System

Abstract

An object of a system according to an embodiment is to analyze driving data of an elderly person, evaluate a limit of safe driving, and propose repayment of a license.SOLUTION: A system according to an embodiment includes an operation data collection unit, an analysis unit, an evaluation unit, and a proposal unit. The driving data collection unit collects driving data of an elderly person. The analysis unit analyzes the operation data collected by the operation data collection unit. The evaluation unit evaluates the limit of safe driving based on the data analyzed by the analysis unit. The proposal unit proposes repayment of the license on the basis of a result evaluated by the 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 properly assess the decline in driving ability of elderly people and suggest that they return their licenses.

[0005] The system according to the embodiment aims to analyze driving data of elderly people, evaluate the limits of safe driving, and suggest that they return their licenses. [Means for solving the problem]

[0006] The system according to the embodiment includes a driving data collection unit, an analysis unit, an evaluation unit, and a proposal unit. The driving data collection unit collects driving data of elderly people. The analysis unit analyzes the driving data collected by the driving data collection unit. The evaluation unit evaluates the limits of safe driving based on the data analyzed by the analysis unit. The proposal unit proposes surrendering the driver's license based on the results of the evaluation by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze driving data of elderly people, evaluate their limits in safe driving, and suggest that they return their licenses. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 driving data analysis system according to an embodiment of the present invention automatically collects driving data from elderly people, analyzes it using a generation AI, evaluates the limits of safe driving, and suggests that the elderly person surrender their license. As a result, the driving data analysis system can analyze the driving data of elderly people in detail and suggest that the elderly person surrender their license at an appropriate time when they reach the limits of safe driving.

[0029] A driving data analysis system according to an embodiment includes a driving data collection unit, an analysis unit, an evaluation unit, and a proposal unit. The driving data collection unit collects driving data of elderly drivers. For example, it collects data such as driving speed, frequency of brake use, and steering operation. The driving data collection unit can also collect driving data using sensors and cameras mounted on vehicles. For example, it collects data using the vehicle's speed sensor and brake sensor. The analysis unit analyzes the collected driving data. For example, it analyzes the driving data using statistical analysis of data or machine learning algorithms. The analysis unit can also analyze the driving data using a generation AI. For example, the generation AI analyzes driving patterns based on the driving data. The evaluation unit evaluates the safe driving limits based on the analyzed data. For example, it evaluates the safe driving limits based on reaction time and the frequency of driving errors. The evaluation unit can also evaluate the safe driving limits using the generation AI. For example, the generation AI identifies the safe driving limits based on the driving data. The proposal unit proposes surrendering the driver's license based on the evaluation results. For example, the generation AI might suggest, "Based on your recent driving data, it's becoming more difficult to drive safely. Perhaps you should consider returning your license." The suggestion unit can also encourage elderly people to return their licenses using appropriate words while protecting their self-esteem. For example, it might suggest, "While respecting your driving experience to date, consider returning your license for safety reasons." This allows the driving data analysis system to analyze elderly people's driving data in detail and suggest returning their licenses at the appropriate time when they have reached the limit of safe driving. For example, if the generation AI can suggest returning their licenses while protecting their self-esteem, it will be easier for elderly people to accept the suggestion.

[0030] The driving data collection unit collects the elderly person's driving data as well as health data such as heart rate and blood pressure in real time, which the generation AI can then comprehensively analyze. The driving data collection unit, for example, builds a system that collects health data such as heart rate and blood pressure in real time along with the elderly person's driving data. For example, health data is obtained using a wearable device while driving, and the generation AI analyzes that data. The generation AI, for example, analyzes heart rate fluctuations and blood pressure changes to evaluate stress and fatigue levels while driving. The generation AI can also perform an integrated analysis of driving data and health data to evaluate driving ability. For example, it can identify a decline in driving ability based on an increase in heart rate or blood pressure fluctuations while driving. This enables a more accurate evaluation of driving ability through an integrated analysis of driving data and health data.

[0031] The driving data collection unit can analyze facial expressions and eye movements using an in-car camera and facial recognition technology. The driving data collection unit, for example, installs an in-car camera and builds a system that uses facial recognition technology to analyze the facial expressions and eye movements of elderly people in real time. For example, it can evaluate the level of concentration and attention while driving. For example, facial recognition technology uses deep learning to extract facial feature points and analyze facial expressions and eye movements. In addition, facial recognition technology can also evaluate the emotional state while driving based on changes in facial expressions and eye movements while driving. For example, it can identify tension and fatigue while driving. As a result, facial recognition technology can be used to evaluate the level of concentration and attention while driving.

[0032] The driving data collection unit can also use drones to simultaneously collect data on the vehicle's external environment. The driving data collection unit uses, for example, drones to build a system that collects data on the vehicle's external environment in real time. For example, it analyzes road conditions and traffic volume and integrates the data with driving data. The drone is equipped with, for example, a high-resolution camera to capture images of road conditions and traffic conditions. The drone can also collect data in real time, which can be analyzed by the generation AI. For example, it can identify road congestion and the occurrence of traffic accidents. This makes it possible to use drones to collect data on the vehicle's external environment in real time and analyze it in an integrated manner with driving data.

[0033] The driving data collection unit can compare the driving data of an elderly person with the data of other elderly people to evaluate their relative driving ability. The driving data collection unit, for example, builds a system that collects driving data of elderly people and compares it with the data of other elderly people. For example, it performs a relative evaluation of driving ability. The generation AI, for example, calculates the average value and standard deviation of driving ability based on the driving data of other elderly people and uses it as a comparison target. The generation AI can also perform statistical analysis of the driving data to evaluate relative driving ability. For example, it performs a relative evaluation of driving ability based on reaction time and frequency of driving errors. This makes it possible to evaluate relative driving ability by comparing it with the data of other elderly people.

