System

Abstract

An object of a system according to an embodiment is to appropriately determine whether or not driving is possible based on a change in the physical condition or cognitive function of an elderly person.SOLUTION: A system according to an embodiment includes a physical condition data collector, a cognitive function evaluator, and a driving availability determiner. The physical condition data collection unit collects physical condition data of an elderly person. The cognitive function evaluation unit evaluates a cognitive function of the elderly person based on the physical condition data collected by the physical condition data collection unit. The driving possibility determination unit determines whether driving is possible based on a result of the evaluation by the cognitive function evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to appropriately determine whether an elderly person is able to drive based on changes in their physical condition and cognitive function.

[0005] The system according to the embodiment aims to appropriately determine whether an elderly person is able to drive based on changes in their physical condition and cognitive function. [Means for solving the problem]

[0006] The system according to the embodiment includes a physical condition data collection unit, a cognitive function assessment unit, and a driving feasibility determination unit. The physical condition data collection unit collects physical condition data of the elderly person. The cognitive function assessment unit evaluates the cognitive function of the elderly person based on the physical condition data collected by the physical condition data collection unit. The driving feasibility determination unit determines whether the elderly person is able to drive based on the results of the assessment by the cognitive function assessment unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately determine whether an elderly person is able to drive based on changes in the elderly person's physical condition and cognitive function. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A safe driving support system according to an embodiment of the present invention is a system that evaluates the physical condition data and cognitive function of an elderly person and determines whether or not the elderly person is capable of driving. As a result, the safe driving support system can support safe driving by evaluating the physical condition data and cognitive function of an elderly person and determining whether or not the elderly person is capable of driving.

[0029] A safe driving support system according to an embodiment includes a physical condition data collection unit, a cognitive function evaluation unit, and a driving feasibility determination unit. The physical condition data collection unit collects physical condition data of an elderly person. For example, the physical condition data collection unit acquires data such as heart rate, blood pressure, sleep time, and number of steps from a smartwatch or a health management app. The physical condition data collection unit can also collect environmental data (e.g., temperature, humidity, and air quality) and evaluate the impact on the elderly person's physical condition. The cognitive function evaluation unit evaluates the elderly person's cognitive function based on the physical condition data collected by the physical condition data collection unit. For example, the cognitive function evaluation unit compares the data with past data to check for abnormalities and issues a warning if a decline in cognitive function is suspected. The cognitive function evaluation unit can also use a generation AI to detect abnormal values ​​in the physical condition data in real time and immediately notify the elderly person when an abnormality occurs. The driving feasibility determination unit determines whether the elderly person is able to drive based on the evaluation results by the cognitive function evaluation unit. For example, the driving feasibility determination unit determines whether the elderly person is able to drive based on the physical condition data and the evaluation results of the cognitive function and provides specific advice. As a result, the safe driving support system according to the embodiment can support safe driving by evaluating the physical condition data and cognitive function of the elderly person and determining whether or not they are able to drive.

[0030] The physical condition data collection unit can acquire data such as heart rate, blood pressure, sleep time, and step count from a smartwatch or a health management app. The physical condition data collection unit acquires data such as heart rate, blood pressure, sleep time, and step count from, for example, a smartwatch or a health management app. For example, a smartwatch is equipped with a heart rate measurement function and a GPS function, allowing it to collect data in real time. In addition, a health management app is equipped with a data recording method and analysis function, allowing it to manage the user's physical condition data in detail. As a result, collecting data from a smartwatch or a health management app enables a more detailed physical condition evaluation.

[0031] The cognitive function assessment unit can compare data with past data to check for abnormalities and issue a warning if a decline in cognitive function is suspected. The cognitive function assessment unit can, for example, compare data with past health checkup results and daily health records to detect abnormal heart rate and blood pressure values. The cognitive function assessment unit can also use generative AI to detect abnormal values ​​in health data in real time and immediately notify users when an abnormality occurs. This allows for early response by detecting abnormalities by comparing data with past data and issuing a warning if a decline in cognitive function is suspected.

[0032] The driving feasibility determination unit determines whether or not a user is able to drive based on the physical condition data and the evaluation results of cognitive function, and can provide advice such as, for example, "You'd better not drive today." The driving feasibility determination unit determines whether or not a user is able to drive based on the physical condition data and the evaluation results of cognitive function, and can provide specific advice such as, "You'd better not drive today." The driving feasibility determination unit can also use an emotion estimation function to evaluate the user's emotional state (e.g., stress level) and make a comprehensive judgment in combination with the physical condition data. This makes it possible to support safe driving for elderly people by determining whether or not a user is able to drive based on the physical condition data and the evaluation results of cognitive function, and providing specific advice.

[0033] Generative AI can detect abnormal values ​​in health data in real time and immediately notify users when an abnormality occurs. Generative AI can, for example, detect abnormal values ​​in health data in real time and immediately notify users when an abnormality occurs. For example, it can detect abnormal values ​​in heart rate or blood pressure and issue an alert. Generative AI can also use voice input and image analysis to reduce the burden on users when reporting their health condition. For example, it can use voice recognition technology to allow users to enter health data simply by speaking. This allows for the detection of abnormal values ​​in health data in real time and immediate notification, enabling a rapid response.

