System

Abstract

An object of a system according to an embodiment is to determine the driving aptitude of an elderly person in advance and share the result with the family.SOLUTION: A system according to an embodiment includes a driving aptitude measurement unit, a data analysis unit, and an information sharing unit. A driving aptitude measuring part measures the state of repetition or response of conversation and the movement of eyes. The data analysis unit analyzes the data acquired by the driving aptitude measurement unit and determines driving aptitude. The information sharing unit shares a result of the driving aptitude determined by the data analysis unit with a family of the elderly person.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 does not adequately provide a means for determining an elderly person's driving aptitude in advance and sharing this information with their family, so there is room for improvement.

[0005] The system according to the embodiment aims to determine the driving aptitude of an elderly person in advance and share the results with their family members. [Means for solving the problem]

[0006] The system according to the embodiment includes a driving aptitude measurement unit, a data analysis unit, and an information sharing unit. The driving aptitude measurement unit measures the state of conversation repetition or response and eye movement. The data analysis unit analyzes the data acquired by the driving aptitude measurement unit and determines driving aptitude. The information sharing unit shares the results of the driving aptitude determined by the data analysis unit with the elderly person's family. [Effects of the Invention]

[0007] The system according to the embodiment can determine the driving aptitude of an elderly person in advance and share the results with family members. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The driving aptitude assessment system according to an embodiment of the present invention assesses the driving aptitude of elderly people using a smartphone app before they drive a vehicle. This system assesses driving aptitude by measuring conversation repetition, response status, and eye movements, and analyzing them with a generative AI. It also shares accident situations involving drivers with similar physical abilities and driving patterns, and shares the assessment results and driving situation before driving with the elderly person's family members, allowing them to use this information to assess their driving aptitude and, if necessary, consider surrendering their license. This allows the driving aptitude assessment system to accurately assess the elderly person's driving aptitude and share this information with family members, thereby reducing driving risks and supporting safe driving.

[0029] A driving aptitude assessment system according to an embodiment includes a driving aptitude measurement unit, a data analysis unit, and an information sharing unit. The driving aptitude measurement unit measures the elderly person's eye movements and whether they repeat or respond to conversations. For example, a smartphone app asks, "What day is it today?" and checks whether the elderly person accurately answers the question. The camera also tracks the elderly person's eye movements and measures their gaze and reaction speed. The data analysis unit analyzes the data acquired by the driving aptitude measurement unit to assess the elderly person's driving aptitude. For example, the generation AI evaluates the elderly person's driving aptitude based on the data on the elderly person's repetition and response to conversations and the eye movements. The information sharing unit shares the results of the driving aptitude assessed by the data analysis unit with the elderly person's family. For example, the information sharing unit notifies the family of the driving aptitude assessment result and the driving situation in real time via a smartphone app. This allows the driving aptitude assessment system to accurately assess the elderly person's driving aptitude and share the results with family members, thereby reducing driving risks and supporting safe driving.

[0030] In the driving aptitude measurement unit, the generation AI analyzes the tone and speed of the elderly person's voice when repeating or responding to a conversation, and can detect signs of stress or fatigue. For example, when an elderly person answers questions from an app, the generation AI analyzes the tone and speed of the voice to detect signs of stress or fatigue. For example, it detects voice tremors and delays and evaluates the stress level. In addition, when repeating a conversation, the generation AI analyzes the tone and speed of the elderly person's voice in real time to detect abnormal changes. For example, it detects slower-than-normal response speeds and unnatural tones. In addition, when responding, the generation AI analyzes the tone and speed of the elderly person's voice to detect signs of stress or fatigue. For example, it analyzes changes in voice pitch and intensity and evaluates the stress level. This detects signs of stress and fatigue in the elderly, improving the accuracy of driving aptitude assessments.

[0031] In addition to measuring eye movements, the driving aptitude measurement unit can analyze facial expressions and subtle muscle movements to detect declines in concentration and attention. In addition to measuring eye movements, the driving aptitude measurement unit, for example, uses a generation AI to analyze facial expressions and subtle muscle movements to detect declines in concentration and attention. For example, it analyzes wrinkles between the eyebrows and movements of the corners of the mouth. Furthermore, when an elderly person uses an app, the generation AI analyzes facial expressions along with eye movements to detect declines in concentration and attention. For example, it detects slowed eye movements and stiffened facial expressions. Furthermore, in addition to measuring eye movements, the generation AI analyzes subtle muscle movements of the face to detect declines in concentration and attention. For example, it analyzes muscle movements around the eyes to evaluate declines in attention. This improves the accuracy of driving aptitude assessment by detecting declines in the elderly person's concentration and attention.

[0032] The driving aptitude measurement unit can add a driving simulation function to evaluate driving aptitude in a virtual environment. The driving aptitude measurement unit adds a driving simulation function to, for example, a smartphone app, allowing elderly people to evaluate their driving aptitude in a virtual environment. For example, virtual road conditions and traffic rules are reproduced. The driving simulation function is also used to evaluate the reaction speed and judgment of elderly people when driving in a virtual environment. For example, reactions to emergency braking and lane changes during the simulation are measured. The driving simulation function is also added to the smartphone app, allowing elderly people to evaluate their driving aptitude in a virtual environment. For example, simulations of night driving and bad weather are performed to evaluate aptitude. This makes it possible to evaluate driving aptitude in a virtual environment in a more realistic manner.

[0033] The driving aptitude measurement unit can measure the elderly person's movements in daily life and evaluate their overall physical ability. The driving aptitude measurement unit, for example, uses a smartphone app to measure the elderly person's movements in daily life (e.g., walking and lifting objects) and evaluate their overall physical ability. For example, it measures walking speed and balance. It also measures movements in daily life and collects data for evaluating driving aptitude. For example, it measures lifting objects and climbing stairs to evaluate muscle strength and flexibility. It also measures the elderly person's movements in daily life and evaluates their overall physical ability. For example, it measures walking stability and reaction speed and uses the data to evaluate driving aptitude. In this way, by measuring movements in daily life, it is possible to evaluate overall physical ability and improve the accuracy of the driving aptitude evaluation.

[0034] The data analysis unit can compare past driving data with current measurement data and track changes in driving aptitude over the long term. For example, the generation AI compares past driving data with current measurement data to track changes in driving aptitude over the long term. For example, it compares past driving records with current reaction speed. Furthermore, based on the past driving data, the generation AI analyzes current measurement data to evaluate changes in driving aptitude. For example, it compares past driving patterns with current attention. Furthermore, the generation AI compares past driving data with current measurement data to track changes in driving aptitude over the long term. For example, it compares past accident history with current driving aptitude. This allows for more accurate evaluation of driving aptitude by tracking changes in driving aptitude over the long term.

[0035] The data analysis unit can suggest an individual training program based on the data on conversation repetition and responses, thereby improving driving aptitude. In the data analysis unit, for example, the generation AI analyzes the data on conversation repetition and responses and suggests an individual training program for the elderly. For example, training to improve memory and reaction speed is provided. Furthermore, the generation AI suggests a training program suitable for the elderly based on the data on conversation repetition and responses. For example, exercises to improve attention and concentration are provided. Furthermore, the generation AI analyzes the data on conversation repetition and responses and suggests an individual training program. For example, methods of stress management and relaxation are provided. In this way, by suggesting an individual training program, it is possible to expect improvement in driving aptitude.

