assess the level of fatigue. For example, to monitor the driver's posture while driving, a posture monitoring device can be installed inside the vehicle and data can be collected in real time. For example, the sitting position and body movements can be measured. The generation AI then analyzes the posture data to assess the level of fatigue. For example, if the posture is poor or the body movements are reduced, it can be determined that the level of fatigue is high. Furthermore, the generation AI evaluates the driver's level of fatigue based on the posture data. For example, it can detect signs of stress and fatigue based on changes in posture. In this way, posture can be monitored and analyzed by the generation AI, allowing for an accurate assessment of fatigue.

[0096] In the operation judgment unit, the generation AI can change the driving route or suggest rest points based on the driver's fatigue level. For example, the generation AI can suggest the optimal driving route based on the driver's fatigue level data. For example, if the fatigue level is high, a shorter route can be selected. The generation AI can also suggest appropriate rest points based on the driver's fatigue level data. For example, if the fatigue level is high, a nearby rest area can be suggested. Furthermore, the generation AI can change the driving route or suggest rest points based on the driver's fatigue level data. For example, if the fatigue level is high, a safe route can be selected and appropriate rest stops can be instructed. This allows for safer driving by changing the driving route or suggesting rest points based on the driver's fatigue level.

[0097] The fatigue assessment unit uses a device to measure the driver's brain waves, which the generation AI analyzes to assess the level of fatigue. For example, the driver wears a device to measure brain waves and collects data in real time. For example, an EEG sensor is used to measure brain activity. The generation AI then analyzes the EEG data to assess the level of fatigue. For example, the driver's level of fatigue is predicted based on fluctuations in brain waves. The generation AI then assesses the driver's level of fatigue based on the EEG data. For example, if a specific pattern of brain waves is observed, it is determined that the level of fatigue is high. In this way, by measuring brain waves and analyzing them with the generation AI, the level of fatigue can be accurately assessed.

[0098] The operation decision unit allows the generation AI to determine whether or not to operate the vehicle based on the individual health condition of the driver. For example, the generation AI collects health data on the driver and determines whether or not to operate the vehicle taking into account the individual health condition. For example, if the driver has a chronic illness, the vehicle may be suspended even if the fatigue level is low. The generation AI also collects allergy data on the driver and uses this information to determine whether or not to operate the vehicle. For example, if the driver has an allergic reaction, the vehicle may be suspended. Furthermore, the generation AI performs an integrated analysis of the individual health condition and determines whether or not to operate the vehicle. For example, the generation AI takes into account multiple health factors to make the optimal operation decision. This allows for safer operation decisions by taking into account the individual health condition.

[0099] The fatigue assessment unit uses a device to measure the driver's breathing pattern, which the generation AI then analyzes to assess the level of fatigue. For example, the driver wears a device to measure the breathing pattern and collects data in real time. For example, a breathing sensor is used to measure the rhythm and depth of breathing. The generation AI then analyzes the breathing pattern data to assess the level of fatigue. For example, if breathing becomes shallow or irregular, it is determined that the level of fatigue is high. Furthermore, the generation AI assesses the driver's level of fatigue based on the breathing pattern data. For example, signs of stress and fatigue can be detected based on fluctuations in breathing. In this way, the breathing pattern can be measured and analyzed by the generation AI, allowing for an accurate assessment of fatigue.

[0100] The operation judgment unit can use the emotion estimation function to evaluate the driver's emotional state and instruct the driver to stop driving if the driver's emotions are unstable. For example, the emotion estimation function is used to evaluate the driver's emotional state in real time. For example, a camera or microphone can be used to analyze the driver's facial expressions and voice, and driving can be stopped if the driver's emotions are unstable. In addition, the generation AI analyzes the emotional data and instructs the driver to stop driving if the driver's emotions are unstable. For example, driving can be stopped if stress or anxiety is high. Furthermore, the emotion estimation function is used to determine whether or not to drive based on the driver's emotional state. For example, if the driver's emotions are unstable, the function can instruct the driver to take a break. In this way, by evaluating the driver's emotional state and stopping driving if the driver's emotions are unstable, the risk of accidents can be reduced.