[0034] Using voice input or image analysis, the burden on users when reporting their physical condition can be reduced. Using voice input or image analysis, for example, the burden on users when reporting their physical condition can be reduced. For example, using voice recognition technology, a user can input physical condition data simply by speaking. In addition, using an emotion estimation function, the emotions of a user when inputting physical condition data can be estimated in real time, improving the accuracy of the input. For example, facial expressions and voice at the time of input can be analyzed to evaluate the emotional state. In this way, using voice input or image analysis, the burden on users when reporting their physical condition can be reduced.

[0035] Data from devices can be integrated to provide a more detailed assessment of physical condition. For example, data from smart home devices can be integrated to build a system that provides a more detailed assessment of physical condition. For example, data can be collected from smart scales and smart thermometers. It is also necessary to clarify the type and function of the device. For example, smart home devices, wearable devices, etc. This allows for a more detailed assessment of physical condition by integrating data from different devices.

[0036] By incorporating daily behavioral data, it is possible to perform more accurate assessments of cognitive function. By incorporating daily behavioral data, for example, it is possible to perform more accurate assessments of cognitive function. For example, by incorporating daily behavioral data, a system can be built that improves the accuracy of cognitive function assessments. For example, the content of conversations and frequency of reading can be analyzed to evaluate changes in cognitive function. It is also necessary to clarify the type of daily behavioral data and how it is acquired. For example, the content of conversations, frequency of reading, etc. By incorporating daily behavioral data, it is possible to perform more accurate assessments of cognitive function.

[0037] Generative AI can visualize the results of cognitive function assessment, allowing users to intuitively understand them. For example, generative AI can be used to build a system that visualizes the results of cognitive function assessment. For example, the assessment results can be displayed using graphs or charts. It is also necessary to clarify the specific methods and techniques for visualization. For example, graph display, dashboard display, etc. In this way, visualizing the results of cognitive function assessment using generative AI will allow users to intuitively understand them.

[0038] By incorporating tests using games or puzzles, cognitive function can be evaluated while having fun. By incorporating tests using games or puzzles, cognitive function can be evaluated while having fun. For example, a system for evaluating cognitive function can be constructed by incorporating tests using games or puzzles. For example, cognitive function can be evaluated through simple puzzle games. It is also necessary to clarify the type and content of the games and puzzles. For example, puzzle games, quiz games, etc. This makes it possible to evaluate cognitive function while having fun using games and puzzles.

[0039] By making the cognitive function assessment algorithm multilingual, it can be adapted to users of different languages ​​and cultural backgrounds. By making the cognitive function assessment algorithm multilingual, it can be adapted to users of different languages ​​and cultural backgrounds, for example. For example, a system can be built that makes the cognitive function assessment algorithm multilingual. For example, it can support multiple languages ​​such as English, French, and Chinese. It is also necessary to clarify the specific methods and technologies for multilingual support. For example, translation algorithms, multilingual databases, etc. This will enable it to be adapted to users of different languages ​​and cultural backgrounds, improving the accuracy of cognitive function assessment.

[0040] By incorporating driving history data, it is possible to make more accurate judgments about whether or not a person is capable of driving. By incorporating driving history data, for example, it is possible to make more accurate judgments about whether or not a person is capable of driving. For example, a system can be built that incorporates past driving history data to improve the accuracy of judgments about whether or not a person is capable of driving. For example, accident history and driving frequency can be analyzed to evaluate risk. It is also necessary to clarify the type of driving history data and how it is obtained. For example, accident history, driving frequency, etc. By incorporating past driving history data, it is possible to make more accurate judgments about whether or not a person is capable of driving.

[0041] The generation AI can provide an interactive guide to explain the driving feasibility judgment results to the user in an easy-to-understand manner. For example, the generation AI can be used to build a system that provides an interactive guide to explain the driving feasibility judgment results to the user in an easy-to-understand manner. For example, the driving feasibility judgment results can be visually displayed and the reasons explained in detail. In addition, the specific content of the interactive guide and how it is provided must be clarified. For example, a dialogue-style guide, a visual guide, etc. In this way, the generation AI can be used to explain the driving feasibility judgment results in an easy-to-understand manner, making it easier for the user to understand.

[0042] It is possible to consider the availability of transportation means and propose alternative means. For example, we will build a system that considers the availability of other transportation means and proposes alternative means based on the results of a driving feasibility assessment. For example, we will check the operation status of public transportation and recommend its use. It is also necessary to clarify the type of transportation means and how to use it. For example, public transportation, bicycle, etc. This will consider the availability of other transportation means and propose alternative means, thereby expanding the user's transportation options.

[0043] By making the algorithm for determining whether a person is fit to drive customizable, it can accommodate users of different age groups and health conditions. By making the algorithm for determining whether a person is fit to drive customizable, it can accommodate users of different age groups and health conditions. For example, a system can be built that allows customization of the algorithm for determining whether a person is fit to drive so that it can accommodate users of different age groups and health conditions. For example, evaluation criteria can be set according to age and health condition. It is also necessary to clarify the specific ranges and classification methods for age groups and health conditions. For example, people in their 20s, 30s, elderly, etc. This will make it possible to accommodate users of different age groups and health conditions, improving the accuracy of determining whether a person is fit to drive.