[0036] The data analysis unit can propose individual driving assist functions (for example, speed limits or automatic braking) based on the driving aptitude evaluation results. For example, the data analysis unit allows the generation AI to propose individual driving assist functions for elderly people based on the driving aptitude evaluation results. For example, it proposes settings for speed limits or automatic braking. Furthermore, based on the driving aptitude evaluation results, the generation AI proposes driving assist functions suitable for elderly people. For example, it proposes lane keeping assist or collision avoidance systems. Furthermore, based on the driving aptitude evaluation results, the generation AI proposes individual driving assist functions. For example, it proposes night driving assistance or a fatigue detection system. In this way, by proposing individual driving assist functions, it is possible to support elderly driving and improve safety.

[0037] The data analysis unit can integrate the conversation repetition and response data with other health data (e.g., heart rate and blood pressure) to evaluate overall health status. The data analysis unit, for example, integrates the conversation repetition and response data with other health data such as heart rate and blood pressure to evaluate overall health status. For example, it evaluates stress levels and fatigue levels. The generation AI also integrates the conversation repetition and response data with other health data to evaluate overall health status. For example, it analyzes changes in heart rate variability and blood pressure. It also integrates the conversation repetition and response data with other health data to evaluate overall health status. For example, it takes into account exercise volume and sleep patterns in the evaluation. This improves the accuracy of driving aptitude assessment by evaluating overall health status.

[0038] The information sharing unit can analyze the accident situations of similar drivers and evaluate the risk during specific time periods and weather conditions. For example, the generation AI in the information sharing unit analyzes the accident situations of similar drivers and evaluates the risk during specific time periods. For example, it evaluates the risk of accidents at night or early in the morning. Furthermore, based on the accident data of similar drivers, the generation AI evaluates the risk during specific weather conditions. For example, it analyzes the risk of accidents on rainy or snowy days. Furthermore, the generation AI analyzes the accident situations of similar drivers and evaluates the risk during specific time periods and weather conditions. For example, it evaluates the risk during rush hour or bad weather. In this way, by evaluating the risk during specific time periods and weather conditions, it is possible to gain a more specific understanding of the risks of driving.

[0039] The information sharing unit can provide specific driving advice to elderly people based on accident data of similar drivers. For example, the generation AI analyzes accident data of similar drivers and provides specific driving advice to elderly people. For example, it may advise on points to be careful of at specific intersections. Furthermore, based on the accident data of similar drivers, the generation AI provides elderly people with points to be careful of while driving. For example, it may advise on specific speed limits and maintaining a safe distance between vehicles. Furthermore, based on the accident data of similar drivers, the generation AI provides specific driving advice to elderly people. For example, it may advise on how to drive in specific road conditions. In this way, by providing specific driving advice, elderly people's driving risks are reduced.

[0040] The information sharing unit can integrate accident situations of similar drivers with geographical information to assess risk in specific areas. For example, the generation AI integrates accident situations of similar drivers with geographical information to assess risk in specific areas. For example, it analyzes the accident risk at specific intersections or roads. It also integrates accident data of similar drivers with geographical information to assess risk in specific areas. For example, it evaluates accident risk in urban and suburban areas. It also integrates accident situations of similar drivers with geographical information to assess risk in specific areas. For example, it analyzes the frequency and causes of accidents in specific areas. This allows for a more specific understanding of driving risks by assessing risk in specific areas.

[0041] The information sharing unit can notify elderly people of important points to pay attention to while driving in real time based on accident data of similar drivers. For example, the generation AI in the information sharing unit notifies elderly people of important points to pay attention to while driving in real time based on accident data of similar drivers. For example, it notifies them of important points to pay attention to at specific intersections. Furthermore, the generation AI notifies elderly people of important points to pay attention to while driving in real time based on accident data of similar drivers. For example, it notifies them of specific speed limits and maintaining a safe distance between vehicles. Furthermore, the generation AI notifies elderly people of important points to pay attention to while driving in real time based on accident data of similar drivers. For example, it notifies them of how to drive in specific road conditions. In this way, by notifying them of important points to pay attention to in real time, risks while driving are reduced.

[0042] The information sharing unit can evaluate the risk that a specific driving pattern will lead to an accident based on accident data of similar drivers. In the information sharing unit, for example, the generation AI analyzes accident data of similar drivers and evaluates the risk that a specific driving pattern will lead to an accident. For example, it evaluates the frequency of sudden braking and sudden steering. Furthermore, based on accident data of similar drivers, the generation AI evaluates the risk that a specific driving pattern will lead to an accident. For example, it evaluates speeding and keeping an insufficient distance between vehicles. Furthermore, the generation AI analyzes accident data of similar drivers and evaluates the risk that a specific driving pattern will lead to an accident. For example, it evaluates driving patterns at night or in bad weather. In this way, by evaluating the risk that a specific driving pattern will lead to an accident, it is possible to understand the risks of driving more specifically.

[0043] The information sharing unit can analyze the accident situations of similar drivers and evaluate the impact that specific health conditions (for example, impaired vision or slower reaction times) have on accidents. In the information sharing unit, for example, the generation AI analyzes the accident situations of similar drivers and evaluates the impact that specific health conditions have on accidents. For example, it evaluates the impact that impaired vision or slower reaction times have on accident risk. Furthermore, based on accident data of similar drivers, the generation AI evaluates the impact that specific health conditions have on accidents. For example, it evaluates the impact that impaired hearing or reduced muscle strength has on accident risk. Furthermore, the generation AI analyzes the accident situations of similar drivers and evaluates the impact that specific health conditions have on accidents. For example, it evaluates the impact that impaired cognitive function or chronic illnesses have on accident risk. In this way, by evaluating the impact that specific health conditions have on accidents, it is possible to understand the risks of driving more specifically.

[0044] The information sharing unit can integrate accident data of similar drivers with other traffic data (for example, traffic volume and road conditions) to perform a comprehensive risk assessment. In the information sharing unit, for example, the generation AI integrates accident data of similar drivers with other traffic data to perform a comprehensive risk assessment. For example, it evaluates risk by taking traffic volume and road conditions into consideration. It also integrates accident data of similar drivers with other traffic data to perform a comprehensive risk assessment. For example, it evaluates the congestion status and accident frequency of specific roads. It also integrates accident data of similar drivers with other traffic data to perform a comprehensive risk assessment. For example, it evaluates the risk in specific areas or time periods. This allows for a more specific understanding of driving risks by performing a comprehensive risk assessment.

[0045] The information sharing unit can notify elderly people in advance of points to be careful of while driving based on accident data of similar drivers. For example, the generation AI in the information sharing unit notifies elderly people in advance of points to be careful of while driving based on accident data of similar drivers. For example, it notifies them of points to be careful of at specific intersections. Furthermore, based on accident data of similar drivers, the generation AI notifies elderly people in advance of points to be careful of while driving. For example, it notifies them of specific speed limits and maintaining a safe distance between vehicles. Furthermore, based on accident data of similar drivers, the generation AI notifies elderly people in advance of points to be careful of while driving. For example, it notifies them of how to drive in specific road conditions. In this way, by notifying them of points to be careful of in advance, risks while driving are reduced.

