System

Abstract

An object of a system according to an embodiment is to comprehensively monitor a living situation and a health condition of an elderly person and provide appropriate support.SOLUTION: A system according to an embodiment includes a monitoring function, health management support, a smart home, and dementia prevention / support. The monitoring function monitors the living situation of the elderly person. The healthcare support monitors health data of the elderly person. The smart home supports the living environment of the elderly. The dementia prevention / support monitors cognitive states and behavior patterns of elderly people.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 not being able to comprehensively monitor the living conditions and health status of the elderly and provide appropriate support.

[0005] The system according to the embodiment aims to comprehensively monitor the living conditions and health conditions of elderly people and provide appropriate support. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring function, health management support, a smart home, and dementia prevention and support. The monitoring function monitors the living conditions of the elderly. The health management support monitors the health data of the elderly. The smart home supports the living environment of the elderly. The dementia prevention and support monitors the cognitive state and behavioral patterns of the elderly. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively monitor the living conditions and health conditions of elderly people and provide appropriate support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The comprehensive support system according to an embodiment of the present invention is a system that monitors the living conditions and health conditions of elderly people and provides appropriate support, thereby enabling elderly people to live independently, safely, and comfortably.

[0029] A comprehensive support system according to an embodiment includes a monitoring function, health management support, a smart home, and dementia prevention and support. The monitoring function monitors the elderly's living conditions. For example, it monitors heart rate and activity levels using IoT devices such as smartwatches, mobile phones, and monitoring batteries. The health management support monitors the elderly's health data. For example, it analyzes daily diet, exercise, and sleep data and provides advice for prevention and early detection. The smart home supports the elderly's living environment. For example, it learns daily rhythms and preferences and automatically controls lighting, temperature, and safety. The dementia prevention and support monitors the elderly's cognitive status and behavioral patterns. For example, it provides advice on cognitive training and lifestyle support. In this way, the comprehensive support system comprehensively supports the elderly's living conditions, health status, living environment, and cognitive status.

[0030] The monitoring function monitors the elderly person's heart rate and activity level, and can issue notifications and alerts if it detects abnormal values. For example, if the heart rate suddenly rises, the AI ​​will call the elderly person and ask, "Are you OK?" It can also notify family members via LINE that "Mom's heart rate is high," and send an alert to the care center. This allows for the rapid detection of abnormalities in the elderly and prompts appropriate action.

[0031] Health management support can monitor and analyze the daily diet, exercise, and sleep data of elderly people. For example, health management support can analyze diet data and advise, "You're not getting enough vegetables." It can also suggest, based on exercise data, that "You should walk a bit more." It can also analyze sleep data and provide support such as, "You've been sleeping lightly lately, so you should consider visiting a medical institution." This allows for a detailed understanding of the elderly person's daily health status and provides appropriate advice.

[0032] Smart homes can learn the lifestyle rhythms and preferences of elderly people and automatically control lighting, temperature, and safety. For example, a smart home can automatically turn on the lights when they go to the toilet at night. It can also turn on the heating if the room temperature drops. It can also monitor the opening and closing of doors and windows and send out alerts if there is any suspicious activity. This optimizes the living environment for the elderly and provides a comfortable life.

[0033] Dementia prevention and support can monitor the cognitive state and behavioral patterns of elderly people and provide advice on cognitive training and lifestyle support. For example, dementia prevention and support can analyze daily behavioral patterns to detect signs of dementia, such as "You've been asking the same questions repeatedly lately." It can also suggest appropriate training and provide lifestyle support advice, such as "Let's go for a walk today." This helps maintain the cognitive function of elderly people and improve their quality of life.

[0034] The monitoring function can analyze an elderly person's walking pattern, predict the risk of falling, and issue a warning in advance. For example, the monitoring function uses AI to analyze an elderly person's walking pattern and predict the risk of falling. For example, it issues a warning if their walking speed suddenly slows or their footing becomes unsteady. The AI ​​also collects walking data from the elderly person and analyzes it to predict the risk of falling. For example, it issues a warning if their stride becomes narrower or they appear to be dragging their feet. Furthermore, the AI ​​analyzes an elderly person's walking pattern, predicts the risk of falling, and issues a warning in advance. For example, it issues a warning if they lose balance while walking. This allows the system to predict the risk of falling in elderly people in advance and encourages appropriate action.

[0035] The monitoring function can monitor the health of an elderly person's pet and notify the elderly person if an abnormality is detected in the pet. For example, the monitoring function can monitor the health of an elderly person's pet and notify the elderly person if an abnormality is detected. For example, a notification can be sent if the pet's appetite decreases. The function can also collect pet activity data and analyze it using AI to monitor the health condition. For example, an abnormality can be detected if the pet is moving less than usual. The function can also monitor the health of an elderly person's pet and notify the elderly person if an abnormality is detected in the pet. For example, a notification can be sent if the pet's body temperature is abnormally high. This allows the elderly person to understand the health condition of their pet and prompts them to take appropriate action if an abnormality occurs.

[0036] The monitoring function can analyze the hobbies and activity history of elderly people and suggest events and activities that they might be interested in. For example, the monitoring function can analyze the hobbies and activity history of elderly people and suggest events and activities that they might be interested in. For example, it can suggest new events based on data on events they have attended in the past. It can also collect data on elderly people's hobbies and analyze that data using AI to suggest activities that they might be interested in. For example, it can suggest workshops and courses related to hobbies. It can also analyze the activity history of elderly people and suggest events and activities that they might be interested in. For example, it can suggest new tourist spots based on data on places they have visited in the past. This allows it to suggest events and activities that they might be interested in based on the hobbies and activity history of elderly people.

[0037] Health management support can perform image analysis of the elderly's dietary content, evaluate nutritional balance, and provide specific dietary improvement suggestions. Health management support, for example, involves AI analyzing images of the elderly's dietary content to evaluate nutritional balance. For example, a photo of the meal is taken, and AI analyzes the image to point out nutrient deficiencies. Alternatively, the elderly can upload a photo of their meal, and AI analyzes the image to evaluate nutritional balance. For example, if there is a lack of vegetables, specific dietary improvement suggestions are provided. Furthermore, AI performs image analysis of the elderly's dietary content, evaluates nutritional balance, and provides specific dietary improvement suggestions. For example, if there is a lack of protein, appropriate ingredients are suggested. In this way, the elderly's dietary content is analyzed, nutritional balance is evaluated, and specific improvement suggestions are provided.

[0038] Health management support can analyze the exercise data of elderly people and create individually customized exercise programs. For example, health management support uses AI to analyze the exercise data of elderly people and create individually customized exercise programs. For example, it can suggest an appropriate walking plan based on walking data. In addition, the exercise data of elderly people is collected and AI analyzes that data to create customized exercise programs. For example, it can adjust the frequency and intensity of strength training. Furthermore, AI can analyze the exercise data of elderly people and create individually customized exercise programs. For example, it can suggest appropriate aerobic exercise based on heart rate data. In this way, the exercise data of elderly people is analyzed and individually customized exercise programs are provided.

[0039] Health management support can monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. Health management support can, for example, monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. For example, an alarm can sound when it's time to take the medication. It can also collect medication data from elderly people and use AI to analyze the data to provide reminders to prevent them from forgetting to take their medication. For example, it can send notifications when it's time to take the medication. It can also monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. For example, it can provide voice reminders when it's time to take the medication. This provides reminders to prevent elderly people from forgetting to take their medication.

[0040] Health management support can monitor the amount of fluid intake of elderly people and encourage them to drink appropriately. Health management support can, for example, monitor the amount of fluid intake of elderly people and encourage them to drink appropriately. For example, it can send a notification if fluid intake is insufficient. It can also collect fluid intake data of elderly people and analyze the data with AI to encourage them to drink appropriately. For example, it can provide a reminder if fluid intake is low. It can also monitor the amount of fluid intake of elderly people and encourage them to drink appropriately. For example, it can provide a voice reminder if fluid intake is insufficient. This allows it to monitor the amount of fluid intake of elderly people and encourage them to drink appropriately.