[0034] The evaluation unit allows the generation AI to perform individual driving simulations based on the driving data of the elderly person and recreate limit situations. The evaluation unit, for example, builds a system in which the generation AI performs individual driving simulations based on the driving data of the elderly person. For example, it recreates limit situations and evaluates driving ability. The generation AI, for example, builds a simulation environment based on the driving data and performs a driving simulation. The generation AI can also recreate limit situations through the driving simulation and evaluate driving ability. For example, it identifies limit situations based on delays in reaction time and the frequency of driving errors. In this way, limit situations can be recreated and driving ability can be evaluated by performing individual driving simulations.

[0035] In analyzing the driving data, the evaluation unit can refer to past accident data to identify similar risky behaviors. The evaluation unit, for example, analyzes driving data of elderly people and builds a system that identifies similar risky behaviors by referring to past accident data. For example, it analyzes patterns of driving errors. The generation AI, for example, identifies patterns of risky behavior based on past accident data and compares it with the driving data. The generation AI can also perform statistical analysis of the driving data to identify similar risky behaviors. For example, it identifies risky behaviors based on the frequency of sudden braking and sudden steering. This makes it possible to identify similar risky behaviors and evaluate driving ability by referring to past accident data.

[0036] The evaluation unit can compare the driving data of elderly people with data under different weather conditions and time periods, and evaluate the critical situation from multiple angles. The evaluation unit, for example, builds a system that compares the driving data of elderly people with data under different weather conditions and time periods. For example, it analyzes driving data in the rain or at night and evaluates the critical situation. The generation AI can evaluate fluctuations in driving ability based on driving data under different weather conditions and time periods. The generation AI can also perform statistical analysis of the driving data and evaluate the critical situation from multiple angles. For example, it can identify the critical situation based on delayed reaction time in the rain or reduced visibility at night. This makes it possible to evaluate the critical situation from multiple angles by comparing it with data under different weather conditions and time periods.

[0037] The evaluation unit can also take into account data from other traffic participants when assessing the limits of safe driving. For example, the evaluation unit collects data from other traffic participants (pedestrians, cyclists, etc.) along with driving data from elderly people, and builds a system in which the generation AI performs comprehensive analysis. For example, it evaluates the risk of traffic accidents. For example, the generation AI analyzes traffic conditions based on the data from other traffic participants and integrates it with the driving data. The generation AI can also assess the limits of safe driving by taking into account the data of other traffic participants. For example, it evaluates driving ability based on the movements of pedestrians and the behavior of cyclists. In this way, by taking into account the data of other traffic participants, the limits of safe driving can be assessed more accurately.

[0038] The proposal unit can use the generation AI to consider past driving history and family opinions when proposing license surrender based on the elderly person's driving data. For example, the proposal unit builds a system in which the generation AI analyzes the elderly person's driving data and makes a license surrender proposal taking into account past driving history and family opinions. For example, the generation AI collects family opinions and reflects them in an evaluation of driving ability. For example, the generation AI evaluates changes in driving ability based on past driving history and makes a license surrender proposal. The generation AI can also make a license surrender proposal taking family opinions into consideration. For example, the generation AI collects family opinions through questionnaires or interviews and reflects them in the proposal. In this way, a more appropriate license surrender proposal can be made by taking into account past driving history and family opinions.

[0039] The proposal unit allows the generation AI to take into account the elderly person's living environment and public transportation usage status when proposing license surrender. For example, the proposal unit builds a system in which the generation AI analyzes the elderly person's driving data and makes license surrender proposals taking into account the living environment (such as public transportation usage status). For example, it evaluates the accessibility of public transportation. For example, the generation AI proposes means of transportation after license surrender based on the elderly person's living environment. The generation AI can also make license surrender proposals taking into account the public transportation usage status. For example, it evaluates the frequency of public transportation use and ease of access and reflects this in the proposal. In this way, by taking the living environment into consideration, it is possible to make more realistic and acceptable license surrender proposals.

[0040] The suggestion unit can use the generation AI to suggest alternative means of transportation by taking into account the hobbies and interests of the elderly. For example, the suggestion unit builds a system in which the generation AI analyzes driving data of the elderly and suggests alternative means of transportation by taking into account the hobbies and interests. For example, it suggests using public transportation related to hobbies. The generation AI suggests alternative means of transportation based on the hobbies and interests of the elderly. The generation AI can also suggest transportation options according to hobbies and interests. For example, it suggests transportation related to sports or music. In this way, by taking hobbies and interests into account, it is possible to suggest alternative means of transportation that are more acceptable.

[0041] The proposal unit can use the generation AI to propose a life support plan after the elderly person surrenders their license based on their driving data. For example, the proposal unit builds a system in which the generation AI analyzes the driving data of the elderly person and proposes a life support plan after the elderly person surrenders their license. For example, it proposes how to use public transportation and life support services. The generation AI proposes a life support plan based on the elderly person's living environment, for example. The generation AI can also propose a life support plan after the elderly person surrenders their license. For example, it proposes shopping assistance and medical support. In this way, by proposing a life support plan after the elderly person surrenders their license, it is possible to maintain the quality of life of the elderly person.

[0042] The proposal unit uses the generative AI to develop a traffic safety education program based on elderly driving data, thereby improving traffic safety throughout society. For example, the proposal unit builds a system in which the generative AI analyzes elderly driving data and develops a traffic safety education program. For example, it designs educational content based on patterns of driving errors. The generative AI develops a traffic safety education program based on elderly driving data. The generative AI can also improve traffic safety throughout society through the traffic safety education program. For example, it designs and implements educational content based on patterns of driving errors. In this way, the development of a traffic safety education program can improve traffic safety throughout society.

[0043] The proposal unit uses the generation AI to propose preventive measures for traffic accidents based on driving data of elderly people, thereby ensuring traffic safety throughout society. For example, the proposal unit constructs a system in which the generation AI analyzes driving data of elderly people and proposes preventive measures for traffic accidents. For example, it designs preventive measures based on patterns of driving errors. For example, the generation AI proposes preventive measures for traffic accidents based on driving data of elderly people. The generation AI can also ensure traffic safety throughout society through preventive measures for traffic accidents. For example, it designs and implements preventive measures based on patterns of driving errors. In this way, it is possible to ensure traffic safety throughout society by proposing preventive measures for traffic accidents.