[0044] Notification content can be customized according to user preferences and provided in a more acceptable format. Notification content can be customized according to user preferences and provided in a more acceptable format, for example. For example, a system can be built that customizes notification content according to user preferences. For example, the wording and format of notifications can be changed to suit the user's preferences. It is also necessary to clarify the specific type of notification content and how it is provided. For example, text notification, audio notification, etc. This allows notification content to be customized according to user preferences, making it possible to provide notifications in a more acceptable format.

[0045] The generation AI can optimize the timing of notifications and send them at the time when the user is most likely to receive them. The generation AI can, for example, optimize the timing of notifications and send them at the time when the user is most likely to receive them. For example, a system that optimizes the timing of notifications can be built using the generation AI. For example, the user's behavioral patterns can be analyzed to determine the optimal notification time. It is also necessary to clarify the specific method and criteria for setting the timing of notifications. For example, the user's activity patterns, optimal time periods, etc. By using the generation AI to optimize the timing of notifications, it is possible to send them at the time when the user is most likely to receive them.

[0046] Notification methods can be diversified, allowing notifications to be sent via smart speakers or wearable devices. Notification methods can be diversified, allowing notifications to be sent via smart speakers or wearable devices. For example, a system can be built that diversifies notification methods and sends notifications via smart speakers. For example, the notification content can be conveyed by voice. It is also necessary to clarify the specific types of notification methods and how they are provided. For example, smart speakers, wearable devices, etc. By diversifying notification methods, notifications can be sent via smart speakers or wearable devices.

[0047] By combining notification means, it is possible to provide information to the user in the most optimal form. By combining notification means, for example, information can be provided to the user in the most optimal form. For example, a system can be built that combines different notification means to provide information to the user in the most optimal form. For example, audio notification and visual notification can be provided simultaneously. It is also necessary to clarify the specific type of notification means and the method of providing it. For example, audio notification, visual notification, etc. This allows information to be provided to the user in the most optimal form by combining different notification means.

[0048] Storing monitoring data on the cloud can enable long-term data analysis. Storing monitoring data on the cloud can enable long-term data analysis, for example. For example, building a system to safely store monitoring data on the cloud. For example, encrypting data and controlling access. It is also necessary to clarify the specific type of cloud and security measures. For example, AWS, Azure, data encryption, etc. This allows for long-term data analysis by safely storing monitoring data on the cloud.

[0049] Generative AI can detect abnormal values ​​in monitoring data in real time and immediately propose countermeasures. Generative AI can, for example, detect abnormal values ​​in monitoring data in real time and immediately propose countermeasures. For example, a system can be built using generative AI to detect abnormal values ​​in monitoring data in real time. For example, abnormal values ​​in heart rate and blood pressure can be detected. It is also necessary to clarify the type and functions of the generative AI. For example, anomaly detection algorithms, notification functions, etc. This makes it possible to use generative AI to detect abnormal values ​​in monitoring data in real time and immediately propose countermeasures.

[0050] Monitoring data can be shared with family members and medical professionals, enabling collaborative health management of the elderly. For example, a system for sharing monitoring data with family members and medical professionals can be built. For example, data can be shared on the cloud and made accessible in real time. It is also necessary to clarify the specific scope and method of involvement of family members and medical professionals. For example, relatives, caregivers, doctors, nurses, care workers, etc. This allows for collaborative health management of the elderly by sharing monitoring data with family members and medical professionals.

[0051] Data from devices and platforms can be integrated to enable more detailed monitoring. For example, data from different devices and platforms can be integrated to build a system for more detailed monitoring. For example, data from smart watches and smart home devices can be integrated. It is also necessary to clarify the type and function of the device or platform. For example, smart home devices, wearable devices, iOS, Android, web platforms, etc. This will enable more detailed monitoring by integrating data from different devices and platforms.

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

[0053] The safe driving support system may further include a driving environment evaluation unit. The driving environment evaluation unit collects road conditions and weather information and takes this into consideration when determining whether or not driving is possible. For example, if the roads are icy or it is raining heavily, the system may advise the driver to refrain from driving. The driving environment evaluation unit may also collect traffic congestion information and suggest an alternative route if congestion is expected. This allows the system to comprehensively evaluate the driving environment and support safe driving.

[0054] The safe driving support system can further include a driving skill evaluation unit. The driving skill evaluation unit evaluates the user's driving skills and takes them into consideration when determining whether or not the user is able to drive. For example, it analyzes the frequency of sudden braking and sudden steering, and issues a warning if the driving skills are declining. The driving skill evaluation unit can also monitor eye movements while driving and detect distracted states. This allows for a comprehensive evaluation of driving skills and supports safe driving.

[0055] The safe driving support system can further include a driving history management unit. The driving history management unit records past driving history and uses it as a reference when determining whether or not a driver is allowed to drive. For example, if a driver has a history of having had an accident in the past, the system can advise the driver to refrain from driving. The driving history management unit can also analyze driving frequency and driving time, and encourage the driver to take a break if fatigue is accumulating. This makes it possible to support safe driving by taking past driving history into consideration.

[0056] The safe driving support system may further include a health management advice unit. The health management advice unit provides health management advice to the user based on the collected physical condition data. For example, if a lack of exercise is detected, the health management advice unit may recommend moderate exercise. Also, if a lack of sleep is detected, the health management advice unit may provide advice on improving the quality of sleep. This supports the user's health management and assists safe driving.