[0046] The information sharing unit can customize the driving aptitude evaluation results individually for each family member and provide them in an easy-to-understand format. For example, the generation AI in the information sharing unit customizes the driving aptitude evaluation results individually for each family member and provides them in an easy-to-understand format. For example, the evaluation results are visually displayed in graphs or charts. The generation AI also customizes the driving aptitude evaluation results individually for each family member and provides them in an easy-to-understand format. For example, the evaluation results are provided in a concise report format. The generation AI also customizes the driving aptitude evaluation results individually for each family member and provides them in an easy-to-understand format. For example, the evaluation results are explained using video or audio. In this way, by providing the results in an easy-to-understand format that is individually customized, it becomes easier for family members to understand the driving aptitude of an elderly person.

[0047] When sharing information with family members, the information sharing unit can provide specific advice taking into account the elderly person's driving history and health condition. In the information sharing unit, for example, the generation AI takes into account the elderly person's driving history and health condition and provides specific advice to family members. For example, it may advise on times and situations when driving should be avoided. In addition, when sharing information with family members, the generation AI takes into account the elderly person's driving history and health condition and provides specific advice. For example, it may suggest points to be careful of while driving and safety measures. In addition, the generation AI takes into account the elderly person's driving history and health condition and provides specific advice to family members. For example, it may suggest training or rehabilitation to improve driving aptitude. In this way, providing specific advice taking into account the elderly person's driving history and health condition makes it easier for family members to understand the elderly person's driving aptitude.

[0048] The information sharing unit can share information with family members not only through a smartphone app but also through other devices (for example, a smartwatch or smart speaker). For example, the information sharing unit can share information with family members not only through a smartphone app but also through a smartwatch. For example, the driving aptitude evaluation results can be notified to the smartwatch. The generation AI can also share information with family members through a smart speaker. For example, the driving aptitude evaluation results can be notified by voice. Information can also be shared with family members not only through a smartphone app but also through other devices. For example, the driving aptitude evaluation results can be notified to a smart home device. This makes it easier for family members to understand the driving aptitude of elderly people by sharing information through multiple devices.

[0049] The information sharing unit can collect opinions and feedback from other family members when sharing information with family members, and use this as comprehensive information for making a decision. For example, when the generation AI shares information with family members, the information sharing unit collects opinions and feedback from other family members and uses this as comprehensive information for making a decision. For example, the information sharing unit aggregates the opinions of all family members and shares the evaluation results. Also, when sharing information with family members, the generation AI collects feedback from other family members and uses this as comprehensive information for making a decision. For example, the evaluation results of driving aptitude are adjusted based on the opinions of all family members. Also, when sharing information with family members, the generation AI collects opinions and feedback from other family members and uses this as comprehensive information for making a decision. For example, it provides evaluation results that reflect the opinions of all family members. This makes it easier to provide comprehensive information for making a decision by collecting the opinions and feedback of other family members.

[0050] The information sharing unit can provide specific driving advice to family members based on the pre-boarding assessment results. For example, the information sharing unit provides specific driving advice to family members based on the pre-boarding assessment results of the generation AI. For example, it may advise on times and situations when driving should be avoided. Furthermore, based on the pre-boarding assessment results, the generation AI may provide family members with points to note and safety measures while driving. For example, it may advise on specific speed limits and maintaining a safe distance between vehicles. Furthermore, based on the pre-boarding assessment results, the generation AI may provide specific driving advice to family members. For example, it may suggest training or rehabilitation to improve driving aptitude. In this way, by providing specific driving advice, family members can better understand the driving aptitude of elderly people.

[0051] When sharing driving status, the information sharing unit can analyze the elderly person's driving patterns in real time and notify family members if an abnormality is detected. In the information sharing unit, for example, the generation AI analyzes the driving status in real time and notifies family members if an abnormality is detected. For example, it detects and notifies the frequency of sudden braking and abrupt steering. In addition, when sharing driving status, the generation AI analyzes the elderly person's driving patterns in real time and notifies family members if an abnormality is detected. For example, it detects and notifies the frequency of speeding or insufficient distance between vehicles. In addition, the generation AI analyzes the driving status in real time and notifies family members if an abnormality is detected. For example, it detects and notifies the abnormal driving at night or in bad weather. This makes it easier for family members to understand the elderly person's driving status by notifying them if an abnormality is detected.

[0052] The information sharing unit can provide comprehensive support by sharing the pre-boarding assessment results and driving status not only with family members but also with medical institutions and nursing care facilities. In the information sharing unit, for example, the generation AI shares the pre-boarding assessment results and driving status with medical institutions to provide comprehensive support. For example, a doctor evaluates driving aptitude and provides appropriate advice. The pre-boarding assessment results and driving status are also shared with nursing care facilities to provide comprehensive support. For example, nursing care staff evaluate driving aptitude and provide appropriate support. The generation AI also shares the pre-boarding assessment results and driving status with medical institutions and nursing care facilities to provide comprehensive support. For example, medical institutions and nursing care facilities work together to evaluate driving aptitude and provide appropriate support. This makes it easier to provide comprehensive support by sharing information with medical institutions and nursing care facilities.

[0053] The information sharing unit can notify elderly people of points to pay attention to while driving in real time based on data on driving conditions. For example, the generation AI in the information sharing unit notifies elderly people of points to pay attention to while driving in real time based on data on driving conditions. For example, it notifies them of points to pay attention to at specific intersections. Furthermore, based on data on driving conditions, the generation AI notifies elderly people of points to pay attention to while driving in real time. For example, it notifies them of specific speed limits and maintaining a safe distance between vehicles. Furthermore, based on data on driving conditions, the generation AI notifies elderly people of points to pay attention to while driving in real time. For example, it notifies them of how to drive in specific road conditions. In this way, by notifying them of points to pay attention to in real time, risks while driving are reduced.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The driving aptitude assessment system also uses a driving aptitude measurement unit to monitor heart rate and blood pressure in real time while driving, and can issue a warning if an abnormality is detected. For example, if the heart rate rises suddenly while driving, a notification is sent to the driver urging them to take a break. Also, if blood pressure is abnormally high, an alert can be issued to advise them to stop driving. Furthermore, it is possible to accumulate heart rate and blood pressure data while driving and track changes in long-term health status. This allows for real-time monitoring of health status while driving, and by urging appropriate action if an abnormality is detected, it can support safe driving.

[0056] The driving aptitude assessment system can further analyze environmental sounds while driving in the driving aptitude measurement unit to evaluate the driver's concentration. For example, if the noise level inside the vehicle is high, the system can notify the driver to close the windows. Also, if the volume of music or the radio is too loud while driving, the system can issue an alert to lower the volume. Furthermore, the system can analyze environmental sounds while driving and evaluate whether the driver is concentrating. This allows the system to appropriately manage environmental sounds while driving and maintain the driver's concentration, thereby supporting safe driving.

[0057] The driving aptitude assessment system can also monitor posture while driving in the driving aptitude measurement unit and provide advice on maintaining proper posture. For example, if the driver's back is hunched while driving, the system will notify the driver to correct their posture. Also, if the driver has been driving in the same posture for a long period of time, the system can issue an alert to encourage them to take appropriate breaks. Furthermore, it is possible to accumulate posture data while driving and track long-term changes in posture. This will enable the system to properly manage posture while driving and reduce driver fatigue, thereby supporting safe driving.

[0058] The driving aptitude assessment system can also use its driving aptitude measurement unit to analyze eye movements while driving and evaluate the degree of eye concentration. For example, if the driver's eyes frequently wander while driving, it can send a notification to urge the driver to be more careful. Also, if the driver's eyes are too focused on a specific area, it can issue an alert to widen their field of vision. It can also accumulate eye movement data while driving and track long-term changes in eye movement. This can support safe driving by appropriately managing eye movements while driving and maintaining the driver's attention.