[0044] The proposal unit can use the generation AI to propose traffic safety measures for each region based on the driving data of elderly people. For example, the proposal unit constructs a system in which the generation AI analyzes the driving data of elderly people and proposes traffic safety measures for each region. For example, it designs measures that take into account the traffic conditions in the region. For example, the generation AI proposes traffic safety measures for each region based on the driving data of elderly people. The generation AI can also propose traffic safety measures that are tailored to the characteristics of the region. For example, it designs measures that take into account traffic problems specific to the region. In this way, by proposing traffic safety measures for each region, it is possible to implement traffic safety measures that are tailored to the characteristics of the region.

[0045] The proposal unit can use the generation AI to conduct traffic safety awareness activities based on driving data of elderly people. For example, the proposal unit constructs a system in which the generation AI analyzes driving data of elderly people and conducts traffic safety awareness activities. For example, the generation AI designs awareness content based on patterns of driving mistakes. For example, the generation AI conducts traffic safety awareness activities based on driving data of elderly people. The generation AI can also raise traffic safety awareness in society as a whole through awareness activities. For example, the generation AI designs and implements awareness content based on patterns of driving mistakes. In this way, traffic safety awareness activities can be conducted to raise traffic safety awareness in society as a whole.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The driving data collection unit can also collect data on the vehicle's internal environment. For example, it can collect data such as temperature, humidity, and volume inside the vehicle to evaluate the driver's comfort. It can also monitor carbon dioxide concentrations and volatile organic compound (VOC) levels using air quality sensors inside the vehicle. This allows it to identify factors affecting the driver's health and concentration and take appropriate measures. For example, if the temperature inside the vehicle is too high, it can suggest automatic adjustment of the air conditioner. Furthermore, if the carbon dioxide concentration is high, it can display a warning urging the driver to open the windows. This improves driver comfort and safety.

[0048] The driving data collection unit can also collect vehicle maintenance data. For example, it collects data such as engine condition, tire wear, and remaining brake pad wear, and evaluates when vehicle maintenance is necessary. It can also link with the vehicle's diagnostic system to detect signs of malfunction. This makes it possible to suggest appropriate maintenance to the driver and maintain vehicle safety. For example, if a tire is showing signs of wear, it can suggest that it should be replaced. Furthermore, if an engine abnormality is detected, it can display a warning urging early repair. This allows for appropriate vehicle maintenance and supports safe driving.

[0049] The driving data collection unit can collect the driver's sleep data and evaluate drowsiness while driving. For example, a wearable device can be used to monitor the driver's sleep time and sleep quality to assess the risk of drowsiness while driving. It can also analyze the frequency of blinking and eye movements while driving to detect signs of drowsiness. This can suggest appropriate breaks to the driver and prevent accidents caused by drowsiness. For example, if lack of sleep is detected, the system can encourage the driver to take sufficient rest before driving. Furthermore, if signs of drowsiness are detected while driving, a warning can be displayed to advise the driver to take a break. This can prevent drowsiness while driving and support safe driving.

[0050] The driving data collection unit can collect the driver's dietary data and evaluate factors that affect concentration and reaction time while driving. For example, it can record the contents and times of meals eaten by the driver and predict fluctuations in blood sugar levels. It can also evaluate the nutritional balance of meals and identify factors that affect concentration and reaction time while driving. This makes it possible to provide the driver with appropriate dietary suggestions and support safe driving. For example, if the contents of a meal are unbalanced, it can suggest a balanced meal. It can also encourage the driver to take a break to digest a high-calorie meal before driving. This helps maintain concentration and reaction time while driving and supports safe driving.

[0051] The driving data collection unit can collect the driver's exercise data and evaluate their physical strength and fatigue level while driving. For example, a wearable device can be used to monitor the driver's exercise volume and heart rate to evaluate their physical strength and fatigue level while driving. The exercise data can also be used to predict fluctuations in the driver's physical strength and suggest appropriate breaks. This makes it possible to suggest appropriate exercise and rest to the driver and manage their physical strength and fatigue level. For example, if a lack of exercise is detected, the system can encourage the driver to do some light exercise before driving. Furthermore, if signs of fatigue are detected while driving, a warning can be displayed to advise them to take a break. This makes it possible to manage physical strength and fatigue level while driving and support safe driving.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The driving data collection unit collects driving data of the elderly person. For example, data such as driving speed, frequency of brake use, and steering operation is collected. The driving data collection unit can also collect driving data using sensors and cameras mounted on the vehicle. For example, data is collected using the vehicle's speed sensor and brake sensor. Step 2: The analysis unit analyzes the collected driving data. For example, it may analyze the driving data using statistical analysis of the data or machine learning algorithms. The analysis unit may also analyze the driving data using a generation AI. For example, the generation AI may analyze driving patterns based on the driving data. Step 3: The evaluation unit evaluates the limits of safe driving based on the analyzed data. For example, it evaluates the limits of safe driving based on reaction time or the frequency of driving errors. The evaluation unit can also use generative AI to evaluate the limits of safe driving. For example, the generative AI identifies the limits of safe driving based on driving data. Step 4: The suggestion unit suggests surrendering the license based on the evaluation results. For example, the generation AI might suggest, "Based on your recent driving data, it is becoming more difficult for you to drive safely. Why don't you consider returning your license?" The suggestion unit can also encourage the elderly person to return their license using appropriate words while protecting their self-esteem. For example, it might suggest, "While respecting your driving experience to date, consider returning your license for safety reasons."

[0054] (Example 2) The driving data analysis system according to an embodiment of the present invention automatically collects driving data from elderly people, analyzes it using a generation AI, evaluates the limits of safe driving, and suggests that the elderly person surrender their license. As a result, the driving data analysis system can analyze the driving data of elderly people in detail and suggest that the elderly person surrender their license at an appropriate time when they reach the limits of safe driving.