[0041] A smart home can learn the lifestyle rhythms of the elderly and automatically control home appliances at the optimal times. In a smart home, for example, AI learns the lifestyle rhythms of the elderly and automatically controls home appliances at the optimal times. For example, opening the curtains at the time it's time to wake up in the morning. Data on the elderly's lifestyle is also collected, and AI analyzes that data to automatically control home appliances at the optimal times. For example, turning on the microwave at mealtimes. Furthermore, AI learns the lifestyle rhythms of the elderly and automatically controls home appliances at the optimal times. For example, turning off the lights at bedtime at night. In this way, home appliances are automatically controlled to match the lifestyle rhythms of the elderly.

[0042] A smart home can recognize the voices of the elderly and allow them to operate home appliances with voice commands. For example, a smart home can use AI to recognize the voices of the elderly and allow them to operate home appliances with voice commands. For example, saying "Turn on the TV" will turn on the TV. Data on the elderly's voice can also be collected and analyzed by AI to enable them to operate home appliances with voice commands. For example, saying "Turn on the air conditioner" will turn on the air conditioner. Furthermore, AI can recognize the voices of the elderly and allow them to operate home appliances with voice commands. For example, saying "Turn off the lights" will turn off the lights. This allows the elderly to operate home appliances with voice commands.

[0043] Smart homes can automatically strengthen security systems when elderly people leave the home, ensuring safety when they are away from home. For example, smart homes can automatically strengthen security systems when elderly people leave the home, ensuring safety when they are away from home. For example, they can automatically lock doors. They can also collect data on when elderly people leave the home and use AI to analyze that data to strengthen security systems when they leave the home. For example, they can turn on window sensors. They can also automatically strengthen security systems when elderly people leave the home, ensuring safety when they are away from home. For example, they can automatically activate surveillance cameras. This automatically strengthens security systems when elderly people leave the home, ensuring safety when they are away from home.

[0044] A smart home can monitor visitors to an elderly person, recognize the visitor's face, and determine whether or not the visitor is trustworthy. For example, a smart home can monitor visitors to an elderly person, recognize the visitor's face, and determine whether or not the visitor is trustworthy. For example, the faces of family and friends can be registered. Furthermore, facial data of the visitor is collected and AI analyzes the data to determine whether or not the visitor is trustworthy. For example, a warning can be issued if the face is not registered. Furthermore, a smart home can monitor visitors to an elderly person, recognize the visitor's face, and determine whether or not the visitor is trustworthy. For example, the visitor's face can be analyzed in real time to evaluate their trustworthiness. In this way, visitors to an elderly person can be monitored and their trustworthiness determined.

[0045] Dementia prevention and support can analyze the content of conversations between elderly people and detect cognitive decline at an early stage. For example, dementia prevention and support uses AI to analyze the content of conversations between elderly people and detect cognitive decline at an early stage. For example, cognitive decline can be detected if the same questions are asked repeatedly. Conversation data between elderly people is also collected, and AI analyzes that data to detect cognitive decline at an early stage. For example, cognitive decline can be detected if the word choice changes. Furthermore, AI analyzes the content of conversations between elderly people and detects cognitive decline at an early stage. For example, cognitive decline can be detected if the flow of conversation becomes unnatural. In this way, the content of conversations between elderly people can be analyzed and cognitive decline can be detected at an early stage.

[0046] Dementia prevention and support can analyze the behavioral patterns of elderly people, evaluate the effectiveness of cognitive training, and provide the optimal program. For example, in dementia prevention and support, AI analyzes the behavioral patterns of elderly people and evaluates the effectiveness of cognitive training. For example, it measures the effectiveness of training based on daily behavioral data. In addition, behavioral data of elderly people is collected, and AI analyzes that data to evaluate the effectiveness of cognitive training. For example, it compares behavioral patterns before and after training. Furthermore, AI analyzes the behavioral patterns of elderly people, evaluates the effectiveness of cognitive training, and provides the optimal program. For example, it changes the training content if no effect is seen. In this way, the behavioral patterns of elderly people are analyzed, the effectiveness of cognitive training is evaluated, and the optimal program is provided.

[0047] Dementia prevention and support provides cognitive training based on the hobbies and interests of the elderly, allowing them to maintain their cognitive function while having fun. Dementia prevention and support, for example, provides cognitive training based on the hobbies and interests of the elderly. For example, training using puzzles or crossword puzzles is proposed. In addition, hobby data of the elderly is collected and AI analyzes the data to provide cognitive training based on the hobbies. For example, training using music or painting is proposed. Furthermore, cognitive training based on the hobbies and interests of the elderly is provided, allowing them to maintain their cognitive function while having fun. For example, training using games related to the hobbies is proposed. In this way, cognitive training based on the hobbies and interests of the elderly is provided, allowing them to maintain their cognitive function while having fun.

[0048] Dementia prevention and support can suggest online community activities to promote social interaction among the elderly. Dementia prevention and support, for example, suggests online community activities to promote social interaction among the elderly. For example, online hobby circles or club activities are suggested. Furthermore, data on social interactions among the elderly is collected and AI analyzes the data to suggest online community activities. For example, online interactions with friends are suggested. Furthermore, online community activities are suggested to promote social interaction among the elderly. For example, online volunteer activities are suggested. In this way, online community activities are suggested to promote social interaction among the elderly.

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

[0050] A comprehensive support system monitors the living conditions and health status of elderly people and provides appropriate support. For example, the monitoring function monitors the living conditions of elderly people. Heart rate and activity levels can be monitored using IoT devices such as smartwatches, mobile phones, and monitoring batteries. Health management support monitors the health data of elderly people. Daily diet, exercise, and sleep data can be analyzed and advice for prevention and early detection can be provided. Smart homes support the living environment of elderly people. They can learn daily rhythms and preferences and automatically control lighting, temperature, and safety. Dementia prevention and support monitors the cognitive status and behavioral patterns of elderly people and provide advice on cognitive training and lifestyle support. In this way, comprehensive support systems provide comprehensive support for the living conditions, health status, living environment, and cognitive status of elderly people.

[0051] The monitoring function monitors the elderly person's heart rate and activity level, and can issue notifications and alerts if it detects abnormal values. For example, if the heart rate suddenly rises, the AI ​​will call the elderly person and ask, "Are you OK?" It can also notify family members via LINE that "Mom's heart rate is high," and send an alert to the care center. This makes it possible to quickly detect abnormalities in the elderly and prompt appropriate action.

[0052] Health management support can monitor and analyze the daily diet, exercise, and sleep data of elderly people. For example, by analyzing dietary data, it can advise, "You're not getting enough vegetables." It can also suggest, based on exercise data, that "You should walk a bit more." It can also analyze sleep data and provide support such as, "You've been sleeping lightly lately, so you should consider visiting a medical institution." This allows for a detailed understanding of the elderly person's daily health condition and the provision of appropriate advice.

[0053] Smart homes can learn the lifestyle rhythms and preferences of elderly people and automatically control lighting, temperature, and safety. For example, the lights can be turned on automatically when an elderly person goes to the toilet at night. The heating can also be turned on if the room temperature drops. In addition, the system can monitor the opening and closing of doors and windows and send alerts if there is any suspicious activity. This can optimize the living environment for the elderly and provide them with a comfortable life.

[0054] Dementia prevention and support can monitor the cognitive status and behavioral patterns of elderly people and provide advice on cognitive training and lifestyle support. For example, by analyzing daily behavioral patterns, it can detect signs of dementia, such as "You've been asking the same questions repeatedly lately." It can also suggest appropriate training. Furthermore, it can provide lifestyle support advice, such as "Let's go for a walk today." This can help maintain the cognitive function of elderly people and improve their quality of life.