[0059] The driving aptitude assessment system can also monitor body temperature while driving in the driving aptitude measurement unit and evaluate changes in physical condition. For example, if the body temperature rises suddenly while driving, a notification is sent to the driver urging them to take a break. Also, if the body temperature is abnormally low, an alert can be issued to stop driving. Furthermore, it is possible to accumulate body temperature data while driving and track long-term changes in body temperature. This will enable the appropriate management of body temperature while driving and the early detection of changes in physical condition, thereby supporting safe driving.

[0060] The driving aptitude assessment system can further monitor the in-car environment (e.g., temperature and humidity) while driving in the driving aptitude measurement unit and provide advice to maintain a comfortable environment. For example, if the temperature inside the car is too high, a notification can be issued to use the air conditioner. Also, if the humidity is too low, an alert can be issued to use a humidifier. Furthermore, it is possible to accumulate in-car environment data while driving and track long-term changes in the environment. This makes it possible to appropriately manage the in-car environment and maintain a comfortable driving environment, thereby supporting safe driving.

[0061] The driving aptitude assessment system can also use its driving aptitude measurement unit to monitor the vehicle's behavior while driving (for example, acceleration and braking strength) and evaluate the driver's driving style. For example, if sudden acceleration or braking occurs frequently, the system can send a notification to the driver to warn them. If the driver's driving is rough, the system can provide advice on improving the driver's driving style. Furthermore, the system can accumulate data on the vehicle's behavior while driving and track long-term changes in driving style. This allows for appropriate management of driving style and supports safe driving.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The driving aptitude measurement unit measures the elderly person's repetition or response, as well as their eye movements. For example, the smartphone app asks, "What day is it today?" and checks whether they answer the question correctly. It also tracks their eye movements with a camera to measure their gaze and reaction speed. Step 2: The data analysis unit analyzes the data acquired by the driving aptitude measurement unit and determines driving aptitude. For example, the generation AI evaluates driving aptitude based on conversation repetition, response status, and eye movement data. Step 3: The information sharing unit shares the driving aptitude results determined by the data analysis unit with the elderly person's family. For example, the information sharing unit notifies the family in real time of the driving aptitude evaluation results and the driving situation via a smartphone app.

[0064] (Example 2) The driving aptitude assessment system according to an embodiment of the present invention assesses the driving aptitude of elderly people using a smartphone app before they drive a vehicle. This system assesses driving aptitude by measuring conversation repetition, response status, and eye movements, and analyzing them with a generative AI. It also shares accident situations involving drivers with similar physical abilities and driving patterns, and shares the assessment results and driving situation before driving with the elderly person's family members, allowing them to use this information to assess their driving aptitude and, if necessary, consider surrendering their license. This allows the driving aptitude assessment system to accurately assess the elderly person's driving aptitude and share this information with family members, thereby reducing driving risks and supporting safe driving.

[0065] A driving aptitude assessment system according to an embodiment includes a driving aptitude measurement unit, a data analysis unit, and an information sharing unit. The driving aptitude measurement unit measures the elderly person's eye movements and whether they repeat or respond to conversations. For example, a smartphone app asks, "What day is it today?" and checks whether the elderly person accurately answers the question. The camera also tracks the elderly person's eye movements and measures their gaze and reaction speed. The data analysis unit analyzes the data acquired by the driving aptitude measurement unit to assess the elderly person's driving aptitude. For example, the generation AI evaluates the elderly person's driving aptitude based on the data on the elderly person's repetition and response to conversations and the eye movements. The information sharing unit shares the results of the driving aptitude assessed by the data analysis unit with the elderly person's family. For example, the information sharing unit notifies the family of the driving aptitude assessment result and the driving situation in real time via a smartphone app. This allows the driving aptitude assessment system to accurately assess the elderly person's driving aptitude and share the results with family members, thereby reducing driving risks and supporting safe driving.

[0066] In the driving aptitude measurement unit, the generation AI analyzes the tone and speed of the elderly person's voice when repeating or responding to a conversation, and can detect signs of stress or fatigue. For example, when an elderly person answers questions from an app, the generation AI analyzes the tone and speed of the voice to detect signs of stress or fatigue. For example, it detects voice tremors and delays and evaluates the stress level. In addition, when repeating a conversation, the generation AI analyzes the tone and speed of the elderly person's voice in real time to detect abnormal changes. For example, it detects slower-than-normal response speeds and unnatural tones. In addition, when responding, the generation AI analyzes the tone and speed of the elderly person's voice to detect signs of stress or fatigue. For example, it analyzes changes in voice pitch and intensity and evaluates the stress level. This detects signs of stress and fatigue in the elderly, improving the accuracy of driving aptitude assessments.

[0067] In addition to measuring eye movements, the driving aptitude measurement unit can analyze facial expressions and subtle muscle movements to detect declines in concentration and attention. In addition to measuring eye movements, the driving aptitude measurement unit, for example, uses a generation AI to analyze facial expressions and subtle muscle movements to detect declines in concentration and attention. For example, it analyzes wrinkles between the eyebrows and movements of the corners of the mouth. Furthermore, when an elderly person uses an app, the generation AI analyzes facial expressions along with eye movements to detect declines in concentration and attention. For example, it detects slowed eye movements and stiffened facial expressions. Furthermore, in addition to measuring eye movements, the generation AI analyzes subtle muscle movements of the face to detect declines in concentration and attention. For example, it analyzes muscle movements around the eyes to evaluate declines in attention. This improves the accuracy of driving aptitude assessment by detecting declines in the elderly person's concentration and attention.

[0068] The driving aptitude measurement unit can use the emotion estimation function to analyze the emotional state of the elderly person when they answer questions and evaluate their emotional stability. For example, when the elderly person answers questions on an app, the generation AI uses the emotion estimation function to analyze their emotional state and evaluate their emotional stability. For example, emotions such as joy and anxiety are detected. Furthermore, when they answer questions, the generation AI uses the emotion estimation function to analyze the elderly person's emotional state in real time and evaluate their emotional stability. For example, emotions are estimated from facial expressions and tone of voice. Furthermore, the emotion estimation function is used to analyze the emotional state of the elderly person when they answer questions and evaluate their emotional stability. For example, changes in facial expressions and voice are analyzed to evaluate emotional fluctuations. This evaluates the emotional stability of the elderly person, thereby improving the accuracy of the evaluation of their driving aptitude.

[0069] The driving aptitude measurement unit can add a driving simulation function to evaluate driving aptitude in a virtual environment. The driving aptitude measurement unit adds a driving simulation function to, for example, a smartphone app, allowing elderly people to evaluate their driving aptitude in a virtual environment. For example, virtual road conditions and traffic rules are reproduced. The driving simulation function is also used to evaluate the reaction speed and judgment of elderly people when driving in a virtual environment. For example, reactions to emergency braking and lane changes during the simulation are measured. The driving simulation function is also added to the smartphone app, allowing elderly people to evaluate their driving aptitude in a virtual environment. For example, simulations of night driving and bad weather are performed to evaluate aptitude. This makes it possible to evaluate driving aptitude in a virtual environment in a more realistic manner.