[0055] A driving data analysis system according to an embodiment includes a driving data collection unit, an analysis unit, an evaluation unit, and a proposal unit. The driving data collection unit collects driving data of elderly drivers. For example, it collects data such as driving speed, frequency of brake use, and steering operation. The driving data collection unit can also collect driving data using sensors and cameras mounted on vehicles. For example, it collects data using the vehicle's speed sensor and brake sensor. The analysis unit analyzes the collected driving data. For example, it analyzes the driving data using statistical analysis of data or machine learning algorithms. The analysis unit can also analyze the driving data using a generation AI. For example, the generation AI analyzes driving patterns based on the driving data. The evaluation unit evaluates the safe driving limits based on the analyzed data. For example, it evaluates the safe driving limits based on reaction time and the frequency of driving errors. The evaluation unit can also evaluate the safe driving limits using the generation AI. For example, the generation AI identifies the safe driving limits based on the driving data. The proposal unit proposes surrendering the driver's license based on the evaluation results. For example, the generation AI might suggest, "Based on your recent driving data, it's becoming more difficult to drive safely. Perhaps you should consider returning your license." The suggestion unit can also encourage elderly people to return their licenses using appropriate words while protecting their self-esteem. For example, it might suggest, "While respecting your driving experience to date, consider returning your license for safety reasons." This allows the driving data analysis system to analyze elderly people's driving data in detail and suggest returning their licenses at the appropriate time when they have reached the limit of safe driving. For example, if the generation AI can suggest returning their licenses while protecting their self-esteem, it will be easier for elderly people to accept the suggestion.

[0056] The driving data collection unit collects the elderly person's driving data as well as health data such as heart rate and blood pressure in real time, which the generation AI can then comprehensively analyze. The driving data collection unit, for example, builds a system that collects health data such as heart rate and blood pressure in real time along with the elderly person's driving data. For example, health data is obtained using a wearable device while driving, and the generation AI analyzes that data. The generation AI, for example, analyzes heart rate fluctuations and blood pressure changes to evaluate stress and fatigue levels while driving. The generation AI can also perform an integrated analysis of driving data and health data to evaluate driving ability. For example, it can identify a decline in driving ability based on an increase in heart rate or blood pressure fluctuations while driving. This enables a more accurate evaluation of driving ability through an integrated analysis of driving data and health data.

[0057] The driving data collection unit can analyze facial expressions and eye movements using an in-car camera and facial recognition technology. The driving data collection unit, for example, installs an in-car camera and builds a system that uses facial recognition technology to analyze the facial expressions and eye movements of elderly people in real time. For example, it can evaluate the level of concentration and attention while driving. For example, facial recognition technology uses deep learning to extract facial feature points and analyze facial expressions and eye movements. In addition, facial recognition technology can also evaluate the emotional state while driving based on changes in facial expressions and eye movements while driving. For example, it can identify tension and fatigue while driving. As a result, facial recognition technology can be used to evaluate the level of concentration and attention while driving.

[0058] The driving data collection unit can use the emotion estimation function to analyze the emotional state of elderly people while driving and evaluate the level of stress and anxiety. The driving data collection unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of elderly people while driving in real time. For example, the emotion estimation function analyzes facial expressions and voice tone to evaluate the level of stress and anxiety. For example, the emotion estimation function uses an expression analysis algorithm to analyze changes in facial expressions and evaluate the emotional state. The emotion estimation function can also use voice analysis technology to analyze the tone and speed of voice to evaluate the emotional state. For example, the level of stress and anxiety can be identified based on changes in voice tone and fluctuations in speed. In this way, the emotion estimation function can be used to evaluate the emotional state while driving and understand the level of stress and anxiety.

[0059] The driving data collection unit can also use drones to simultaneously collect data on the vehicle's external environment. The driving data collection unit uses, for example, drones to build a system that collects data on the vehicle's external environment in real time. For example, it analyzes road conditions and traffic volume and integrates the data with driving data. The drone is equipped with, for example, a high-resolution camera to capture images of road conditions and traffic conditions. The drone can also collect data in real time, which can be analyzed by the generation AI. For example, it can identify road congestion and the occurrence of traffic accidents. This makes it possible to use drones to collect data on the vehicle's external environment in real time and analyze it in an integrated manner with driving data.

[0060] The driving data collection unit can compare the driving data of an elderly person with the data of other elderly people to evaluate their relative driving ability. The driving data collection unit, for example, builds a system that collects driving data of elderly people and compares it with the data of other elderly people. For example, it performs a relative evaluation of driving ability. The generation AI, for example, calculates the average value and standard deviation of driving ability based on the driving data of other elderly people and uses it as a comparison target. The generation AI can also perform statistical analysis of the driving data to evaluate relative driving ability. For example, it performs a relative evaluation of driving ability based on reaction time and frequency of driving errors. This makes it possible to evaluate relative driving ability by comparing it with the data of other elderly people.

[0061] The driving data collection unit uses the emotion estimation function to monitor the emotional state of elderly people while driving in real time and provide driving assistance according to their emotions. The driving data collection unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of elderly people while driving in real time. For example, it detects stress and anxiety and provides driving assistance. The emotion estimation function, for example, uses an expression analysis algorithm to analyze changes in facial expressions and evaluate the emotional state. The emotion estimation function can also use voice analysis technology to analyze the tone and speed of voice and evaluate the emotional state. For example, it can identify the degree of stress or anxiety based on changes in voice tone and fluctuations in speed. The driving assistance, for example, builds a system that issues warnings according to the emotional state. For example, if stress increases, a warning urging the driver to take a break is displayed. In this way, the emotion estimation function can be used to provide driving assistance according to the emotional state while driving.

[0062] The evaluation unit allows the generation AI to perform individual driving simulations based on the driving data of the elderly person and recreate limit situations. The evaluation unit, for example, builds a system in which the generation AI performs individual driving simulations based on the driving data of the elderly person. For example, it recreates limit situations and evaluates driving ability. The generation AI, for example, builds a simulation environment based on the driving data and performs a driving simulation. The generation AI can also recreate limit situations through the driving simulation and evaluate driving ability. For example, it identifies limit situations based on delays in reaction time and the frequency of driving errors. In this way, limit situations can be recreated and driving ability can be evaluated by performing individual driving simulations.