[0055] The monitoring function can analyze an elderly person's walking pattern, predict the risk of falling, and issue a warning in advance. For example, AI can analyze an elderly person's walking pattern and predict the risk of falling. A warning can be issued if their walking speed suddenly slows or their footing becomes unsteady. In addition, AI can collect an elderly person's walking data and analyze it to predict the risk of falling. A warning can be issued if the stride becomes narrower or if a shuffling movement is observed. Furthermore, AI can analyze an elderly person's walking pattern and predict the risk of falling and issue a warning in advance. A warning can be issued if a movement that causes loss of balance is observed while walking. This makes it possible to predict the risk of falling in elderly people in advance and encourage appropriate measures.

[0056] The monitoring function can monitor the health of elderly people's pets and notify the elderly if an abnormality is detected. For example, it can monitor the health of elderly people's pets and notify the elderly if an abnormality is detected. It can notify the elderly if the pet's appetite decreases. It can also collect pet activity data and analyze it using AI to monitor its health. It can detect an abnormality if the pet is less active than usual. It can also monitor the health of elderly people's pets and notify the elderly if an abnormality is detected. It can notify the elderly if the pet's body temperature is abnormally high. This allows the elderly to understand the health of their pets and prompt appropriate action in the event of an abnormality.

[0057] The monitoring function can analyze the hobbies and activity history of elderly people and suggest events and activities that they may be interested in. For example, it can analyze the hobbies and activity history of elderly people and suggest events and activities that they may be interested in. It can suggest new events based on data from events they have previously attended. It can also collect data on elderly people's hobbies and use AI to analyze that data to suggest activities that they may be interested in. It can suggest workshops and courses related to hobbies. It can also analyze the activity history of elderly people and suggest events and activities that they may be interested in. It can suggest new tourist spots based on data from places they have visited in the past. This makes it possible to suggest events and activities that they may be interested in based on the hobbies and activity history of elderly people.

[0058] Health management support can analyze images of an elderly person's diet, evaluate their nutritional balance, and provide specific suggestions for dietary improvement. For example, AI can analyze images of an elderly person's diet and evaluate their nutritional balance. A photo of the meal can be taken, and AI can analyze the image to identify nutrient deficiencies. Alternatively, an elderly person can upload a photo of their meal, and AI can analyze the image to evaluate the nutritional balance. If there is a lack of vegetables, specific suggestions for dietary improvement can be provided. Furthermore, AI can analyze images of an elderly person's diet, evaluate their nutritional balance, and provide specific suggestions for dietary improvement. If there is a lack of protein, appropriate ingredients can be suggested. This makes it possible to analyze an elderly person's diet, evaluate their nutritional balance, and provide specific suggestions for improvement.

[0059] Health management support can analyze the exercise data of elderly people and create individually customized exercise programs. For example, AI can analyze the exercise data of elderly people and create individually customized exercise programs. An appropriate walking plan can be suggested based on walking data. In addition, the exercise data of elderly people can be collected and AI can analyze the data to create customized exercise programs. The frequency and intensity of strength training can be adjusted. Furthermore, AI can analyze the exercise data of elderly people and create individually customized exercise programs. Appropriate aerobic exercise can be suggested based on heart rate data. In this way, it is possible to analyze the exercise data of elderly people and provide individually customized exercise programs.

[0060] Health management support can monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. For example, it can monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. It can sound an alarm when it's time to take the medication. It can also collect medication data from elderly people and use AI to analyze the data to provide reminders to prevent them from forgetting to take their medication. It can send notifications when it's time to take the medication. It can also monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. It can provide voice reminders when it's time to take the medication. This makes it possible to provide reminders to prevent elderly people from forgetting to take their medication.

[0061] Health management support can monitor the elderly's water intake and encourage them to drink appropriately. For example, it can monitor the elderly's water intake and encourage them to drink appropriately. It can send notifications if water intake is insufficient. It can also collect the elderly's water intake data and use AI to analyze the data to encourage appropriate hydration. It can provide reminders if water intake is low. It can also monitor the elderly's water intake and encourage them to drink appropriately. It can provide voice reminders if water intake is insufficient. This makes it possible to monitor the elderly's water intake and encourage them to drink appropriately.

[0062] Smart homes can learn the lifestyle rhythms of the elderly and automatically control home appliances at the optimal times. For example, AI can learn the lifestyle rhythms of the elderly and automatically control home appliances at the optimal times. Curtains can be opened when it's time to wake up in the morning. In addition, AI can collect lifestyle data of the elderly and analyze it to automatically control home appliances at the optimal times. The microwave can be turned on when it's time to eat. Furthermore, AI can learn the lifestyle rhythms of the elderly and automatically control home appliances at the optimal times. Lights can be turned off when it's time to go to bed at night. In this way, home appliances can be automatically controlled to suit the lifestyle rhythms of the elderly.

[0063] A smart home can recognize the voices of the elderly and allow them to control home appliances with voice commands. For example, AI can recognize the voices of the elderly and allow them to control home appliances with voice commands. When they say, "Turn on the TV," the TV will turn on. Also, AI can collect voice data from the elderly and analyze it to allow them to control home appliances with voice commands. When they say, "Turn on the air conditioner," the air conditioner will turn on. Furthermore, AI can recognize the voices of the elderly and allow them to control home appliances with voice commands. When they say, "Turn off the lights," the lights will turn off. This allows the elderly to control home appliances with voice commands.

[0064] Smart homes can automatically strengthen security systems when elderly people leave the home, ensuring safety when the home is unattended. For example, security systems can be automatically strengthened when elderly people leave the home, ensuring safety when the home is unattended. Door locks can be automatically locked. Data on elderly people's departure times can be collected and analyzed by AI to strengthen the security system when the home is unattended. Window sensors can be turned on. Furthermore, security systems can be automatically strengthened when elderly people leave the home, ensuring safety when the home is unattended. Surveillance cameras can be automatically activated. This allows security systems to be automatically strengthened when elderly people leave the home, ensuring safety when the home is unattended.

[0065] Smart homes can monitor visitors to elderly people, recognize their faces, and determine whether they are trustworthy. For example, they can monitor visitors to elderly people, recognize their faces, and determine whether they are trustworthy. The faces of family and friends can be registered. In addition, the visitor's facial data can be collected and AI can analyze the data to determine whether they are trustworthy. A warning can be issued if the face is not registered. Furthermore, they can monitor visitors to elderly people, recognize their faces, and determine whether they are trustworthy. The visitor's face can be analyzed in real time and their trustworthiness evaluated. This makes it possible to monitor visitors to elderly people and determine whether they are trustworthy.

[0066] Dementia prevention and support can analyze the content of elderly people's conversations and detect cognitive decline early. For example, AI can analyze the content of elderly people's conversations and detect cognitive decline early. It can detect cognitive decline if the same questions are repeated. It can also collect elderly people's conversation data and analyze it to detect cognitive decline early. It can detect cognitive decline if the choice of words changes. Furthermore, AI can analyze the content of elderly people's conversations and detect cognitive decline early. It can detect cognitive decline if the flow of conversation becomes unnatural. This makes it possible to analyze the content of elderly people's conversations and detect cognitive decline early.

[0067] Dementia prevention and support can analyze the behavioral patterns of elderly people, evaluate the effectiveness of cognitive training, and provide the optimal program. For example, AI can analyze the behavioral patterns of elderly people and evaluate the effectiveness of cognitive training. The effectiveness of training can be measured based on daily behavioral data. In addition, behavioral data of elderly people can be collected and analyzed by AI to evaluate the effectiveness of cognitive training. Behavioral patterns before and after training can be compared. Furthermore, AI can analyze the behavioral patterns of elderly people, evaluate the effectiveness of cognitive training, and provide the optimal program. If no effect is observed, the training content can be changed. This makes it possible to analyze the behavioral patterns of elderly people, evaluate the effectiveness of cognitive training, and provide the optimal program.