[0070] The driving aptitude measurement unit can measure the elderly person's movements in daily life and evaluate their overall physical ability. The driving aptitude measurement unit, for example, uses a smartphone app to measure the elderly person's movements in daily life (e.g., walking and lifting objects) and evaluate their overall physical ability. For example, it measures walking speed and balance. It also measures movements in daily life and collects data for evaluating driving aptitude. For example, it measures lifting objects and climbing stairs to evaluate muscle strength and flexibility. It also measures the elderly person's movements in daily life and evaluates their overall physical ability. For example, it measures walking stability and reaction speed and uses the data to evaluate driving aptitude. In this way, by measuring movements in daily life, it is possible to evaluate overall physical ability and improve the accuracy of the driving aptitude evaluation.

[0071] The driving aptitude measurement unit can use the emotion estimation function to evaluate the psychological state before driving and provide advice for relaxation. The driving aptitude measurement unit, for example, uses the emotion estimation function to evaluate the psychological state of an elderly person before driving and provide advice for relaxation. For example, it may suggest deep breathing or playing relaxation music. The emotion estimation function may also be used to analyze the psychological state before driving and provide specific advice for relaxation. For example, it may suggest stretching or meditation methods. The emotion estimation function may also be used to evaluate the psychological state of an elderly person before driving and provide advice for relaxation. For example, it may suggest positive thinking or relaxation methods. In this way, by evaluating the psychological state before driving and providing advice for relaxation, the accuracy of the evaluation of driving aptitude is improved.

[0072] The data analysis unit can compare past driving data with current measurement data and track changes in driving aptitude over the long term. For example, the generation AI compares past driving data with current measurement data to track changes in driving aptitude over the long term. For example, it compares past driving records with current reaction speed. Furthermore, based on the past driving data, the generation AI analyzes current measurement data to evaluate changes in driving aptitude. For example, it compares past driving patterns with current attention. Furthermore, the generation AI compares past driving data with current measurement data to track changes in driving aptitude over the long term. For example, it compares past accident history with current driving aptitude. This allows for more accurate evaluation of driving aptitude by tracking changes in driving aptitude over the long term.

[0073] The data analysis unit can suggest an individual training program based on the data on conversation repetition and responses, thereby improving driving aptitude. In the data analysis unit, for example, the generation AI analyzes the data on conversation repetition and responses and suggests an individual training program for the elderly. For example, training to improve memory and reaction speed is provided. Furthermore, the generation AI suggests a training program suitable for the elderly based on the data on conversation repetition and responses. For example, exercises to improve attention and concentration are provided. Furthermore, the generation AI analyzes the data on conversation repetition and responses and suggests an individual training program. For example, methods of stress management and relaxation are provided. In this way, by suggesting an individual training program, it is possible to expect improvement in driving aptitude.

[0074] The data analysis unit uses the emotion estimation function to take emotional factors into account in the driving aptitude assessment results, enabling more accurate assessments. The data analysis unit, for example, uses the emotion estimation function to take emotional factors into account in the driving aptitude assessment results, enabling more accurate assessments. For example, stress and anxiety levels are reflected in the assessment. The generation AI also uses the emotion estimation function to take emotional factors into account in the driving aptitude assessment results. For example, the effect of emotional states on driving aptitude is evaluated. The emotion estimation function also takes emotional factors into account in the driving aptitude assessment results, enabling more accurate assessments. For example, the effect of emotional fluctuations on driving aptitude is evaluated. In this way, by taking emotional factors into account, the accuracy of driving aptitude assessment is improved.

[0075] The data analysis unit can propose individual driving assist functions (for example, speed limits or automatic braking) based on the driving aptitude evaluation results. For example, the data analysis unit allows the generation AI to propose individual driving assist functions for elderly people based on the driving aptitude evaluation results. For example, it proposes settings for speed limits or automatic braking. Furthermore, based on the driving aptitude evaluation results, the generation AI proposes driving assist functions suitable for elderly people. For example, it proposes lane keeping assist or collision avoidance systems. Furthermore, based on the driving aptitude evaluation results, the generation AI proposes individual driving assist functions. For example, it proposes night driving assistance or a fatigue detection system. In this way, by proposing individual driving assist functions, it is possible to support elderly driving and improve safety.

[0076] The data analysis unit can integrate the conversation repetition and response data with other health data (e.g., heart rate and blood pressure) to evaluate overall health status. The data analysis unit, for example, integrates the conversation repetition and response data with other health data such as heart rate and blood pressure to evaluate overall health status. For example, it evaluates stress levels and fatigue levels. The generation AI also integrates the conversation repetition and response data with other health data to evaluate overall health status. For example, it analyzes changes in heart rate variability and blood pressure. It also integrates the conversation repetition and response data with other health data to evaluate overall health status. For example, it takes into account exercise volume and sleep patterns in the evaluation. This improves the accuracy of driving aptitude assessment by evaluating overall health status.

[0077] The data analysis unit can use the emotion estimation function to provide the driving aptitude evaluation results to family members in a format that is emotionally easy to understand. For example, the data analysis unit uses the emotion estimation function to provide the driving aptitude evaluation results to family members in a format that is emotionally easy to understand. For example, the emotion score is displayed visually. Furthermore, the generation AI uses the emotion estimation function to provide the driving aptitude evaluation results to family members in a format that is emotionally easy to understand. For example, the emotional state is displayed in a graph or chart. Furthermore, the emotion estimation function is used to provide the driving aptitude evaluation results to family members in a format that is emotionally easy to understand. For example, emotional fluctuations are displayed using colors or icons. In this way, providing the evaluation results in a format that is emotionally easy to understand makes it easier for family members to understand the driving aptitude of an elderly person.

[0078] The information sharing unit can analyze the accident situations of similar drivers and evaluate the risk during specific time periods and weather conditions. For example, the generation AI in the information sharing unit analyzes the accident situations of similar drivers and evaluates the risk during specific time periods. For example, it evaluates the risk of accidents at night or early in the morning. Furthermore, based on the accident data of similar drivers, the generation AI evaluates the risk during specific weather conditions. For example, it analyzes the risk of accidents on rainy or snowy days. Furthermore, the generation AI analyzes the accident situations of similar drivers and evaluates the risk during specific time periods and weather conditions. For example, it evaluates the risk during rush hour or bad weather. In this way, by evaluating the risk during specific time periods and weather conditions, it is possible to gain a more specific understanding of the risks of driving.

[0079] The information sharing unit can provide specific driving advice to elderly people based on accident data of similar drivers. For example, the generation AI analyzes accident data of similar drivers and provides specific driving advice to elderly people. For example, it may advise on points to be careful of at specific intersections. Furthermore, based on the accident data of similar drivers, the generation AI provides elderly people with points to be careful of while driving. For example, it may advise on specific speed limits and maintaining a safe distance between vehicles. Furthermore, based on the accident data of similar drivers, the generation AI provides specific driving advice to elderly people. For example, it may advise on how to drive in specific road conditions. In this way, by providing specific driving advice, elderly people's driving risks are reduced.

[0080] The information sharing unit can use the emotion estimation function to provide support to reduce the anxiety and fear felt by elderly people when sharing the accident situation. The information sharing unit, for example, uses the emotion estimation function to provide support to reduce the anxiety and fear felt by elderly people when sharing the accident situation. For example, it displays a message that gives a sense of security. The generation AI also uses the emotion estimation function to provide support to reduce the anxiety and fear felt by elderly people when sharing the accident situation. For example, it suggests relaxation techniques or breathing techniques. The emotion estimation function also provides support to reduce the anxiety and fear felt by elderly people when sharing the accident situation. For example, it shares positive examples and successful experiences. This provides support to reduce anxiety and fear, making it easier for elderly people to understand the accident situation.