[0063] In analyzing the driving data, the evaluation unit can refer to past accident data to identify similar risky behaviors. The evaluation unit, for example, analyzes driving data of elderly people and builds a system that identifies similar risky behaviors by referring to past accident data. For example, it analyzes patterns of driving errors. The generation AI, for example, identifies patterns of risky behavior based on past accident data and compares it with the driving data. The generation AI can also perform statistical analysis of the driving data to identify similar risky behaviors. For example, it identifies risky behaviors based on the frequency of sudden braking and sudden steering. This makes it possible to identify similar risky behaviors and evaluate driving ability by referring to past accident data.

[0064] The evaluation unit can use the emotion estimation function to analyze emotional changes in elderly people while driving and evaluate their emotional limits. The evaluation unit, for example, uses the emotion estimation function to build a system that analyzes emotional changes in elderly people while driving in real time. For example, it evaluates the level of stress and anxiety. The emotion estimation function, for example, uses an expression analysis algorithm to analyze changes in facial expressions and evaluate the emotional state. The emotion estimation function can also use voice analysis technology to analyze the tone and speed of voice and evaluate the emotional state. For example, it can identify the level of stress and anxiety based on changes in voice tone and fluctuations in speed. In this way, the emotion estimation function can be used to analyze emotional changes while driving and evaluate the emotional limits.

[0065] The evaluation unit can compare the driving data of elderly people with data under different weather conditions and time periods, and evaluate the critical situation from multiple angles. The evaluation unit, for example, builds a system that compares the driving data of elderly people with data under different weather conditions and time periods. For example, it analyzes driving data in the rain or at night and evaluates the critical situation. The generation AI can evaluate fluctuations in driving ability based on driving data under different weather conditions and time periods. The generation AI can also perform statistical analysis of the driving data and evaluate the critical situation from multiple angles. For example, it can identify the critical situation based on delayed reaction time in the rain or reduced visibility at night. This makes it possible to evaluate the critical situation from multiple angles by comparing it with data under different weather conditions and time periods.

[0066] The evaluation unit can also take into account data from other traffic participants when assessing the limits of safe driving. For example, the evaluation unit collects data from other traffic participants (pedestrians, cyclists, etc.) along with driving data from elderly people, and builds a system in which the generation AI performs comprehensive analysis. For example, it evaluates the risk of traffic accidents. For example, the generation AI analyzes traffic conditions based on the data from other traffic participants and integrates it with the driving data. The generation AI can also assess the limits of safe driving by taking into account the data of other traffic participants. For example, it evaluates driving ability based on the movements of pedestrians and the behavior of cyclists. In this way, by taking into account the data of other traffic participants, the limits of safe driving can be assessed more accurately.

[0067] The evaluation unit can use the emotion estimation function to evaluate the emotional state of an elderly person while driving in real time and perform a limit evaluation based on the emotion. The evaluation unit, for example, uses the emotion estimation function to build a system that evaluates the emotional state of an elderly person while driving in real time. For example, the level of stress or anxiety is evaluated. The emotion estimation function, for example, uses an expression analysis algorithm to analyze changes in facial expression and evaluate the emotional state. The emotion estimation function can also use voice analysis technology to analyze the tone and speed of voice and evaluate the emotional state. For example, the level of stress or anxiety is identified based on changes in voice tone and fluctuations in speed. As a result, the emotion estimation function can be used to perform a limit evaluation based on the emotional state while driving.

[0068] The proposal unit can use the generation AI to consider past driving history and family opinions when proposing license surrender based on the elderly person's driving data. For example, the proposal unit builds a system in which the generation AI analyzes the elderly person's driving data and makes a license surrender proposal taking into account past driving history and family opinions. For example, the generation AI collects family opinions and reflects them in an evaluation of driving ability. For example, the generation AI evaluates changes in driving ability based on past driving history and makes a license surrender proposal. The generation AI can also make a license surrender proposal taking family opinions into consideration. For example, the generation AI collects family opinions through questionnaires or interviews and reflects them in the proposal. In this way, a more appropriate license surrender proposal can be made by taking into account past driving history and family opinions.

[0069] The proposal unit allows the generation AI to take into account the elderly person's living environment and public transportation usage status when proposing license surrender. For example, the proposal unit builds a system in which the generation AI analyzes the elderly person's driving data and makes license surrender proposals taking into account the living environment (such as public transportation usage status). For example, it evaluates the accessibility of public transportation. For example, the generation AI proposes means of transportation after license surrender based on the elderly person's living environment. The generation AI can also make license surrender proposals taking into account the public transportation usage status. For example, it evaluates the frequency of public transportation use and ease of access and reflects this in the proposal. In this way, by taking the living environment into consideration, it is possible to make more realistic and acceptable license surrender proposals.

[0070] The suggestion unit can use the emotion estimation function to suggest license surrender at the optimal timing depending on the elderly person's emotional state. The suggestion unit, for example, uses the emotion estimation function to analyze the elderly person's emotional state in real time and build a system that suggests license surrender at the optimal timing. For example, the suggestion unit makes a suggestion to surrender the license based on emotional data. The emotion estimation function, for example, uses an expression analysis algorithm to analyze changes in facial expression and evaluate the emotional state. The emotion estimation function can also use voice analysis technology to analyze the tone and speed of speech and evaluate the emotional state. For example, the degree of stress and anxiety can be identified based on changes in voice tone and fluctuations in speed. This makes it easier for elderly people to accept the suggestion by suggesting license surrender at the optimal timing depending on their emotional state.