[0068] Dementia prevention and support can provide cognitive training based on the hobbies and interests of elderly people, allowing them to maintain their cognitive function while having fun. For example, cognitive training based on the hobbies and interests of elderly people can be provided. Training using puzzles or crossword puzzles can be suggested. Furthermore, data on the hobbies of elderly people can be collected, and AI can analyze that data to provide cognitive training based on their hobbies. Training using music or painting can be suggested. Furthermore, cognitive training based on the hobbies and interests of elderly people can be provided, allowing them to maintain their cognitive function while having fun. Training using games related to hobbies can be suggested. In this way, cognitive training based on the hobbies and interests of elderly people can be provided, allowing them to maintain their cognitive function while having fun.

[0069] Dementia prevention and support can suggest online community activities to promote social interaction among the elderly. For example, online community activities can be suggested to promote social interaction among the elderly. Online hobby circles and club activities can be suggested. In addition, data on social interactions among the elderly can be collected and analyzed by AI to suggest online community activities. Online interactions with friends can be suggested. Furthermore, online community activities can be suggested to promote social interaction among the elderly. Online volunteer activities can be suggested. This makes it possible to suggest online community activities to promote social interaction among the elderly.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The monitoring function monitors the elderly's living conditions, for example, heart rate and activity level using IoT devices such as smartwatches, mobile phones, and monitoring batteries. Step 2: Health management support monitors the health data of the elderly, for example, analyzing daily diet, exercise, and sleep data, and providing advice for prevention and early detection. Step 3: Smart homes will support the elderly's living environment by learning their daily rhythms and preferences, and automatically controlling lighting, temperature, and safety. Step 4: Dementia prevention and support involves monitoring the cognitive status and behavioral patterns of elderly people, for example by providing advice on cognitive training and lifestyle support.

[0072] (Example 2) The comprehensive support system according to an embodiment of the present invention is a system that monitors the living conditions and health conditions of elderly people and provides appropriate support, thereby enabling elderly people to live independently, safely, and comfortably.

[0073] A comprehensive support system according to an embodiment includes a monitoring function, health management support, a smart home, and dementia prevention and support. The monitoring function monitors the elderly's living conditions. For example, it monitors heart rate and activity levels using IoT devices such as smartwatches, mobile phones, and monitoring batteries. The health management support monitors the elderly's health data. For example, it analyzes daily diet, exercise, and sleep data and provides advice for prevention and early detection. The smart home supports the elderly's living environment. For example, it learns daily rhythms and preferences and automatically controls lighting, temperature, and safety. The dementia prevention and support monitors the elderly's cognitive status and behavioral patterns. For example, it provides advice on cognitive training and lifestyle support. In this way, the comprehensive support system comprehensively supports the elderly's living conditions, health status, living environment, and cognitive status.

[0074] The monitoring function monitors the elderly person's heart rate and activity level, and can issue notifications and alerts if it detects abnormal values. For example, if the heart rate suddenly rises, the AI ​​will call the elderly person and ask, "Are you OK?" It can also notify family members via LINE that "Mom's heart rate is high," and send an alert to the care center. This allows for the rapid detection of abnormalities in the elderly and prompts appropriate action.

[0075] Health management support can monitor and analyze the daily diet, exercise, and sleep data of elderly people. For example, health management support can analyze diet data and advise, "You're not getting enough vegetables." It can also suggest, based on exercise data, that "You should walk a bit more." It can also analyze sleep data and provide support such as, "You've been sleeping lightly lately, so you should consider visiting a medical institution." This allows for a detailed understanding of the elderly person's daily health status and provides appropriate advice.

[0076] Smart homes can learn the lifestyle rhythms and preferences of elderly people and automatically control lighting, temperature, and safety. For example, a smart home can automatically turn on the lights when they go to the toilet at night. It can also turn on the heating if the room temperature drops. It can also monitor the opening and closing of doors and windows and send out alerts if there is any suspicious activity. This optimizes the living environment for the elderly and provides a comfortable life.

[0077] Dementia prevention and support can monitor the cognitive state and behavioral patterns of elderly people and provide advice on cognitive training and lifestyle support. For example, dementia prevention and support can analyze daily behavioral patterns to detect signs of dementia, such as "You've been asking the same questions repeatedly lately." It can also suggest appropriate training and provide lifestyle support advice, such as "Let's go for a walk today." This helps maintain the cognitive function of elderly people and improve their quality of life.

[0078] The monitoring function analyzes the tone of an elderly person's voice and speaking style, and can detect changes in emotion and alert the user to any abnormalities. For example, the monitoring function uses AI to analyze an elderly person's tone of voice and speaking style to detect changes in emotion. For example, it will detect an abnormality if their voice is trembling more than usual or if they are speaking more slowly. It can also analyze what the elderly person is saying on the phone in real time to detect changes in emotion. For example, it will detect an abnormality if the topic suddenly changes or they become stumped. Furthermore, AI can analyze an elderly person's tone of voice and speaking style to detect changes in emotion and alert the user to any abnormalities. For example, it will detect an abnormality if their voice is lower than usual or if their speaking style suddenly becomes faster. This allows it to quickly detect changes in an elderly person's emotions and prompt appropriate action.

[0079] The monitoring function can analyze an elderly person's walking pattern, predict the risk of falling, and issue a warning in advance. For example, the monitoring function uses AI to analyze an elderly person's walking pattern and predict the risk of falling. For example, it issues a warning if their walking speed suddenly slows or their footing becomes unsteady. The AI ​​also collects walking data from the elderly person and analyzes it to predict the risk of falling. For example, it issues a warning if their stride becomes narrower or they appear to be dragging their feet. Furthermore, the AI ​​analyzes an elderly person's walking pattern, predicts the risk of falling, and issues a warning in advance. For example, it issues a warning if they lose balance while walking. This allows the system to predict the risk of falling in elderly people in advance and encourages appropriate action.

[0080] The monitoring function uses the emotion estimation function to monitor the emotional state of the elderly and can suggest relaxation methods when stress or anxiety increases. The monitoring function, for example, uses the emotion estimation function to monitor the emotional state of the elderly. For example, it suggests relaxation methods when stress or anxiety increases. It also collects emotional data of the elderly and analyzes that data using AI to monitor their emotional state. For example, it suggests deep breathing or meditation when stress increases. Furthermore, it uses the emotion estimation function to monitor the emotional state of the elderly and suggests relaxation methods when stress or anxiety increases. For example, it plays relaxing music. This allows the emotional state of the elderly to be understood and appropriate relaxation methods to be suggested.

[0081] The monitoring function can monitor the health of an elderly person's pet and notify the elderly person if an abnormality is detected in the pet. For example, the monitoring function can monitor the health of an elderly person's pet and notify the elderly person if an abnormality is detected. For example, a notification can be sent if the pet's appetite decreases. The function can also collect pet activity data and analyze it using AI to monitor the health condition. For example, an abnormality can be detected if the pet is moving less than usual. The function can also monitor the health of an elderly person's pet and notify the elderly person if an abnormality is detected in the pet. For example, a notification can be sent if the pet's body temperature is abnormally high. This allows the elderly person to understand the health condition of their pet and prompts them to take appropriate action if an abnormality occurs.

[0082] The monitoring function can analyze the hobbies and activity history of elderly people and suggest events and activities that they might be interested in. For example, the monitoring function can analyze the hobbies and activity history of elderly people and suggest events and activities that they might be interested in. For example, it can suggest new events based on data on events they have attended in the past. It can also collect data on elderly people's hobbies and analyze that data using AI to suggest activities that they might be interested in. For example, it can suggest workshops and courses related to hobbies. It can also analyze the activity history of elderly people and suggest events and activities that they might be interested in. For example, it can suggest new tourist spots based on data on places they have visited in the past. This allows it to suggest events and activities that they might be interested in based on the hobbies and activity history of elderly people.