[0081] The information sharing unit can integrate accident situations of similar drivers with geographical information to assess risk in specific areas. For example, the generation AI integrates accident situations of similar drivers with geographical information to assess risk in specific areas. For example, it analyzes the accident risk at specific intersections or roads. It also integrates accident data of similar drivers with geographical information to assess risk in specific areas. For example, it evaluates accident risk in urban and suburban areas. It also integrates accident situations of similar drivers with geographical information to assess risk in specific areas. For example, it analyzes the frequency and causes of accidents in specific areas. This allows for a more specific understanding of driving risks by assessing risk in specific areas.

[0082] The information sharing unit can notify elderly people of important points to pay attention to while driving in real time based on accident data of similar drivers. For example, the generation AI in the information sharing unit notifies elderly people of important points to pay attention to while driving in real time based on accident data of similar drivers. For example, it notifies them of important points to pay attention to at specific intersections. Furthermore, the generation AI notifies elderly people of important points to pay attention to while driving in real time based on accident data of similar drivers. For example, it notifies them of specific speed limits and maintaining a safe distance between vehicles. Furthermore, the generation AI notifies elderly people of important points to pay attention to while driving in real time based on accident data of similar drivers. For example, it notifies them of how to drive in specific road conditions. In this way, by notifying them of important points to pay attention to in real time, risks while driving are reduced.

[0083] The information sharing unit can use the emotion estimation function to provide information to reduce the anxiety felt by family members when the accident situation is shared. The information sharing unit, for example, uses the emotion estimation function to provide information to reduce the anxiety felt by family members when the accident situation is shared. For example, it displays a message that gives a sense of security. The generation AI also uses the emotion estimation function to provide information to reduce the anxiety felt by family members when the accident situation is shared. For example, it suggests relaxation techniques or breathing techniques. The emotion estimation function also provides information to reduce the anxiety felt by family members when the accident situation is shared. For example, it shares positive examples or successful experiences. In this way, providing information that reduces anxiety makes it easier for family members to understand the accident situation.

[0084] The information sharing unit can evaluate the risk that a specific driving pattern will lead to an accident based on accident data of similar drivers. In the information sharing unit, for example, the generation AI analyzes accident data of similar drivers and evaluates the risk that a specific driving pattern will lead to an accident. For example, it evaluates the frequency of sudden braking and sudden steering. Furthermore, based on accident data of similar drivers, the generation AI evaluates the risk that a specific driving pattern will lead to an accident. For example, it evaluates speeding and keeping an insufficient distance between vehicles. Furthermore, the generation AI analyzes accident data of similar drivers and evaluates the risk that a specific driving pattern will lead to an accident. For example, it evaluates driving patterns at night or in bad weather. In this way, by evaluating the risk that a specific driving pattern will lead to an accident, it is possible to understand the risks of driving more specifically.

[0085] The information sharing unit can analyze the accident situations of similar drivers and evaluate the impact that specific health conditions (for example, impaired vision or slower reaction times) have on accidents. In the information sharing unit, for example, the generation AI analyzes the accident situations of similar drivers and evaluates the impact that specific health conditions have on accidents. For example, it evaluates the impact that impaired vision or slower reaction times have on accident risk. Furthermore, based on accident data of similar drivers, the generation AI evaluates the impact that specific health conditions have on accidents. For example, it evaluates the impact that impaired hearing or reduced muscle strength has on accident risk. Furthermore, the generation AI analyzes the accident situations of similar drivers and evaluates the impact that specific health conditions have on accidents. For example, it evaluates the impact that impaired cognitive function or chronic illnesses have on accident risk. In this way, by evaluating the impact that specific health conditions have on accidents, it is possible to understand the risks of driving more specifically.

[0086] The information sharing unit can use the emotion estimation function to provide support to reduce the stress felt by elderly people when information about the accident situation is provided. The information sharing unit, for example, uses the emotion estimation function to provide support to reduce the stress felt by elderly people when information about the accident situation is provided. For example, it displays a message that gives a sense of security. The generation AI also uses the emotion estimation function to provide support to reduce stress felt by elderly people when information about the accident situation is provided. For example, it suggests relaxation techniques or breathing techniques. The emotion estimation function also provides support to reduce the stress felt by elderly people when information about the accident situation is provided. For example, it shares positive examples and successful experiences. In this way, support to reduce stress is provided, making it easier for elderly people to understand the accident situation.

[0087] The information sharing unit can integrate accident data of similar drivers with other traffic data (for example, traffic volume and road conditions) to perform a comprehensive risk assessment. In the information sharing unit, for example, the generation AI integrates accident data of similar drivers with other traffic data to perform a comprehensive risk assessment. For example, it evaluates risk by taking traffic volume and road conditions into consideration. It also integrates accident data of similar drivers with other traffic data to perform a comprehensive risk assessment. For example, it evaluates the congestion status and accident frequency of specific roads. It also integrates accident data of similar drivers with other traffic data to perform a comprehensive risk assessment. For example, it evaluates the risk in specific areas or time periods. This allows for a more specific understanding of driving risks by performing a comprehensive risk assessment.

[0088] The information sharing unit can notify elderly people in advance of points to be careful of while driving based on accident data of similar drivers. For example, the generation AI in the information sharing unit notifies elderly people in advance of points to be careful of while driving based on accident data of similar drivers. For example, it notifies them of points to be careful of at specific intersections. Furthermore, based on accident data of similar drivers, the generation AI notifies elderly people in advance of points to be careful of while driving. For example, it notifies them of specific speed limits and maintaining a safe distance between vehicles. Furthermore, based on accident data of similar drivers, the generation AI notifies elderly people in advance of points to be careful of while driving. For example, it notifies them of how to drive in specific road conditions. In this way, by notifying them of points to be careful of in advance, risks while driving are reduced.

[0089] The information sharing unit can use the emotion estimation function to provide information to reduce the anxiety felt by family members when information about the accident situation is provided. The information sharing unit, for example, uses the emotion estimation function to provide information to reduce the anxiety felt by family members when information about the accident situation is provided. For example, it displays a message that gives a sense of security. The generation AI also uses the emotion estimation function to provide information to reduce the anxiety felt by family members when information about the accident situation is provided. For example, it suggests relaxation techniques or breathing techniques. The emotion estimation function also provides information to reduce the anxiety felt by family members when information about the accident situation is provided. For example, it shares positive examples or successful experiences. In this way, providing information that reduces anxiety makes it easier for family members to understand the accident situation.

[0090] The information sharing unit can customize the driving aptitude evaluation results individually for each family member and provide them in an easy-to-understand format. For example, the generation AI in the information sharing unit customizes the driving aptitude evaluation results individually for each family member and provides them in an easy-to-understand format. For example, the evaluation results are visually displayed in graphs or charts. The generation AI also customizes the driving aptitude evaluation results individually for each family member and provides them in an easy-to-understand format. For example, the evaluation results are provided in a concise report format. The generation AI also customizes the driving aptitude evaluation results individually for each family member and provides them in an easy-to-understand format. For example, the evaluation results are explained using video or audio. In this way, by providing the results in an easy-to-understand format that is individually customized, it becomes easier for family members to understand the driving aptitude of an elderly person.