[0101] The fatigue evaluation unit can use the emotion estimation function to analyze the driver's facial expression data and evaluate the level of fatigue from their emotional state. For example, the driver's facial expression data is collected, and the generation AI analyzes it using the emotion estimation function. For example, a camera is used to photograph the driver's face and detect changes in facial expression. The generation AI then analyzes the facial expression data and evaluates the emotional state. For example, it may determine that the level of fatigue is high if the driver smiles less or if the brow furrows. The emotion estimation function then evaluates the level of fatigue based on the driver's facial expression data. For example, it may detect signs of stress or fatigue based on changes in facial expression. This allows for a more accurate assessment of fatigue by analyzing facial expression data and evaluating the level of fatigue from the emotional state.

[0102] The fatigue evaluation unit can use the emotion estimation function to analyze the content of the driver's speech while driving and evaluate the level of fatigue from the emotional state. For example, the content of the driver's speech while driving is collected, and the generation AI analyzes it using the emotion estimation function. For example, conversations or monologue while driving are recorded and changes in the content of the speech are detected. The generation AI also analyzes the content of the speech and evaluates the emotional state. For example, the driver's fatigue level is evaluated based on changes in the frequency and content of the speech. Furthermore, the emotion estimation function is used to evaluate the level of fatigue based on the content of the driver's speech. For example, signs of stress and fatigue are detected based on changes in the tone and content of the speech. This allows for a more accurate assessment of fatigue by analyzing the content of the speech and evaluating the level of fatigue from the emotional state.

[0103] The operation judgment unit can use the emotion estimation function to evaluate the driver's emotional state and instruct the vehicle to continue operating if the emotion is stable. For example, the emotion estimation function is used to evaluate the driver's emotional state in real time. For example, a camera or microphone can be used to analyze the driver's facial expressions and voice, and operation can be continued if the emotion is stable. In addition, the generation AI analyzes the emotion data and instructs the vehicle to continue operating if the emotion is stable. For example, operation can be continued if stress and anxiety are low. Furthermore, the emotion estimation function is used to determine whether or not to operate based on the driver's emotional state. For example, operation can be instructed to continue operating if the emotion is stable. This enables efficient operation by continuing operation if the emotional state is stable.

[0104] The fatigue level assessment unit can use the emotion estimation function to analyze the driver's emotional data and assess the level of fatigue from their emotional state. For example, the emotion estimation function is used to collect the driver's emotional data, which the generation AI analyzes. For example, the emotional state is assessed based on facial expressions and voice data. The generation AI also analyzes the emotional data and assesses the level of fatigue based on the emotional state. For example, it may determine that the level of fatigue is high if emotions are unstable. Furthermore, the emotion estimation function is used to assess the level of fatigue based on the driver's emotional data. For example, signs of stress and fatigue are detected based on emotional fluctuations. This allows for a more accurate assessment of fatigue by analyzing the emotional data and assessing the level of fatigue from the emotional state.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The fatigue assessment unit uses the generation AI to assess the driver's fatigue level. Specifically, data such as the driver's heart rate, body temperature, eye movements, and reaction time are collected, and the generation AI analyzes this data. The generation AI can also input the driver's past driving history data and predict the current level of fatigue based on past fatigue patterns. Furthermore, the generation AI can analyze voice data, evaluate the driver's psychological state from the tone of voice and speaking style, and estimate the level of fatigue. Step 2: The operation decision unit determines whether or not to operate the vehicle based on the fatigue level assessed by the fatigue level assessment unit. Specifically, if the fatigue level is high, the unit will stop operation and instruct the driver to take a rest. In addition to the fatigue level assessed by the generation AI, the unit can also consider external data such as weather and traffic conditions to determine whether or not to operate the vehicle. Furthermore, the unit can also consider the driver's individual health condition (for example, chronic illnesses or allergies) to determine whether or not to operate the vehicle.

[0107] 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.

[0108] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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. [Explanation of symbols]

[0174] 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 fatigue level evaluation unit that evaluates the driver's fatigue level using a generation AI; an operation determination unit that determines whether or not operation is possible based on the fatigue level evaluated by the fatigue level evaluation unit; A system characterized by:

2. The fatigue level evaluation unit collecting data on the driver's heart rate, body temperature, eye movement, and reaction time; The generating AI analyzes this data.

2. The system of claim 1.

3. The operation determination unit If the fatigue level is high, the driver will be instructed to stop driving and take a break.

2. The system of claim 1.

4. The fatigue level evaluation unit Inputting the driver's past driving history data into the generating AI, Predicting current fatigue level based on past fatigue level patterns 2. The system of claim 1.

5. The fatigue level evaluation unit Analyzing the driver's voice data, Estimate fatigue level from the tone of voice and speaking style 2. The system of claim 1.

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