[0083] The monitoring function uses the emotion estimation function to automatically play music and videos that the elderly person likes, thereby improving their mood. For example, the monitoring function uses the emotion estimation function to automatically play music and videos that the elderly person likes. For example, if the emotion score is low, relaxing music is played. The monitoring function also collects the elderly person's emotion data, and AI analyzes that data to play their favorite music and videos. For example, if they are feeling down, an uplifting video is played. Furthermore, the emotion estimation function is used to automatically play music and videos that the elderly person likes, thereby improving their mood. For example, if the emotion score is high, a favorite movie is played. In this way, favorite music and videos are automatically played to improve the elderly person's mood.

[0084] Health management support can perform image analysis of the elderly's dietary content, evaluate nutritional balance, and provide specific dietary improvement suggestions. Health management support, for example, involves AI analyzing images of the elderly's dietary content to evaluate nutritional balance. For example, a photo of the meal is taken, and AI analyzes the image to point out nutrient deficiencies. Alternatively, the elderly can upload a photo of their meal, and AI analyzes the image to evaluate nutritional balance. For example, if there is a lack of vegetables, specific dietary improvement suggestions are provided. Furthermore, AI performs image analysis of the elderly's dietary content, evaluates nutritional balance, and provides specific dietary improvement suggestions. For example, if there is a lack of protein, appropriate ingredients are suggested. In this way, the elderly's dietary content is analyzed, nutritional balance is evaluated, and specific improvement suggestions are provided.

[0085] Health management support can analyze the exercise data of elderly people and create individually customized exercise programs. For example, health management support uses AI to analyze the exercise data of elderly people and create individually customized exercise programs. For example, it can suggest an appropriate walking plan based on walking data. In addition, the exercise data of elderly people is collected and AI analyzes that data to create customized exercise programs. For example, it can adjust the frequency and intensity of strength training. Furthermore, AI can analyze the exercise data of elderly people and create individually customized exercise programs. For example, it can suggest appropriate aerobic exercise based on heart rate data. In this way, the exercise data of elderly people is analyzed and individually customized exercise programs are provided.

[0086] Health management support can use the emotion estimation function to provide health advice according to the emotional state of the elderly. Health management support, for example, uses the emotion estimation function to provide health advice according to the emotional state of the elderly. For example, if stress is rising, relaxation methods are suggested. In addition, emotional data of the elderly is collected, and AI analyzes the data to provide health advice according to the emotional state. For example, if anxiety is rising, relaxation exercises are suggested. Furthermore, the emotion estimation function is used to provide health advice according to the emotional state of the elderly. For example, if the emotion score is low, relaxation meals are suggested. In this way, health advice according to the emotional state of the elderly is provided.

[0087] Health management support can monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. Health management support can, for example, monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. For example, an alarm can sound when it's time to take the medication. It can also collect medication data from elderly people and use AI to analyze the data to provide reminders to prevent them from forgetting to take their medication. For example, it can send notifications when it's time to take the medication. It can also monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. For example, it can provide voice reminders when it's time to take the medication. This provides reminders to prevent elderly people from forgetting to take their medication.

[0088] Health management support can monitor the amount of fluid intake of elderly people and encourage them to drink appropriately. Health management support can, for example, monitor the amount of fluid intake of elderly people and encourage them to drink appropriately. For example, it can send a notification if fluid intake is insufficient. It can also collect fluid intake data of elderly people and analyze the data with AI to encourage them to drink appropriately. For example, it can provide a reminder if fluid intake is low. It can also monitor the amount of fluid intake of elderly people and encourage them to drink appropriately. For example, it can provide a voice reminder if fluid intake is insufficient. This allows it to monitor the amount of fluid intake of elderly people and encourage them to drink appropriately.

[0089] Health management support can use the emotion estimation function to suggest an environment where the elderly can relax, thereby reducing stress. Health management support, for example, uses the emotion estimation function to suggest an environment where the elderly can relax. For example, if the emotion score is low, relaxing music can be played. In addition, emotional data of the elderly can be collected and AI can analyze the data to suggest an environment where the elderly can relax. For example, if stress is high, relaxing lighting can be set. Furthermore, the emotion estimation function can be used to suggest an environment where the elderly can relax, thereby reducing stress. For example, if the emotion score is low, a relaxing fragrance can be diffused. In this way, an environment where the elderly can relax can be suggested and stress can be reduced.

[0090] A smart home can learn the lifestyle rhythms of the elderly and automatically control home appliances at the optimal times. In a smart home, for example, AI learns the lifestyle rhythms of the elderly and automatically controls home appliances at the optimal times. For example, opening the curtains at the time it's time to wake up in the morning. Data on the elderly's lifestyle is also collected, and AI analyzes that data to automatically control home appliances at the optimal times. For example, turning on the microwave at mealtimes. Furthermore, AI learns the lifestyle rhythms of the elderly and automatically controls home appliances at the optimal times. For example, turning off the lights at bedtime at night. In this way, home appliances are automatically controlled to match the lifestyle rhythms of the elderly.

[0091] A smart home can recognize the voices of the elderly and allow them to operate home appliances with voice commands. For example, a smart home can use AI to recognize the voices of the elderly and allow them to operate home appliances with voice commands. For example, saying "Turn on the TV" will turn on the TV. Data on the elderly's voice can also be collected and analyzed by AI to enable them to operate home appliances with voice commands. For example, saying "Turn on the air conditioner" will turn on the air conditioner. Furthermore, AI can recognize the voices of the elderly and allow them to operate home appliances with voice commands. For example, saying "Turn off the lights" will turn off the lights. This allows the elderly to operate home appliances with voice commands.

[0092] A smart home can use the emotion estimation function to automatically adjust lighting and music according to the emotional state of the elderly. For example, a smart home can use the emotion estimation function to automatically adjust lighting and music according to the emotional state of the elderly. For example, if the emotion score is low, lighting that is relaxing is set. In addition, emotional data of the elderly is collected, and AI analyzes the data to automatically adjust lighting and music according to the emotional state. For example, relaxing music is played when stress is high. Furthermore, the emotion estimation function can be used to automatically adjust lighting and music according to the emotional state of the elderly. For example, brighter lighting is set when the emotion score is high. This allows lighting and music to be automatically adjusted according to the emotional state of the elderly.

[0093] Smart homes can automatically strengthen security systems when elderly people leave the home, ensuring safety when they are away from home. For example, smart homes can automatically strengthen security systems when elderly people leave the home, ensuring safety when they are away from home. For example, they can automatically lock doors. They can also collect data on when elderly people leave the home and use AI to analyze that data to strengthen security systems when they leave the home. For example, they can turn on window sensors. They can also automatically strengthen security systems when elderly people leave the home, ensuring safety when they are away from home. For example, they can automatically activate surveillance cameras. This automatically strengthens security systems when elderly people leave the home, ensuring safety when they are away from home.

[0094] A smart home can monitor visitors to an elderly person, recognize the visitor's face, and determine whether or not the visitor is trustworthy. For example, a smart home can monitor visitors to an elderly person, recognize the visitor's face, and determine whether or not the visitor is trustworthy. For example, the faces of family and friends can be registered. Furthermore, facial data of the visitor is collected and AI analyzes the data to determine whether or not the visitor is trustworthy. For example, a warning can be issued if the face is not registered. Furthermore, a smart home can monitor visitors to an elderly person, recognize the visitor's face, and determine whether or not the visitor is trustworthy. For example, the visitor's face can be analyzed in real time to evaluate their trustworthiness. In this way, visitors to an elderly person can be monitored and their trustworthiness determined.