[0091] When sharing information with family members, the information sharing unit can provide specific advice taking into account the elderly person's driving history and health condition. In the information sharing unit, for example, the generation AI takes into account the elderly person's driving history and health condition and provides specific advice to family members. For example, it may advise on times and situations when driving should be avoided. In addition, when sharing information with family members, the generation AI takes into account the elderly person's driving history and health condition and provides specific advice. For example, it may suggest points to be careful of while driving and safety measures. In addition, the generation AI takes into account the elderly person's driving history and health condition and provides specific advice to family members. For example, it may suggest training or rehabilitation to improve driving aptitude. In this way, providing specific advice taking into account the elderly person's driving history and health condition makes it easier for family members to understand the elderly person's driving aptitude.

[0092] The information sharing unit can use the emotion estimation function to analyze the emotional state of family members when they receive information and provide the information at the appropriate time. For example, the information sharing unit uses the emotion estimation function to analyze the emotional state of family members when they receive information and provide the information at the appropriate time. For example, it sends a notification when the family member is relaxed. The generation AI also uses the emotion estimation function to analyze the emotional state of family members and provide the information at the appropriate time. For example, it shares the evaluation results when the family member is not feeling stressed. The emotion estimation function also analyzes the emotional state of family members when they receive information and provides the information at the appropriate time. For example, it notifies the family member of the driving aptitude evaluation results when the family member is calm. In this way, by providing information at the appropriate time, it becomes easier for family members to understand the driving aptitude of an elderly person.

[0093] The information sharing unit can share information with family members not only through a smartphone app but also through other devices (for example, a smartwatch or smart speaker). For example, the information sharing unit can share information with family members not only through a smartphone app but also through a smartwatch. For example, the driving aptitude evaluation results can be notified to the smartwatch. The generation AI can also share information with family members through a smart speaker. For example, the driving aptitude evaluation results can be notified by voice. Information can also be shared with family members not only through a smartphone app but also through other devices. For example, the driving aptitude evaluation results can be notified to a smart home device. This makes it easier for family members to understand the driving aptitude of elderly people by sharing information through multiple devices.

[0094] The information sharing unit can collect opinions and feedback from other family members when sharing information with family members, and use this as comprehensive information for making a decision. For example, when the generation AI shares information with family members, the information sharing unit collects opinions and feedback from other family members and uses this as comprehensive information for making a decision. For example, the information sharing unit aggregates the opinions of all family members and shares the evaluation results. Also, when sharing information with family members, the generation AI collects feedback from other family members and uses this as comprehensive information for making a decision. For example, the evaluation results of driving aptitude are adjusted based on the opinions of all family members. Also, when sharing information with family members, the generation AI collects opinions and feedback from other family members and uses this as comprehensive information for making a decision. For example, it provides evaluation results that reflect the opinions of all family members. This makes it easier to provide comprehensive information for making a decision by collecting the opinions and feedback of other family members.

[0095] The information sharing unit can use the emotion estimation function to analyze the emotional state of family members when they receive information and provide appropriate support. For example, the information sharing unit uses the emotion estimation function to analyze the emotional state of family members when they receive information and provide appropriate support. For example, it displays a message that gives a sense of security when a family member is feeling anxious. The generation AI also uses the emotion estimation function to analyze the emotional state of family members and provide appropriate support. For example, it suggests relaxation methods when a family member is feeling stressed. The emotion estimation function also analyzes the emotional state of family members when they receive information and provides appropriate support. For example, it notifies the family member of the results of a driving aptitude evaluation when they are calm. This makes it easier for family members to understand the driving aptitude of an elderly person by providing appropriate support.

[0096] The information sharing unit can provide specific driving advice to family members based on the pre-boarding assessment results. For example, the information sharing unit provides specific driving advice to family members based on the pre-boarding assessment results of the generation AI. For example, it may advise on times and situations when driving should be avoided. Furthermore, based on the pre-boarding assessment results, the generation AI may provide family members with points to note and safety measures while driving. For example, it may advise on specific speed limits and maintaining a safe distance between vehicles. Furthermore, based on the pre-boarding assessment results, the generation AI may provide specific driving advice to family members. For example, it may suggest training or rehabilitation to improve driving aptitude. In this way, by providing specific driving advice, family members can better understand the driving aptitude of elderly people.

[0097] When sharing driving status, the information sharing unit can analyze the elderly person's driving patterns in real time and notify family members if an abnormality is detected. In the information sharing unit, for example, the generation AI analyzes the driving status in real time and notifies family members if an abnormality is detected. For example, it detects and notifies the frequency of sudden braking and abrupt steering. In addition, when sharing driving status, the generation AI analyzes the elderly person's driving patterns in real time and notifies family members if an abnormality is detected. For example, it detects and notifies the frequency of speeding or insufficient distance between vehicles. In addition, the generation AI analyzes the driving status in real time and notifies family members if an abnormality is detected. For example, it detects and notifies the abnormal driving at night or in bad weather. This makes it easier for family members to understand the elderly person's driving status by notifying them if an abnormality is detected.

[0098] The information sharing unit can use the emotion estimation function to analyze the emotional state of family members when they receive the driving information and provide appropriate support. For example, the information sharing unit uses the emotion estimation function to analyze the emotional state of family members when they receive the driving information and provide appropriate support. For example, it displays a message that gives a sense of security when a family member is feeling anxious. The generation AI also uses the emotion estimation function to analyze the emotional state of family members and provide appropriate support. For example, it suggests relaxation methods when a family member is feeling stressed. The emotion estimation function also analyzes the emotional state of family members when they receive the driving information and provides appropriate support. For example, it notifies the family member of the results of the driving aptitude evaluation when the family member is calm. This makes it easier for family members to understand the driving status of elderly people by providing appropriate support.

[0099] The information sharing unit can provide comprehensive support by sharing the pre-boarding assessment results and driving status not only with family members but also with medical institutions and nursing care facilities. In the information sharing unit, for example, the generation AI shares the pre-boarding assessment results and driving status with medical institutions to provide comprehensive support. For example, a doctor evaluates driving aptitude and provides appropriate advice. The pre-boarding assessment results and driving status are also shared with nursing care facilities to provide comprehensive support. For example, nursing care staff evaluate driving aptitude and provide appropriate support. The generation AI also shares the pre-boarding assessment results and driving status with medical institutions and nursing care facilities to provide comprehensive support. For example, medical institutions and nursing care facilities work together to evaluate driving aptitude and provide appropriate support. This makes it easier to provide comprehensive support by sharing information with medical institutions and nursing care facilities.

[0100] The information sharing unit can notify elderly people of points to pay attention to while driving in real time based on data on driving conditions. For example, the generation AI in the information sharing unit notifies elderly people of points to pay attention to while driving in real time based on data on driving conditions. For example, it notifies them of points to pay attention to at specific intersections. Furthermore, based on data on driving conditions, the generation AI notifies elderly people of points to pay attention to while driving in real time. For example, it notifies them of specific speed limits and maintaining a safe distance between vehicles. Furthermore, based on data on driving conditions, the generation AI notifies elderly people of points to pay attention to while driving in real time. For example, it notifies them of how to drive in specific road conditions. In this way, by notifying them of points to pay attention to in real time, risks while driving are reduced.