[0071] The suggestion unit can use the generation AI to suggest alternative means of transportation by taking into account the hobbies and interests of the elderly. For example, the suggestion unit builds a system in which the generation AI analyzes driving data of the elderly and suggests alternative means of transportation by taking into account the hobbies and interests. For example, it suggests using public transportation related to hobbies. The generation AI suggests alternative means of transportation based on the hobbies and interests of the elderly. The generation AI can also suggest transportation options according to hobbies and interests. For example, it suggests transportation related to sports or music. In this way, by taking hobbies and interests into account, it is possible to suggest alternative means of transportation that are more acceptable.

[0072] The proposal unit can use the generation AI to propose a life support plan after the elderly person surrenders their license based on their driving data. For example, the proposal unit builds a system in which the generation AI analyzes the driving data of the elderly person and proposes a life support plan after the elderly person surrenders their license. For example, it proposes how to use public transportation and life support services. The generation AI proposes a life support plan based on the elderly person's living environment, for example. The generation AI can also propose a life support plan after the elderly person surrenders their license. For example, it proposes shopping assistance and medical support. In this way, by proposing a life support plan after the elderly person surrenders their license, it is possible to maintain the quality of life of the elderly person.

[0073] The suggestion unit can use the emotion estimation function to monitor the emotional state of the elderly person in real time and make a suggestion to surrender the license according to the emotion. The suggestion unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the elderly person in real time and makes a suggestion to surrender the license according to the emotion. For example, the suggestion unit makes a suggestion to surrender the license based on the emotion data. The emotion estimation function, for example, uses an expression analysis algorithm to analyze changes in facial expression and evaluate the emotional state. The emotion estimation function can also use voice analysis technology to analyze the tone and speed of the voice and evaluate the emotional state. For example, the degree of stress or anxiety can be identified based on changes in tone and fluctuations in speed of the voice. In this way, by monitoring the emotional state in real time, a suggestion to surrender the license according to the emotion can be made.

[0074] The suggestion unit can use the generation AI to provide positive feedback based on the elderly person's driving data and increase their self-esteem. For example, the suggestion unit constructs a system in which the generation AI analyzes the elderly person's driving data and provides positive feedback. For example, it emphasizes successful driving experiences to increase self-esteem. For example, the generation AI extracts successful driving experiences based on the elderly person's driving data and provides feedback. The generation AI can also increase the elderly person's self-esteem through positive feedback. For example, it provides a message praising successful driving experiences. In this way, the self-esteem of the elderly can be increased by providing positive feedback.

[0075] The suggestion unit can use the generation AI to look back on past successful experiences based on the elderly person's driving history and make suggestions to protect their self-esteem. For example, the suggestion unit builds a system in which the generation AI analyzes the elderly person's driving history and makes suggestions to look back on past successful experiences. For example, it emphasizes successful driving experiences to protect self-esteem. For example, the generation AI extracts past successful experiences based on the elderly person's driving history and reflects them in the content of the suggestions. The generation AI can also protect the elderly person's self-esteem by looking back on past successful experiences. For example, it provides a message praising successful driving experiences. In this way, the elderly person's self-esteem can be protected by looking back on past successful experiences.

[0076] The suggestion unit can use the emotion estimation function to analyze the emotional state of the elderly person and suggest that they surrender their driver's license using appropriate words according to their emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the emotional state of the elderly person in real time and build a system that suggests that they surrender their driver's license using appropriate words. For example, the suggestion unit makes a suggestion to surrender their driver's license based on emotional data. The emotion estimation function, for example, uses an expression analysis algorithm to analyze changes in facial expression and evaluate the emotional state. The emotion estimation function can also use voice analysis technology to analyze the tone and speed of the voice and evaluate the emotional state. For example, the degree of stress or anxiety can be identified based on changes in voice tone and fluctuations in speed. This makes it easier for the elderly person to accept the suggestion by using appropriate words according to their emotional state.

[0077] The suggestion unit uses the generation AI to suggest successful experiences in activities other than driving based on the elderly person's driving data, thereby increasing self-esteem. For example, the suggestion unit constructs a system in which the generation AI analyzes the elderly person's driving data and suggests successful experiences in activities other than driving. For example, it suggests hobbies or volunteer activities, thereby increasing self-esteem. For example, the generation AI extracts successful experiences in activities other than driving based on the elderly person's driving data and reflects them in the suggestions. The generation AI can also increase the elderly person's self-esteem by suggesting successful experiences in activities other than driving. For example, it provides messages praising hobbies and volunteer activities. In this way, it is possible to increase the elderly person's self-esteem by suggesting successful experiences in activities other than driving.

[0078] The suggestion unit can use the generation AI to suggest new hobbies and activities after the elderly person surrenders their license based on their driving data. For example, the suggestion unit builds a system in which the generation AI analyzes the elderly person's driving data and suggests new hobbies and activities after the elderly person surrenders their license. For example, it suggests new hobbies or volunteer activities. For example, the generation AI suggests new hobbies and activities based on the elderly person's driving data. The generation AI can also suggest new hobbies and activities to maintain quality of life after the elderly person surrenders their license. For example, it suggests sports, art, and community activities. In this way, by suggesting new hobbies and activities after the elderly person surrenders their license, it is possible to maintain the quality of life of the elderly person and increase their self-esteem.

[0079] The suggestion unit can use the emotion estimation function to monitor the emotional state of the elderly person in real time and make suggestions to protect self-esteem according to the emotions. The suggestion unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the elderly person in real time and makes suggestions to protect self-esteem according to the emotions. For example, the suggestion unit makes suggestions based on emotional data. The emotion estimation function, for example, uses an expression analysis algorithm to analyze changes in facial expressions and evaluate the emotional state. The emotion estimation function can also use voice analysis technology to analyze the tone and speed of voice and evaluate the emotional state. For example, the degree of stress or anxiety can be identified based on changes in tone and fluctuations in speed of voice. In this way, by monitoring the emotional state in real time, suggestions to protect self-esteem according to the emotions can be made.

[0080] The proposal unit uses the generative AI to develop a traffic safety education program based on elderly driving data, thereby improving traffic safety throughout society. For example, the proposal unit builds a system in which the generative AI analyzes elderly driving data and develops a traffic safety education program. For example, it designs educational content based on patterns of driving errors. The generative AI develops a traffic safety education program based on elderly driving data. The generative AI can also improve traffic safety throughout society through the traffic safety education program. For example, it designs and implements educational content based on patterns of driving errors. In this way, the development of a traffic safety education program can improve traffic safety throughout society.