[0095] A smart home can use emotion estimation functions to automatically diffuse a relaxing aroma for the elderly. For example, a smart home can use emotion estimation functions to automatically diffuse a relaxing aroma for the elderly. For example, if the emotion score is low, a relaxing aroma is diffused. Furthermore, emotional data of the elderly is collected, and AI analyzes the data to automatically diffuse a relaxing aroma. For example, a relaxing aroma is selected when stress levels are high. Furthermore, the emotion estimation function can be used to automatically diffuse a relaxing aroma for the elderly. For example, a refreshing aroma is diffused when the emotion score is high. In this way, a relaxing aroma for the elderly is automatically diffused.

[0096] Dementia prevention and support can analyze the content of conversations between elderly people and detect cognitive decline at an early stage. For example, dementia prevention and support uses AI to analyze the content of conversations between elderly people and detect cognitive decline at an early stage. For example, cognitive decline can be detected if the same questions are asked repeatedly. Conversation data between elderly people is also collected, and AI analyzes that data to detect cognitive decline at an early stage. For example, cognitive decline can be detected if the word choice changes. Furthermore, AI analyzes the content of conversations between elderly people and detects cognitive decline at an early stage. For example, cognitive decline can be detected if the flow of conversation becomes unnatural. In this way, the content of conversations between elderly people can be analyzed and cognitive decline can be detected at an early stage.

[0097] Dementia prevention and support can analyze the behavioral patterns of elderly people, evaluate the effectiveness of cognitive training, and provide the optimal program. For example, in dementia prevention and support, AI analyzes the behavioral patterns of elderly people and evaluates the effectiveness of cognitive training. For example, it measures the effectiveness of training based on daily behavioral data. In addition, behavioral data of elderly people is collected, and AI analyzes that data to evaluate the effectiveness of cognitive training. For example, it compares behavioral patterns before and after training. Furthermore, AI analyzes the behavioral patterns of elderly people, evaluates the effectiveness of cognitive training, and provides the optimal program. For example, it changes the training content if no effect is seen. In this way, the behavioral patterns of elderly people are analyzed, the effectiveness of cognitive training is evaluated, and the optimal program is provided.

[0098] Dementia prevention and support can use the emotion estimation function to suggest cognitive training that suits the emotional state of the elderly. Dementia prevention and support, for example, uses the emotion estimation function to suggest cognitive training that suits the emotional state of the elderly. For example, if the emotion score is low, relaxation training is suggested. In addition, emotional data of the elderly is collected and AI analyzes the data to suggest cognitive training that suits the emotional state. For example, relaxation training is suggested if stress is high. Furthermore, the emotion estimation function is used to suggest cognitive training that suits the emotional state of the elderly. For example, training to improve concentration is suggested if the emotion score is high. In this way, cognitive training that suits the emotional state of the elderly is suggested.

[0099] Dementia prevention and support provides cognitive training based on the hobbies and interests of the elderly, allowing them to maintain their cognitive function while having fun. Dementia prevention and support, for example, provides cognitive training based on the hobbies and interests of the elderly. For example, training using puzzles or crossword puzzles is proposed. In addition, hobby data of the elderly is collected and AI analyzes the data to provide cognitive training based on the hobbies. For example, training using music or painting is proposed. Furthermore, cognitive training based on the hobbies and interests of the elderly is provided, allowing them to maintain their cognitive function while having fun. For example, training using games related to the hobbies is proposed. In this way, cognitive training based on the hobbies and interests of the elderly is provided, allowing them to maintain their cognitive function while having fun.

[0100] Dementia prevention and support can suggest online community activities to promote social interaction among the elderly. Dementia prevention and support, for example, suggests online community activities to promote social interaction among the elderly. For example, online hobby circles or club activities are suggested. Furthermore, data on social interactions among the elderly is collected and AI analyzes the data to suggest online community activities. For example, online interactions with friends are suggested. Furthermore, online community activities are suggested to promote social interaction among the elderly. For example, online volunteer activities are suggested. In this way, online community activities are suggested to promote social interaction among the elderly.

[0101] Dementia prevention and support can use the emotion estimation function to suggest games and puzzles that the elderly can enjoy, thereby helping to maintain cognitive function. Dementia prevention and support, for example, uses the emotion estimation function to suggest games and puzzles that the elderly can enjoy. For example, if the emotion score is low, it suggests games that are relaxing. In addition, it collects emotional data from the elderly and uses AI to analyze that data to suggest games and puzzles that the elderly can enjoy. For example, it suggests puzzles that are relaxing when stress is high. Furthermore, it uses the emotion estimation function to suggest games and puzzles that the elderly can enjoy, helping to maintain cognitive function. For example, if the emotion score is high, it suggests games that improve concentration. In this way, it suggests games and puzzles that the elderly can enjoy, helping to maintain cognitive function.

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

[0103] A comprehensive support system monitors the living conditions and health status of elderly people and provides appropriate support. For example, the monitoring function monitors the living conditions of elderly people. Heart rate and activity levels can be monitored using IoT devices such as smartwatches, mobile phones, and monitoring batteries. Health management support monitors the health data of elderly people. Daily diet, exercise, and sleep data can be analyzed and advice for prevention and early detection can be provided. Smart homes support the living environment of elderly people. They can learn daily rhythms and preferences and automatically control lighting, temperature, and safety. Dementia prevention and support monitors the cognitive status and behavioral patterns of elderly people and provide advice on cognitive training and lifestyle support. In this way, comprehensive support systems provide comprehensive support for the living conditions, health status, living environment, and cognitive status of elderly people.

[0104] The monitoring function monitors the elderly person's heart rate and activity level, and can issue notifications and alerts if it detects abnormal values. For example, if the heart rate suddenly rises, the AI ​​will call the elderly person and ask, "Are you OK?" It can also notify family members via LINE that "Mom's heart rate is high," and send an alert to the care center. This makes it possible to quickly detect abnormalities in the elderly and prompt appropriate action.

[0105] Health management support can monitor and analyze the daily diet, exercise, and sleep data of elderly people. For example, by analyzing dietary data, it can advise, "You're not getting enough vegetables." It can also suggest, based on exercise data, that "You should walk a bit more." It can also analyze sleep data and provide support such as, "You've been sleeping lightly lately, so you should consider visiting a medical institution." This allows for a detailed understanding of the elderly person's daily health condition and the provision of appropriate advice.

[0106] Smart homes can learn the lifestyle rhythms and preferences of elderly people and automatically control lighting, temperature, and safety. For example, the lights can be turned on automatically when an elderly person goes to the toilet at night. The heating can also be turned on if the room temperature drops. In addition, the system can monitor the opening and closing of doors and windows and send alerts if there is any suspicious activity. This can optimize the living environment for the elderly and provide them with a comfortable life.

[0107] Dementia prevention and support can monitor the cognitive status and behavioral patterns of elderly people and provide advice on cognitive training and lifestyle support. For example, by analyzing daily behavioral patterns, it can detect signs of dementia, such as "You've been asking the same questions repeatedly lately." It can also suggest appropriate training. Furthermore, it can provide lifestyle support advice, such as "Let's go for a walk today." This can help maintain the cognitive function of elderly people and improve their quality of life.

[0108] The monitoring function analyzes the tone of an elderly person's voice and speaking style, and can detect changes in emotion and alert the user to abnormalities. For example, AI can analyze an elderly person's tone of voice and speaking style to detect changes in emotion. An abnormality can be detected if their voice is trembling more than usual or if they are speaking more slowly. It can also analyze what an elderly person is saying on the phone in real time to detect changes in emotion. An abnormality can be detected if the topic suddenly changes or they become stumped. Furthermore, AI can analyze an elderly person's tone of voice and speaking style to detect changes in emotion and alert the user to abnormalities. An abnormality can be detected if their voice is lower than usual or if their speaking style suddenly becomes faster. This makes it possible to quickly detect changes in an elderly person's emotion and prompt appropriate action.