[0057] The safe driving support system can further include an emergency response unit. The emergency response unit provides a function for responding quickly when an abnormality is detected. For example, if an abnormality in heart rate or blood pressure is detected, a notification is automatically sent to an emergency contact. In addition, if an abnormality occurs while driving, it can also issue instructions to stop the vehicle in a safe place. This allows for a quick response in an emergency and supports safe driving.

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

[0059] Step 1: The health data collection unit collects the elderly person's health data. For example, it obtains data such as heart rate, blood pressure, sleep time, and number of steps from a smartwatch or health management app. It can also collect environmental data (e.g., temperature, humidity, and air quality) to evaluate the impact on health. Step 2: The cognitive function assessment unit evaluates the cognitive function of the elderly person based on the physical condition data collected by the physical condition data collection unit. For example, it compares the data with past data to check for abnormalities and issues a warning if a decline in cognitive function is suspected. It can also use generation AI to detect abnormal values ​​in the physical condition data in real time and immediately notify users when an abnormality occurs. Step 3: The driving feasibility determination unit determines whether the driver is able to drive based on the results of the assessment by the cognitive function assessment unit. For example, it determines whether the driver is able to drive based on the physical condition data and the results of the cognitive function assessment, and provides specific advice.

[0060] (Example 2) A safe driving support system according to an embodiment of the present invention is a system that evaluates the physical condition data and cognitive function of an elderly person and determines whether or not the elderly person is capable of driving. As a result, the safe driving support system can support safe driving by evaluating the physical condition data and cognitive function of an elderly person and determining whether or not the elderly person is capable of driving.

[0061] A safe driving support system according to an embodiment includes a physical condition data collection unit, a cognitive function evaluation unit, and a driving feasibility determination unit. The physical condition data collection unit collects physical condition data of an elderly person. For example, the physical condition data collection unit acquires data such as heart rate, blood pressure, sleep time, and number of steps from a smartwatch or a health management app. The physical condition data collection unit can also collect environmental data (e.g., temperature, humidity, and air quality) and evaluate the impact on the elderly person's physical condition. The cognitive function evaluation unit evaluates the elderly person's cognitive function based on the physical condition data collected by the physical condition data collection unit. For example, the cognitive function evaluation unit compares the data with past data to check for abnormalities and issues a warning if a decline in cognitive function is suspected. The cognitive function evaluation unit can also use a generation AI to detect abnormal values ​​in the physical condition data in real time and immediately notify the elderly person when an abnormality occurs. The driving feasibility determination unit determines whether the elderly person is able to drive based on the evaluation results by the cognitive function evaluation unit. For example, the driving feasibility determination unit determines whether the elderly person is able to drive based on the physical condition data and the evaluation results of the cognitive function and provides specific advice. As a result, the safe driving support system according to the embodiment can support safe driving by evaluating the physical condition data and cognitive function of the elderly person and determining whether or not they are able to drive.

[0062] The physical condition data collection unit can acquire data such as heart rate, blood pressure, sleep time, and step count from a smartwatch or a health management app. The physical condition data collection unit acquires data such as heart rate, blood pressure, sleep time, and step count from, for example, a smartwatch or a health management app. For example, a smartwatch is equipped with a heart rate measurement function and a GPS function, allowing it to collect data in real time. In addition, a health management app is equipped with a data recording method and analysis function, allowing it to manage the user's physical condition data in detail. As a result, collecting data from a smartwatch or a health management app enables a more detailed physical condition evaluation.

[0063] The cognitive function assessment unit can compare data with past data to check for abnormalities and issue a warning if a decline in cognitive function is suspected. The cognitive function assessment unit can, for example, compare data with past health checkup results and daily health records to detect abnormal heart rate and blood pressure values. The cognitive function assessment unit can also use generative AI to detect abnormal values ​​in health data in real time and immediately notify users when an abnormality occurs. This allows for early response by detecting abnormalities by comparing data with past data and issuing a warning if a decline in cognitive function is suspected.

[0064] The driving feasibility determination unit determines whether or not a user is able to drive based on the physical condition data and the evaluation results of cognitive function, and can provide advice such as, for example, "You'd better not drive today." The driving feasibility determination unit determines whether or not a user is able to drive based on the physical condition data and the evaluation results of cognitive function, and can provide specific advice such as, "You'd better not drive today." The driving feasibility determination unit can also use an emotion estimation function to evaluate the user's emotional state (e.g., stress level) and make a comprehensive judgment in combination with the physical condition data. This makes it possible to support safe driving for elderly people by determining whether or not a user is able to drive based on the physical condition data and the evaluation results of cognitive function, and providing specific advice.

[0065] Generative AI can detect abnormal values ​​in health data in real time and immediately notify users when an abnormality occurs. Generative AI can, for example, detect abnormal values ​​in health data in real time and immediately notify users when an abnormality occurs. For example, it can detect abnormal values ​​in heart rate or blood pressure and issue an alert. Generative AI can also use voice input and image analysis to reduce the burden on users when reporting their health condition. For example, it can use voice recognition technology to allow users to enter health data simply by speaking. This allows for the detection of abnormal values ​​in health data in real time and immediate notification, enabling a rapid response.