[0081] The proposal unit uses the generation AI to propose preventive measures for traffic accidents based on driving data of elderly people, thereby ensuring traffic safety throughout society. For example, the proposal unit constructs a system in which the generation AI analyzes driving data of elderly people and proposes preventive measures for traffic accidents. For example, it designs preventive measures based on patterns of driving errors. For example, the generation AI proposes preventive measures for traffic accidents based on driving data of elderly people. The generation AI can also ensure traffic safety throughout society through preventive measures for traffic accidents. For example, it designs and implements preventive measures based on patterns of driving errors. In this way, it is possible to ensure traffic safety throughout society by proposing preventive measures for traffic accidents.

[0082] The proposal unit can use the emotion estimation function to analyze the emotional state of the elderly and propose emotion-based traffic safety measures. The proposal unit, for example, uses the emotion estimation function to analyze the emotional state of the elderly in real time and build a system that proposes emotion-based traffic safety measures. For example, the proposal unit designs measures based on emotion data. The emotion estimation function, for example, uses an expression analysis algorithm to analyze changes in facial expressions and evaluate the emotional state. The emotion estimation function can also use voice analysis technology to analyze the tone and speed of speech and evaluate the emotional state. For example, the degree of stress and anxiety can be identified based on changes in voice tone and fluctuations in speed. This makes it possible to propose traffic safety measures based on emotional states, thereby improving traffic safety throughout society.

[0083] The proposal unit can use the generation AI to propose traffic safety measures for each region based on the driving data of elderly people. For example, the proposal unit constructs a system in which the generation AI analyzes the driving data of elderly people and proposes traffic safety measures for each region. For example, it designs measures that take into account the traffic conditions in the region. For example, the generation AI proposes traffic safety measures for each region based on the driving data of elderly people. The generation AI can also propose traffic safety measures that are tailored to the characteristics of the region. For example, it designs measures that take into account traffic problems specific to the region. In this way, by proposing traffic safety measures for each region, it is possible to implement traffic safety measures that are tailored to the characteristics of the region.

[0084] The proposal unit can use the generation AI to conduct traffic safety awareness activities based on driving data of elderly people. For example, the proposal unit constructs a system in which the generation AI analyzes driving data of elderly people and conducts traffic safety awareness activities. For example, the generation AI designs awareness content based on patterns of driving mistakes. For example, the generation AI conducts traffic safety awareness activities based on driving data of elderly people. The generation AI can also raise traffic safety awareness in society as a whole through awareness activities. For example, the generation AI designs and implements awareness content based on patterns of driving mistakes. In this way, traffic safety awareness activities can be conducted to raise traffic safety awareness in society as a whole.

[0085] The proposal unit can use the emotion estimation function to monitor the emotional state of the elderly in real time and propose traffic safety measures according to the emotions. The proposal unit, for example, uses the emotion estimation function to monitor the emotional state of the elderly in real time and build a system that proposes traffic safety measures according to the emotions. For example, the proposal unit designs measures based on the emotion data. The emotion estimation function, for example, uses an expression analysis algorithm to analyze changes in facial expressions and evaluate the emotional state. The emotion estimation function can also use voice analysis technology to analyze the tone and speed of voice and evaluate the emotional state. For example, the degree of stress or anxiety can be identified based on changes in voice tone and fluctuations in speed. In this way, by monitoring the emotional state in real time, traffic safety measures according to the emotions can be proposed.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The driving data collection unit can also collect data on the vehicle's internal environment. For example, it can collect data such as temperature, humidity, and volume inside the vehicle to evaluate the driver's comfort. It can also monitor carbon dioxide concentrations and volatile organic compound (VOC) levels using air quality sensors inside the vehicle. This allows it to identify factors affecting the driver's health and concentration and take appropriate measures. For example, if the temperature inside the vehicle is too high, it can suggest automatic adjustment of the air conditioner. Furthermore, if the carbon dioxide concentration is high, it can display a warning urging the driver to open the windows. This improves driver comfort and safety.

[0088] The driving data collection unit can also collect vehicle maintenance data. For example, it collects data such as engine condition, tire wear, and remaining brake pad wear, and evaluates when vehicle maintenance is necessary. It can also link with the vehicle's diagnostic system to detect signs of malfunction. This makes it possible to suggest appropriate maintenance to the driver and maintain vehicle safety. For example, if a tire is showing signs of wear, it can suggest that it should be replaced. Furthermore, if an engine abnormality is detected, it can display a warning urging early repair. This allows for appropriate vehicle maintenance and supports safe driving.

[0089] The driving data collection unit can collect the driver's sleep data and evaluate drowsiness while driving. For example, a wearable device can be used to monitor the driver's sleep time and sleep quality to assess the risk of drowsiness while driving. It can also analyze the frequency of blinking and eye movements while driving to detect signs of drowsiness. This can suggest appropriate breaks to the driver and prevent accidents caused by drowsiness. For example, if lack of sleep is detected, the system can encourage the driver to take sufficient rest before driving. Furthermore, if signs of drowsiness are detected while driving, a warning can be displayed to advise the driver to take a break. This can prevent drowsiness while driving and support safe driving.

[0090] The driving data collection unit can collect the driver's dietary data and evaluate factors that affect concentration and reaction time while driving. For example, it can record the contents and times of meals eaten by the driver and predict fluctuations in blood sugar levels. It can also evaluate the nutritional balance of meals and identify factors that affect concentration and reaction time while driving. This makes it possible to provide the driver with appropriate dietary suggestions and support safe driving. For example, if the contents of a meal are unbalanced, it can suggest a balanced meal. It can also encourage the driver to take a break to digest a high-calorie meal before driving. This helps maintain concentration and reaction time while driving and supports safe driving.