[0109] The monitoring function can analyze an elderly person's walking pattern, predict the risk of falling, and issue a warning in advance. For example, AI can analyze an elderly person's walking pattern and predict the risk of falling. A warning can be issued if their walking speed suddenly slows or their footing becomes unsteady. In addition, AI can collect an elderly person's walking data and analyze it to predict the risk of falling. A warning can be issued if the stride becomes narrower or if a shuffling movement is observed. Furthermore, AI can analyze an elderly person's walking pattern and predict the risk of falling and issue a warning in advance. A warning can be issued if a movement that causes loss of balance is observed while walking. This makes it possible to predict the risk of falling in elderly people in advance and encourage appropriate measures.

[0110] The monitoring function uses the emotion estimation function to monitor the emotional state of the elderly and can suggest relaxation methods when stress or anxiety increases. For example, the emotion estimation function can be used to monitor the emotional state of the elderly. Relaxation methods can be suggested when stress or anxiety increases. In addition, the emotional data of the elderly can be collected and analyzed by AI to monitor the emotional state. Deep breathing or meditation can be suggested when stress increases. Furthermore, the emotion estimation function can be used to monitor the emotional state of the elderly and suggest relaxation methods when stress or anxiety increases. Relaxing music can be played. This makes it possible to understand the emotional state of the elderly and suggest appropriate relaxation methods.

[0111] The monitoring function can monitor the health of elderly people's pets and notify the elderly if an abnormality is detected. For example, it can monitor the health of elderly people's pets and notify the elderly if an abnormality is detected. It can notify the elderly if the pet's appetite decreases. It can also collect pet activity data and analyze it using AI to monitor its health. It can detect an abnormality if the pet is less active than usual. It can also monitor the health of elderly people's pets and notify the elderly if an abnormality is detected. It can notify the elderly if the pet's body temperature is abnormally high. This allows the elderly to understand the health of their pets and prompt appropriate action in the event of an abnormality.

[0112] The monitoring function can analyze the hobbies and activity history of elderly people and suggest events and activities that they may be interested in. For example, it can analyze the hobbies and activity history of elderly people and suggest events and activities that they may be interested in. It can suggest new events based on data from events they have previously attended. It can also collect data on elderly people's hobbies and use AI to analyze that data to suggest activities that they may be interested in. It can suggest workshops and courses related to hobbies. It can also analyze the activity history of elderly people and suggest events and activities that they may be interested in. It can suggest new tourist spots based on data from places they have visited in the past. This makes it possible to suggest events and activities that they may be interested in based on the hobbies and activity history of elderly people.

[0113] The monitoring function uses the emotion estimation function to automatically play music or videos that the elderly person likes to improve their mood. For example, the emotion estimation function can be used to automatically play music or videos that the elderly person likes. If the emotion score is low, relaxing music can be played. In addition, the emotional data of the elderly person can be collected and AI can analyze the data to play their favorite music or videos. If they are feeling down, uplifting videos can be played. Furthermore, the emotion estimation function can be used to automatically play music or videos that the elderly person likes to improve their mood. If the emotion score is high, a favorite movie can be played. This makes it possible to automatically play favorite music or videos to improve the elderly person's mood.

[0114] Health management support can analyze images of an elderly person's diet, evaluate their nutritional balance, and provide specific suggestions for dietary improvement. For example, AI can analyze images of an elderly person's diet and evaluate their nutritional balance. A photo of the meal can be taken, and AI can analyze the image to identify nutrient deficiencies. Alternatively, an elderly person can upload a photo of their meal, and AI can analyze the image to evaluate the nutritional balance. If there is a lack of vegetables, specific suggestions for dietary improvement can be provided. Furthermore, AI can analyze images of an elderly person's diet, evaluate their nutritional balance, and provide specific suggestions for dietary improvement. If there is a lack of protein, appropriate ingredients can be suggested. This makes it possible to analyze an elderly person's diet, evaluate their nutritional balance, and provide specific suggestions for improvement.

[0115] Health management support can analyze the exercise data of elderly people and create individually customized exercise programs. For example, AI can analyze the exercise data of elderly people and create individually customized exercise programs. An appropriate walking plan can be suggested based on walking data. In addition, the exercise data of elderly people can be collected and AI can analyze the data to create customized exercise programs. The frequency and intensity of strength training can be adjusted. Furthermore, AI can analyze the exercise data of elderly people and create individually customized exercise programs. Appropriate aerobic exercise can be suggested based on heart rate data. In this way, it is possible to analyze the exercise data of elderly people and provide individually customized exercise programs.

[0116] Health management support can use the emotion estimation function to provide health advice according to the emotional state of the elderly. For example, the emotion estimation function can be used to provide health advice according to the emotional state of the elderly. Relaxation methods can be suggested when stress levels are high. In addition, emotional data of the elderly can be collected and analyzed by AI to provide health advice according to their emotional state. Relaxing exercises can be suggested when anxiety levels are high. Furthermore, the emotion estimation function can be used to provide health advice according to the emotional state of the elderly. Relaxing meals can be suggested when the emotion score is low. This makes it possible to provide health advice according to the emotional state of the elderly.

[0117] Health management support can monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. For example, it can monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. It can sound an alarm when it's time to take the medication. It can also collect medication data from elderly people and use AI to analyze the data to provide reminders to prevent them from forgetting to take their medication. It can send notifications when it's time to take the medication. It can also monitor the medication status of elderly people and provide reminders to prevent them from forgetting to take their medication. It can provide voice reminders when it's time to take the medication. This makes it possible to provide reminders to prevent elderly people from forgetting to take their medication.

[0118] Health management support can monitor the elderly's water intake and encourage them to drink appropriately. For example, it can monitor the elderly's water intake and encourage them to drink appropriately. It can send notifications if water intake is insufficient. It can also collect the elderly's water intake data and use AI to analyze the data to encourage appropriate hydration. It can provide reminders if water intake is low. It can also monitor the elderly's water intake and encourage them to drink appropriately. It can provide voice reminders if water intake is insufficient. This makes it possible to monitor the elderly's water intake and encourage them to drink appropriately.

[0119] Health management support can use the emotion estimation function to suggest an environment where the elderly can relax and reduce stress. For example, the emotion estimation function can be used to suggest an environment where the elderly can relax. If the emotion score is low, relaxing music can be played. In addition, emotional data of the elderly can be collected and AI can analyze the data to suggest an environment where the elderly can relax. If stress is high, relaxing lighting can be set. Furthermore, the emotion estimation function can be used to suggest an environment where the elderly can relax and reduce stress. If the emotion score is low, a relaxing scent can be diffused. This makes it possible to suggest an environment where the elderly can relax and reduce stress.

[0120] Smart homes can learn the lifestyle rhythms of the elderly and automatically control home appliances at the optimal times. For example, AI can learn the lifestyle rhythms of the elderly and automatically control home appliances at the optimal times. Curtains can be opened when it's time to wake up in the morning. In addition, AI can collect lifestyle data of the elderly and analyze it to automatically control home appliances at the optimal times. The microwave can be turned on when it's time to eat. Furthermore, AI can learn the lifestyle rhythms of the elderly and automatically control home appliances at the optimal times. Lights can be turned off when it's time to go to bed at night. In this way, home appliances can be automatically controlled to suit the lifestyle rhythms of the elderly.

[0121] A smart home can recognize the voices of the elderly and allow them to control home appliances with voice commands. For example, AI can recognize the voices of the elderly and allow them to control home appliances with voice commands. When they say, "Turn on the TV," the TV will turn on. Also, AI can collect voice data from the elderly and analyze it to allow them to control home appliances with voice commands. When they say, "Turn on the air conditioner," the air conditioner will turn on. Furthermore, AI can recognize the voices of the elderly and allow them to control home appliances with voice commands. When they say, "Turn off the lights," the lights will turn off. This allows the elderly to control home appliances with voice commands.