[0066] The emotion estimation function can evaluate the user's emotional state and make a comprehensive judgment in combination with physical condition data. The emotion estimation function, for example, evaluates the user's emotional state (e.g., stress level) and makes a comprehensive judgment in combination with physical condition data. For example, the emotion estimation function can be used to evaluate the user's stress level and make a comprehensive judgment in combination with physical condition data. For example, it can analyze the tendency for heart rate to increase on days with high stress. The emotion estimation function can also integrate data from different devices (e.g., smart home devices) to make a more detailed physical condition evaluation. This allows for a more accurate judgment on whether or not the user is fit to drive by evaluating the user's emotional state and making a comprehensive judgment in combination with physical condition data.

[0067] Using voice input or image analysis, the burden on users when reporting their physical condition can be reduced. Using voice input or image analysis, for example, the burden on users when reporting their physical condition can be reduced. For example, using voice recognition technology, a user can input physical condition data simply by speaking. In addition, using an emotion estimation function, the emotions of a user when inputting physical condition data can be estimated in real time, improving the accuracy of the input. For example, facial expressions and voice at the time of input can be analyzed to evaluate the emotional state. In this way, using voice input or image analysis, the burden on users when reporting their physical condition can be reduced.

[0068] Data from devices can be integrated to provide a more detailed assessment of physical condition. For example, data from smart home devices can be integrated to build a system that provides a more detailed assessment of physical condition. For example, data can be collected from smart scales and smart thermometers. It is also necessary to clarify the type and function of the device. For example, smart home devices, wearable devices, etc. This allows for a more detailed assessment of physical condition by integrating data from different devices.

[0069] The emotion estimation function estimates the emotion of a user when entering physical condition data in real time, thereby improving the accuracy of the input. The emotion estimation function estimates the emotion of a user when entering physical condition data in real time, thereby improving the accuracy of the input. For example, the emotion estimation function is used to build a system that estimates the emotion of a user when entering physical condition data in real time. For example, the emotion estimation function analyzes facial expressions and voice at the time of input and evaluates the emotional state. In this way, by using the emotion estimation function, the emotion of a user when entering physical condition data can be estimated in real time, thereby improving the accuracy of the input.

[0070] By incorporating daily behavioral data, it is possible to perform more accurate assessments of cognitive function. By incorporating daily behavioral data, for example, it is possible to perform more accurate assessments of cognitive function. For example, by incorporating daily behavioral data, a system can be built that improves the accuracy of cognitive function assessments. For example, the content of conversations and frequency of reading can be analyzed to evaluate changes in cognitive function. It is also necessary to clarify the type of daily behavioral data and how it is acquired. For example, the content of conversations, frequency of reading, etc. By incorporating daily behavioral data, it is possible to perform more accurate assessments of cognitive function.

[0071] Generative AI can visualize the results of cognitive function assessment, allowing users to intuitively understand them. For example, generative AI can be used to build a system that visualizes the results of cognitive function assessment. For example, the assessment results can be displayed using graphs or charts. It is also necessary to clarify the specific methods and techniques for visualization. For example, graph display, dashboard display, etc. In this way, visualizing the results of cognitive function assessment using generative AI will allow users to intuitively understand them.

[0072] The emotion estimation function can evaluate the impact of a user's emotional state on cognitive function and make a comprehensive judgment. The emotion estimation function can, for example, evaluate the impact of a user's emotional state on cognitive function and make a comprehensive judgment. For example, the emotion estimation function can be used to build a system that evaluates the impact of a user's emotional state on cognitive function. For example, analyzing the tendency for cognitive function to decline on days with high stress. In addition, the type and implementation method of the emotion estimation function must be clarified. For example, facial expression analysis, voice analysis, etc. As a result, the emotion estimation function can be used to evaluate the impact of a user's emotional state on cognitive function and make a comprehensive judgment.

[0073] By incorporating tests using games or puzzles, cognitive function can be evaluated while having fun. By incorporating tests using games or puzzles, cognitive function can be evaluated while having fun. For example, a system for evaluating cognitive function can be constructed by incorporating tests using games or puzzles. For example, cognitive function can be evaluated through simple puzzle games. It is also necessary to clarify the type and content of the games and puzzles. For example, puzzle games, quiz games, etc. This makes it possible to evaluate cognitive function while having fun using games and puzzles.

[0074] By making the cognitive function assessment algorithm multilingual, it can be adapted to users of different languages ​​and cultural backgrounds. By making the cognitive function assessment algorithm multilingual, it can be adapted to users of different languages ​​and cultural backgrounds, for example. For example, a system can be built that makes the cognitive function assessment algorithm multilingual. For example, it can support multiple languages ​​such as English, French, and Chinese. It is also necessary to clarify the specific methods and technologies for multilingual support. For example, translation algorithms, multilingual databases, etc. This will enable it to be adapted to users of different languages ​​and cultural backgrounds, improving the accuracy of cognitive function assessment.

[0075] The emotion estimation function can analyze the emotional reactions of a user during a cognitive function assessment and improve the accuracy of the assessment. The emotion estimation function, for example, analyzes the emotional reactions of a user during a cognitive function assessment and improves the accuracy of the assessment. For example, a system can be built using the emotion estimation function to analyze the emotional reactions of a user during a cognitive function assessment in real time. For example, facial expressions and voice during the assessment can be analyzed to evaluate the emotional state. It is also necessary to clarify the type and implementation method of the emotion estimation function. For example, facial expression analysis, voice analysis, etc. As a result, the emotion estimation function can be used to analyze the emotional reactions of a user during a cognitive function assessment and improve the accuracy of the assessment.