[0091] The driving data collection unit can collect the driver's exercise data and evaluate their physical strength and fatigue level while driving. For example, a wearable device can be used to monitor the driver's exercise volume and heart rate to evaluate their physical strength and fatigue level while driving. The exercise data can also be used to predict fluctuations in the driver's physical strength and suggest appropriate breaks. This makes it possible to suggest appropriate exercise and rest to the driver and manage their physical strength and fatigue level. For example, if a lack of exercise is detected, the system can encourage the driver to do some light exercise before driving. Furthermore, if signs of fatigue are detected while driving, a warning can be displayed to advise them to take a break. This makes it possible to manage physical strength and fatigue level while driving and support safe driving.

[0092] The evaluation unit can estimate the driver's emotional state and assess risks while driving based on the estimated emotions. For example, it can detect stress or anxiety while driving and identify high-risk situations. It can also evaluate fluctuations in the driver's reaction time and judgment based on the emotional state. This makes it possible to provide appropriate advice to the driver and reduce risks. For example, if stress is increasing, it can suggest ways to relax. Also, if anxiety is high, it can encourage the driver to temporarily stop driving. In this way, it is possible to assess risks while driving based on the emotional state and support safe driving.

[0093] The evaluation unit can estimate the driver's emotional state and evaluate their concentration while driving based on the estimated emotions. For example, it can detect joy or excitement while driving and identify fluctuations in concentration. It can also evaluate fluctuations in the driver's attention based on the emotional state. This allows the system to provide appropriate advice to the driver and maintain their concentration. For example, if joy is increasing, a warning can be displayed to prevent the driver from becoming distracted. Also, if excitement is strong, it can suggest ways to calm down. This makes it possible to evaluate the driver's concentration while driving based on the emotional state and support safe driving.

[0094] The evaluation unit can estimate the driver's emotional state and evaluate their driving judgment based on the estimated emotions. For example, it can detect anger or irritation while driving and identify fluctuations in their judgment. It can also evaluate the quality of the driver's decision-making based on their emotional state. This allows the system to provide appropriate advice to the driver and help them maintain their judgment. For example, if anger is rising, it can suggest ways to calm down. Also, if irritation is strong, it can encourage the driver to temporarily stop driving. This makes it possible to evaluate the driver's driving judgment based on their emotional state and support safe driving.

[0095] The evaluation unit can estimate the driver's emotional state and evaluate the stress level while driving based on the estimated emotion. For example, it can detect tension or impatience while driving and identify fluctuations in the stress level. It can also suggest stress management methods for the driver based on the emotional state. This allows the driver to receive appropriate advice and reduce stress. For example, if tension is rising, it can suggest deep breathing or other relaxation techniques. It can also encourage the driver to temporarily stop driving if the driver is feeling very impatient. This makes it possible to evaluate the stress level while driving based on the emotional state and support safe driving.

[0096] The evaluation unit can estimate the emotional state of the driver and evaluate the level of fatigue during driving based on the estimated emotion. For example, it can detect tiredness or fatigue during driving and identify fluctuations in fatigue level. It can also suggest timing for the driver to take a break based on the emotional state. This allows appropriate advice to be provided to the driver to reduce fatigue. For example, if fatigue is increasing, it can encourage the driver to take a break. Also, if fatigue is strong, it can display a warning to temporarily suspend driving. This makes it possible to evaluate the level of fatigue during driving based on the emotional state and support safe driving.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The driving data collection unit collects driving data of the elderly person. For example, data such as driving speed, frequency of brake use, and steering operation is collected. The driving data collection unit can also collect driving data using sensors and cameras mounted on the vehicle. For example, data is collected using the vehicle's speed sensor and brake sensor. Step 2: The analysis unit analyzes the collected driving data. For example, it may analyze the driving data using statistical analysis of the data or machine learning algorithms. The analysis unit may also analyze the driving data using a generation AI. For example, the generation AI may analyze driving patterns based on the driving data. Step 3: The evaluation unit evaluates the limits of safe driving based on the analyzed data. For example, it evaluates the limits of safe driving based on reaction time or the frequency of driving errors. The evaluation unit can also use generative AI to evaluate the limits of safe driving. For example, the generative AI identifies the limits of safe driving based on driving data. Step 4: The suggestion unit suggests surrendering the license based on the evaluation results. For example, the generation AI might suggest, "Based on your recent driving data, it is becoming more difficult for you to drive safely. Why don't you consider returning your license?" The suggestion unit can also encourage the elderly person to return their license using appropriate words while protecting their self-esteem. For example, it might suggest, "While respecting your driving experience to date, consider returning your license for safety reasons."

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0101] 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.

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

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 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.

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

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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 driving data collection unit that collects driving data of elderly people; an analysis unit that analyzes the driving data collected by the driving data collection unit; an evaluation unit that evaluates the limit of safe driving based on the data analyzed by the analysis unit; a proposal unit that proposes surrendering the license based on the result of the evaluation by the evaluation unit; A system characterized by:

2. The driving data collection unit In addition to driving data of elderly people, health data such as heart rate and blood pressure will be collected in real time and analyzed comprehensively by the AI ​​generating the data.

2. The system of claim 1.

3. The driving data collection unit Drones are also used to simultaneously collect data on the vehicle's external environment.

2. The system of claim 1.

4. The evaluation unit Based on the driving data of the elderly person, the generation AI performs individual driving simulations to reproduce limit situations.

2. The system of claim 1.

5. The proposal unit Using a generation AI, the system takes into consideration the elderly person's past driving history and the opinions of their family members when proposing to surrender their license based on the elderly person's driving data.

2. The system of claim 1.

6. The proposal unit Using generative AI, positive feedback is provided based on the elderly person's driving data, enhancing their self-esteem.

2. The system of claim 1.

7. The proposal unit Using generative AI, a traffic safety education program will be developed based on the driving data of the elderly, improving traffic safety throughout society.

2. The system of claim 1.

8. The driving data collection unit Analyzing the emotional state of the elderly driver while driving and assessing their level of stress and anxiety 2. The system of claim 1.

Images