[0101] The information sharing unit can use the emotion estimation function to analyze the emotional state of family members when they receive the driving information and provide appropriate support. For example, the information sharing unit uses the emotion estimation function to analyze the emotional state of family members when they receive the driving information and provide appropriate support. For example, it displays a message that gives a sense of security when a family member is feeling anxious. The generation AI also uses the emotion estimation function to analyze the emotional state of family members and provide appropriate support. For example, it suggests relaxation methods when a family member is feeling stressed. The emotion estimation function also analyzes the emotional state of family members when they receive the driving information and provides appropriate support. For example, it notifies the family member of the results of the driving aptitude evaluation when the family member is calm. This makes it easier for family members to understand the driving status of elderly people by providing appropriate support.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The driving aptitude assessment system also uses a driving aptitude measurement unit to monitor heart rate and blood pressure in real time while driving, and can issue a warning if an abnormality is detected. For example, if the heart rate rises suddenly while driving, a notification is sent to the driver urging them to take a break. Also, if blood pressure is abnormally high, an alert can be issued to advise them to stop driving. Furthermore, it is possible to accumulate heart rate and blood pressure data while driving and track changes in long-term health status. This allows for real-time monitoring of health status while driving, and by urging appropriate action if an abnormality is detected, it can support safe driving.

[0104] The driving aptitude assessment system can further analyze environmental sounds while driving in the driving aptitude measurement unit to evaluate the driver's concentration. For example, if the noise level inside the vehicle is high, the system can notify the driver to close the windows. Also, if the volume of music or the radio is too loud while driving, the system can issue an alert to lower the volume. Furthermore, the system can analyze environmental sounds while driving and evaluate whether the driver is concentrating. This allows the system to appropriately manage environmental sounds while driving and maintain the driver's concentration, thereby supporting safe driving.

[0105] The driving aptitude assessment system can also monitor posture while driving in the driving aptitude measurement unit and provide advice on maintaining proper posture. For example, if the driver's back is hunched while driving, the system will notify the driver to correct their posture. Also, if the driver has been driving in the same posture for a long period of time, the system can issue an alert to encourage them to take appropriate breaks. Furthermore, it is possible to accumulate posture data while driving and track long-term changes in posture. This will enable the system to properly manage posture while driving and reduce driver fatigue, thereby supporting safe driving.

[0106] The driving aptitude assessment system can also use its driving aptitude measurement unit to analyze eye movements while driving and evaluate the degree of eye concentration. For example, if the driver's eyes frequently wander while driving, it can send a notification to urge the driver to be more careful. Also, if the driver's eyes are too focused on a specific area, it can issue an alert to widen their field of vision. It can also accumulate eye movement data while driving and track long-term changes in eye movement. This can support safe driving by appropriately managing eye movements while driving and maintaining the driver's attention.

[0107] The driving aptitude assessment system can also use its driving aptitude measurement unit to analyze the driver's emotional state while driving and evaluate their emotional stability. For example, if stress or anxiety increases while driving, the system can provide the driver with advice on how to relax. Also, if anger or irritation is observed while driving, the system can suggest ways to calm down. Furthermore, it can accumulate emotional data while driving and track long-term changes in emotions. This can support safe driving by properly managing the driver's emotional state and maintaining emotional stability.

[0108] The driving aptitude assessment system can also monitor brain waves while driving in the driving aptitude measurement unit to evaluate concentration and attention. For example, if brain waves indicate a relaxed state while driving, it can send a notification to the driver to alert them. Also, if brain waves indicate a stressed state, it can provide advice on how to relax. It can also accumulate brain wave data while driving and track long-term changes in brain waves. This can support safe driving by appropriately managing brain waves while driving and maintaining concentration and attention.

[0109] The driving aptitude assessment system can further analyze breathing patterns while driving in the driving aptitude measurement unit and evaluate the level of relaxation. For example, if breathing becomes shallow while driving, a notification encouraging deep breathing can be sent. Also, if breathing becomes rapid, advice on how to relax can be provided. Furthermore, it is possible to accumulate breathing data while driving and track long-term changes in breathing patterns. This can support safe driving by appropriately managing breathing patterns while driving and maintaining the level of relaxation.

[0110] The driving aptitude assessment system can also monitor body temperature while driving in the driving aptitude measurement unit and evaluate changes in physical condition. For example, if the body temperature rises suddenly while driving, a notification is sent to the driver urging them to take a break. Also, if the body temperature is abnormally low, an alert can be issued to stop driving. Furthermore, it is possible to accumulate body temperature data while driving and track long-term changes in body temperature. This will enable the appropriate management of body temperature while driving and the early detection of changes in physical condition, thereby supporting safe driving.

[0111] The driving aptitude assessment system can further monitor the in-car environment (e.g., temperature and humidity) while driving in the driving aptitude measurement unit and provide advice to maintain a comfortable environment. For example, if the temperature inside the car is too high, a notification can be issued to use the air conditioner. Also, if the humidity is too low, an alert can be issued to use a humidifier. Furthermore, it is possible to accumulate in-car environment data while driving and track long-term changes in the environment. This makes it possible to appropriately manage the in-car environment and maintain a comfortable driving environment, thereby supporting safe driving.

[0112] The driving aptitude assessment system can also use its driving aptitude measurement unit to monitor the vehicle's behavior while driving (for example, acceleration and braking strength) and evaluate the driver's driving style. For example, if sudden acceleration or braking occurs frequently, the system can send a notification to the driver to warn them. If the driver's driving is rough, the system can provide advice on improving the driver's driving style. Furthermore, the system can accumulate data on the vehicle's behavior while driving and track long-term changes in driving style. This allows for appropriate management of driving style and supports safe driving.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The driving aptitude measurement unit measures the elderly person's repetition or response, as well as their eye movements. For example, the smartphone app asks, "What day is it today?" and checks whether they answer the question correctly. It also tracks their eye movements with a camera to measure their gaze and reaction speed. Step 2: The data analysis unit analyzes the data acquired by the driving aptitude measurement unit and determines driving aptitude. For example, the generation AI evaluates driving aptitude based on conversation repetition, response status, and eye movement data. Step 3: The information sharing unit shares the driving aptitude results determined by the data analysis unit with the elderly person's family. For example, the information sharing unit notifies the family in real time of the driving aptitude evaluation results and the driving situation via a smartphone app.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

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

[0121] The 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.

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

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

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

[0125] Fig. 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.

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

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

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0132] 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.

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

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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]

[0182] 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 aptitude measurement unit that measures the state of conversation repetition or response and eye movements; a data analysis unit that analyzes the data acquired by the driving aptitude measurement unit and determines driving aptitude; an information sharing unit that shares the results of the driving aptitude determined by the data analysis unit with the elderly person's family members; A system characterized by:

2. The driving aptitude measurement unit When repeating or responding to a conversation, the generative AI analyzes the tone and rate of the elderly person's voice to detect signs of stress or fatigue.

2. The system of claim 1.

3. The driving aptitude measurement unit In addition to measuring eye movements, the system analyzes facial expressions and subtle muscle movements to detect declines in concentration and attention.

2. The system of claim 1.

4. The driving aptitude measurement unit The emotional state of the elderly person when answering questions is analyzed to evaluate their emotional stability.

2. The system of claim 1.

5. The driving aptitude measurement unit Add a driving simulation function to evaluate driving aptitude in a virtual environment 2. The system of claim 1.

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