[0076] By incorporating driving history data, it is possible to make more accurate judgments about whether or not a person is capable of driving. By incorporating driving history data, for example, it is possible to make more accurate judgments about whether or not a person is capable of driving. For example, a system can be built that incorporates past driving history data to improve the accuracy of judgments about whether or not a person is capable of driving. For example, accident history and driving frequency can be analyzed to evaluate risk. It is also necessary to clarify the type of driving history data and how it is obtained. For example, accident history, driving frequency, etc. By incorporating past driving history data, it is possible to make more accurate judgments about whether or not a person is capable of driving.

[0077] The generation AI can provide an interactive guide to explain the driving feasibility judgment results to the user in an easy-to-understand manner. For example, the generation AI can be used to build a system that provides an interactive guide to explain the driving feasibility judgment results to the user in an easy-to-understand manner. For example, the driving feasibility judgment results can be visually displayed and the reasons explained in detail. In addition, the specific content of the interactive guide and how it is provided must be clarified. For example, a dialogue-style guide, a visual guide, etc. In this way, the generation AI can be used to explain the driving feasibility judgment results in an easy-to-understand manner, making it easier for the user to understand.

[0078] The emotion estimation function can evaluate the impact of a user's emotional state on whether or not they are able to drive, and make a comprehensive judgment. The emotion estimation function can, for example, evaluate the impact of a user's emotional state on whether or not they are able to drive, and make a comprehensive judgment. For example, a system can be built using the emotion estimation function to evaluate the impact of a user's emotional state on whether or not they are able to drive. For example, advising the user to refrain from driving on days with high stress. It is also necessary to clarify the type and implementation method of the emotion estimation function. For example, facial expression analysis, voice analysis, etc. As a result, by using the emotion estimation function, it is possible to evaluate the impact of a user's emotional state on whether or not they are able to drive, and make a comprehensive judgment.

[0079] It is possible to consider the availability of transportation means and propose alternative means. For example, we will build a system that considers the availability of other transportation means and proposes alternative means based on the results of a driving feasibility assessment. For example, we will check the operation status of public transportation and recommend its use. It is also necessary to clarify the type of transportation means and how to use it. For example, public transportation, bicycle, etc. This will consider the availability of other transportation means and propose alternative means, thereby expanding the user's transportation options.

[0080] By making the algorithm for determining whether a person is fit to drive customizable, it can accommodate users of different age groups and health conditions. By making the algorithm for determining whether a person is fit to drive customizable, it can accommodate users of different age groups and health conditions. For example, a system can be built that allows customization of the algorithm for determining whether a person is fit to drive so that it can accommodate users of different age groups and health conditions. For example, evaluation criteria can be set according to age and health condition. It is also necessary to clarify the specific ranges and classification methods for age groups and health conditions. For example, people in their 20s, 30s, elderly, etc. This will make it possible to accommodate users of different age groups and health conditions, improving the accuracy of determining whether a person is fit to drive.

[0081] The emotion estimation function can analyze the user's emotional response to the results of the driving feasibility judgment and improve the algorithm based on the feedback. The emotion estimation function, for example, analyzes the user's emotional response to the results of the driving feasibility judgment and improves the algorithm based on the feedback. For example, the emotion estimation function can be used to build a system that analyzes the user's emotional response to the results of the driving feasibility judgment in real time. For example, the user's facial expression and voice in response to the judgment result can be analyzed. In addition, the type and implementation method of the emotion estimation function must be clarified. For example, facial expression analysis, voice analysis, etc. In this way, the emotion estimation function can be used to analyze the user's emotional response to the results of the driving feasibility judgment and improve the algorithm based on the feedback.

[0082] Notification content can be customized according to user preferences and provided in a more acceptable format. Notification content can be customized according to user preferences and provided in a more acceptable format, for example. For example, a system can be built that customizes notification content according to user preferences. For example, the wording and format of notifications can be changed to suit the user's preferences. It is also necessary to clarify the specific type of notification content and how it is provided. For example, text notification, audio notification, etc. This allows notification content to be customized according to user preferences, making it possible to provide notifications in a more acceptable format.

[0083] The generation AI can optimize the timing of notifications and send them at the time when the user is most likely to receive them. The generation AI can, for example, optimize the timing of notifications and send them at the time when the user is most likely to receive them. For example, a system that optimizes the timing of notifications can be built using the generation AI. For example, the user's behavioral patterns can be analyzed to determine the optimal notification time. It is also necessary to clarify the specific method and criteria for setting the timing of notifications. For example, the user's activity patterns, optimal time periods, etc. By using the generation AI to optimize the timing of notifications, it is possible to send them at the time when the user is most likely to receive them.

[0084] The emotion estimation function can analyze the user's emotional reaction when receiving a notification and improve the content of the notification. For example, the emotion estimation function can be used to build a system that analyzes the user's emotional reaction when receiving a notification in real time. For example, the facial expression and voice when receiving the notification can be analyzed. It is also necessary to clarify the type and implementation method of the emotion estimation function. For example, facial expression analysis, voice analysis, etc. By using the emotion estimation function, the user's emotional reaction when receiving a notification can be analyzed and the content of the notification can be improved.

[0085] Notification methods can be diversified, allowing notifications to be sent via smart speakers or wearable devices. Notification methods can be diversified, allowing notifications to be sent via smart speakers or wearable devices. For example, a system can be built that diversifies notification methods and sends notifications via smart speakers. For example, the notification content can be conveyed by voice. It is also necessary to clarify the specific types of notification methods and how they are provided. For example, smart speakers, wearable devices, etc. By diversifying notification methods, notifications can be sent via smart speakers or wearable devices.

[0086] By combining notification means, it is possible to provide information to the user in the most optimal form. By combining notification means, for example, information can be provided to the user in the most optimal form. For example, a system can be built that combines different notification means to provide information to the user in the most optimal form. For example, audio notification and visual notification can be provided simultaneously. It is also necessary to clarify the specific type of notification means and the method of providing it. For example, audio notification, visual notification, etc. This allows information to be provided to the user in the most optimal form by combining different notification means.

[0087] The emotion estimation function can monitor the user's emotional response to the notification content in real time and adjust the notification method. For example, the emotion estimation function can be used to build a system that monitors the user's emotional response to the notification content in real time. For example, the emotion estimation function can analyze facial expressions and voice when the notification is received. It is also necessary to clarify the type and implementation method of the emotion estimation function. For example, facial expression analysis, voice analysis, etc. As a result, the emotion estimation function can be used to monitor the user's emotional response to the notification content in real time and adjust the notification method.

[0088] Storing monitoring data on the cloud can enable long-term data analysis. Storing monitoring data on the cloud can enable long-term data analysis, for example. For example, building a system to safely store monitoring data on the cloud. For example, encrypting data and controlling access. It is also necessary to clarify the specific type of cloud and security measures. For example, AWS, Azure, data encryption, etc. This allows for long-term data analysis by safely storing monitoring data on the cloud.

[0089] Generative AI can detect abnormal values ​​in monitoring data in real time and immediately propose countermeasures. Generative AI can, for example, detect abnormal values ​​in monitoring data in real time and immediately propose countermeasures. For example, a system can be built using generative AI to detect abnormal values ​​in monitoring data in real time. For example, abnormal values ​​in heart rate and blood pressure can be detected. It is also necessary to clarify the type and functions of the generative AI. For example, anomaly detection algorithms, notification functions, etc. This makes it possible to use generative AI to detect abnormal values ​​in monitoring data in real time and immediately propose countermeasures.

[0090] The emotion estimation function can evaluate the emotional state of a user being monitored and make a comprehensive judgment in combination with physical condition data. The emotion estimation function, for example, evaluates the emotional state of a user being monitored and makes a comprehensive judgment in combination with physical condition data. For example, the emotion estimation function can be used to build a system that evaluates the emotional state of a user being monitored in real time. For example, the emotional state can be evaluated by analyzing facial expressions and voice. It is also necessary to clarify the type and implementation method of the emotion estimation function. For example, facial expression analysis, voice analysis, etc. As a result, the emotion estimation function can be used to evaluate the emotional state of a user being monitored and make a comprehensive judgment in combination with physical condition data.

[0091] Monitoring data can be shared with family members and medical professionals, enabling collaborative health management of the elderly. For example, a system for sharing monitoring data with family members and medical professionals can be built. For example, data can be shared on the cloud and made accessible in real time. It is also necessary to clarify the specific scope and method of involvement of family members and medical professionals. For example, relatives, caregivers, doctors, nurses, care workers, etc. This allows for collaborative health management of the elderly by sharing monitoring data with family members and medical professionals.

[0092] Data from devices and platforms can be integrated to enable more detailed monitoring. For example, data from different devices and platforms can be integrated to build a system for more detailed monitoring. For example, data from smart watches and smart home devices can be integrated. It is also necessary to clarify the type and function of the device or platform. For example, smart home devices, wearable devices, iOS, Android, web platforms, etc. This will enable more detailed monitoring by integrating data from different devices and platforms.

[0093] The emotion estimation function can analyze a user's emotional response to monitoring data and improve monitoring methods. For example, the emotion estimation function can be used to build a system that analyzes a user's emotional response to monitoring data in real time. For example, the emotion estimation function can analyze facial expressions and voice when receiving the data. It is also necessary to clarify the type and implementation method of the emotion estimation function. For example, facial expression analysis, voice analysis, etc. By using the emotion estimation function, it is possible to analyze a user's emotional response to monitoring data and improve monitoring methods.

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

[0095] The safe driving support system may further include a driving environment evaluation unit. The driving environment evaluation unit collects road conditions and weather information and takes this into consideration when determining whether or not driving is possible. For example, if the roads are icy or it is raining heavily, the system may advise the driver to refrain from driving. The driving environment evaluation unit may also collect traffic congestion information and suggest an alternative route if congestion is expected. This allows the system to comprehensively evaluate the driving environment and support safe driving.

[0096] The safe driving support system can further include a driving skill evaluation unit. The driving skill evaluation unit evaluates the user's driving skills and takes them into consideration when determining whether or not the user is able to drive. For example, it analyzes the frequency of sudden braking and sudden steering, and issues a warning if the driving skills are declining. The driving skill evaluation unit can also monitor eye movements while driving and detect distracted states. This allows for a comprehensive evaluation of driving skills and supports safe driving.

[0097] The safe driving support system can further include a driving history management unit. The driving history management unit records past driving history and uses it as a reference when determining whether or not a driver is allowed to drive. For example, if a driver has a history of having had an accident in the past, the system can advise the driver to refrain from driving. The driving history management unit can also analyze driving frequency and driving time, and encourage the driver to take a break if fatigue is accumulating. This makes it possible to support safe driving by taking past driving history into consideration.

[0098] The safe driving support system may further include a health management advice unit. The health management advice unit provides health management advice to the user based on the collected physical condition data. For example, if a lack of exercise is detected, the health management advice unit may recommend moderate exercise. Also, if a lack of sleep is detected, the health management advice unit may provide advice on improving the quality of sleep. This supports the user's health management and assists safe driving.

[0099] The safe driving support system can further include an emergency response unit. The emergency response unit provides a function for responding quickly when an abnormality is detected. For example, if an abnormality in heart rate or blood pressure is detected, a notification is automatically sent to an emergency contact. In addition, if an abnormality occurs while driving, it can also issue instructions to stop the vehicle in a safe place. This allows for a quick response in an emergency and supports safe driving.

[0100] The determination unit can estimate the user's emotions and determine whether or not the user is allowed to drive based on the estimated user's emotions. For example, if the user's stress level is high, the determination unit can advise the user to refrain from driving. Furthermore, the emotion estimation function can be used to recommend driving if the user is relaxed. This allows the system to support safe driving by taking the user's emotional state into consideration.

[0101] The emotion estimation function can evaluate a user's emotional state and make a comprehensive judgment in combination with physical condition data. For example, the emotion estimation function can be used to evaluate a user's stress level and make a comprehensive judgment in combination with physical condition data. For example, it can analyze the tendency for heart rate to increase on days with high stress. The emotion estimation function can also integrate data from different devices (e.g., smart home devices) to perform a more detailed physical condition evaluation. This allows for a more accurate judgment on whether or not a user is fit to drive by evaluating the user's emotional state and making a comprehensive judgment in combination with physical condition data.

[0102] The emotion estimation function estimates the emotion a user is feeling when entering physical condition data in real time, thereby improving the accuracy of the input. For example, the emotion estimation function can be used to build a system that estimates the emotion a user is feeling when entering physical condition data in real time. For example, the emotion estimation function can analyze facial expressions and voices at the time of entry to evaluate the emotional state. In this way, the emotion estimation function can estimate the emotion a user is feeling when entering physical condition data in real time, thereby improving the accuracy of the input.

[0103] The emotion estimation function can evaluate the impact of a user's emotional state on cognitive function and make a comprehensive judgment. For example, a system can be built using the emotion estimation function to evaluate the impact of a user's emotional state on cognitive function. For example, analyzing the tendency for cognitive function to decline on days with high stress. It is also necessary to clarify the type and implementation method of the emotion estimation function. For example, facial expression analysis, voice analysis, etc. By using the emotion estimation function, it is possible to evaluate the impact of a user's emotional state on cognitive function and make a comprehensive judgment.

[0104] The emotion estimation function can analyze the emotional reactions of a user during a cognitive function assessment and improve the accuracy of the assessment. For example, a system can be built using the emotion estimation function to analyze the emotional reactions of a user during a cognitive function assessment in real time. For example, facial expressions and voice during the assessment can be analyzed to evaluate the emotional state. It is also necessary to clarify the type and implementation method of the emotion estimation function. For example, facial expression analysis, voice analysis, etc. By using the emotion estimation function, it is possible to analyze the emotional reactions of a user during a cognitive function assessment and improve the accuracy of the assessment.

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

[0106] Step 1: The health data collection unit collects the elderly person's health data. For example, it obtains data such as heart rate, blood pressure, sleep time, and number of steps from a smartwatch or health management app. It can also collect environmental data (e.g., temperature, humidity, and air quality) to evaluate the impact on health. Step 2: The cognitive function assessment unit evaluates the cognitive function of the elderly person based on the physical condition data collected by the physical condition data collection unit. For example, it compares the data with past data to check for abnormalities and issues a warning if a decline in cognitive function is suspected. It can also use generation AI to detect abnormal values ​​in the physical condition data in real time and immediately notify users when an abnormality occurs. Step 3: The driving feasibility determination unit determines whether the driver is able to drive based on the results of the assessment by the cognitive function assessment unit. For example, it determines whether the driver is able to drive based on the physical condition data and the results of the cognitive function assessment, and provides specific advice.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0109] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0151] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a physical condition data collection unit that collects physical condition data of the elderly person; a cognitive function assessment unit that assesses the cognitive function of an elderly person based on the physical condition data collected by the physical condition data collection unit; a driving feasibility determination unit that determines whether or not the driver is able to drive based on the results of the evaluation by the cognitive function evaluation unit. A system characterized by:

2. The physical condition data collection unit Get heart rate, blood pressure, sleep, and step count data from your smartwatch or health management app 2. The system of claim 1.

3. The cognitive function assessment unit It compares the data with past data to check for any abnormalities and issues a warning if a decline in cognitive function is suspected.

2. The system of claim 1.

4. The driving possibility determination unit Based on the physical condition data and the cognitive function evaluation results, the system determines whether the driver is able to drive and provides advice such as "You'd better not drive today." 2. The system of claim 1.

5. The emotion estimation function is Evaluate the user's emotional state and make a comprehensive judgment in combination with the physical condition data.

2. The system of claim 1.

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