[0122] A smart home can use emotion estimation to automatically adjust lighting and music according to the emotional state of the elderly. For example, the emotion estimation function can be used to automatically adjust lighting and music according to the emotional state of the elderly. Relaxing lighting can be set when the emotion score is low. Furthermore, the emotional data of the elderly can be collected, and AI can analyze the data to automatically adjust lighting and music according to the emotional state. Relaxing music can be played when stress levels are high. Furthermore, the emotion estimation function can be used to automatically adjust lighting and music according to the emotional state of the elderly. Brighter lighting can be set when the emotion score is high. This makes it possible to automatically adjust lighting and music according to the emotional state of the elderly.

[0123] Smart homes can automatically strengthen security systems when elderly people leave the home, ensuring safety when the home is unattended. For example, security systems can be automatically strengthened when elderly people leave the home, ensuring safety when the home is unattended. Door locks can be automatically locked. Data on elderly people's departure times can be collected and analyzed by AI to strengthen the security system when the home is unattended. Window sensors can be turned on. Furthermore, security systems can be automatically strengthened when elderly people leave the home, ensuring safety when the home is unattended. Surveillance cameras can be automatically activated. This allows security systems to be automatically strengthened when elderly people leave the home, ensuring safety when the home is unattended.

[0124] Smart homes can monitor visitors to elderly people, recognize their faces, and determine whether they are trustworthy. For example, they can monitor visitors to elderly people, recognize their faces, and determine whether they are trustworthy. The faces of family and friends can be registered. In addition, the visitor's facial data can be collected and AI can analyze the data to determine whether they are trustworthy. A warning can be issued if the face is not registered. Furthermore, they can monitor visitors to elderly people, recognize their faces, and determine whether they are trustworthy. The visitor's face can be analyzed in real time and their trustworthiness evaluated. This makes it possible to monitor visitors to elderly people and determine whether they are trustworthy.

[0125] Smart homes can use emotion estimation functions to automatically diffuse relaxing aromas for the elderly. For example, the emotion estimation function can be used to automatically diffuse relaxing aromas for the elderly. If the emotion score is low, a relaxing aroma can be diffused. Furthermore, the emotion data of the elderly can be collected and analyzed by AI to automatically diffuse relaxing aromas. A relaxing aroma can be selected when stress levels are high. Furthermore, the emotion estimation function can be used to automatically diffuse relaxing aromas for the elderly. If the emotion score is high, a refreshing aroma can be diffused. This makes it possible to automatically diffuse relaxing aromas for the elderly.

[0126] Dementia prevention and support can analyze the content of elderly people's conversations and detect cognitive decline early. For example, AI can analyze the content of elderly people's conversations and detect cognitive decline early. It can detect cognitive decline if the same questions are repeated. It can also collect elderly people's conversation data and analyze it to detect cognitive decline early. It can detect cognitive decline if the choice of words changes. Furthermore, AI can analyze the content of elderly people's conversations and detect cognitive decline early. It can detect cognitive decline if the flow of conversation becomes unnatural. This makes it possible to analyze the content of elderly people's conversations and detect cognitive decline early.

[0127] Dementia prevention and support can analyze the behavioral patterns of elderly people, evaluate the effectiveness of cognitive training, and provide the optimal program. For example, AI can analyze the behavioral patterns of elderly people and evaluate the effectiveness of cognitive training. The effectiveness of training can be measured based on daily behavioral data. In addition, behavioral data of elderly people can be collected and analyzed by AI to evaluate the effectiveness of cognitive training. Behavioral patterns before and after training can be compared. Furthermore, AI can analyze the behavioral patterns of elderly people, evaluate the effectiveness of cognitive training, and provide the optimal program. If no effect is observed, the training content can be changed. This makes it possible to analyze the behavioral patterns of elderly people, evaluate the effectiveness of cognitive training, and provide the optimal program.

[0128] Dementia prevention and support can use the emotion estimation function to suggest cognitive training that matches the emotional state of the elderly. For example, the emotion estimation function can be used to suggest cognitive training that matches the emotional state of the elderly. If the emotional score is low, relaxation training can be suggested. In addition, emotional data of the elderly can be collected, and AI can analyze the data to suggest cognitive training that matches the emotional state. If stress is rising, relaxation training can be suggested. Furthermore, the emotion estimation function can be used to suggest cognitive training that matches the emotional state of the elderly. If the emotional score is high, training to improve concentration can be suggested. This makes it possible to suggest cognitive training that matches the emotional state of the elderly.

[0129] Dementia prevention and support can provide cognitive training based on the hobbies and interests of elderly people, allowing them to maintain their cognitive function while having fun. For example, cognitive training based on the hobbies and interests of elderly people can be provided. Training using puzzles or crossword puzzles can be suggested. Furthermore, data on the hobbies of elderly people can be collected, and AI can analyze that data to provide cognitive training based on their hobbies. Training using music or painting can be suggested. Furthermore, cognitive training based on the hobbies and interests of elderly people can be provided, allowing them to maintain their cognitive function while having fun. Training using games related to hobbies can be suggested. In this way, cognitive training based on the hobbies and interests of elderly people can be provided, allowing them to maintain their cognitive function while having fun.

[0130] Dementia prevention and support can suggest online community activities to promote social interaction among the elderly. For example, online community activities can be suggested to promote social interaction among the elderly. Online hobby circles and club activities can be suggested. In addition, data on social interactions among the elderly can be collected and analyzed by AI to suggest online community activities. Online interactions with friends can be suggested. Furthermore, online community activities can be suggested to promote social interaction among the elderly. Online volunteer activities can be suggested. This makes it possible to suggest online community activities to promote social interaction among the elderly.

[0131] Dementia prevention and support can use the emotion estimation function to suggest games and puzzles that the elderly can enjoy, thereby helping to maintain cognitive function. For example, the emotion estimation function can be used to suggest games and puzzles that the elderly can enjoy. If the emotion score is low, it can suggest games that are relaxing. In addition, it is possible to collect the elderly's emotion data and use AI to analyze that data to suggest games and puzzles that they can enjoy. If stress levels are high, it can suggest puzzles that are relaxing. Furthermore, the emotion estimation function can be used to suggest games and puzzles that the elderly can enjoy, helping to maintain cognitive function. If the emotion score is high, it can suggest games that improve concentration. This makes it possible to suggest games and puzzles that the elderly can enjoy, helping to maintain cognitive function.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The monitoring function monitors the elderly's living conditions, for example, heart rate and activity level using IoT devices such as smartwatches, mobile phones, and monitoring batteries. Step 2: Health management support monitors the health data of the elderly, for example, analyzing daily diet, exercise, and sleep data, and providing advice for prevention and early detection. Step 3: Smart homes will support the elderly's living environment by learning their daily rhythms and preferences, and automatically controlling lighting, temperature, and safety. Step 4: Dementia prevention and support involves monitoring the cognitive status and behavioral patterns of elderly people, for example by providing advice on cognitive training and lifestyle support.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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]

[0201] 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 monitoring function that monitors the living conditions of the elderly, Health management support for monitoring health data of the elderly person; a smart home that supports the living environment of the elderly; and dementia prevention and support that monitors the cognitive state and behavioral patterns of the elderly. A system characterized by:

2. The monitoring function is The system monitors the elderly person's heart rate and activity level, and issues notifications and alerts if abnormal values ​​are detected.

2. The system of claim 1.

3. The health management support includes: The elderly person's daily diet, exercise, and sleep data are monitored and analyzed.

2. The system of claim 1.

4. The smart home It learns the lifestyle and preferences of the elderly and automatically controls lighting, temperature, and safety.

2. The system of claim 1.

5. The dementia prevention and support mentioned above is as follows: Monitoring the cognitive state and behavioral patterns of the elderly person and providing advice on cognitive training and lifestyle support 2. The system of claim 1.

6. The monitoring function is Analyzing the tone of voice and speaking style of the elderly person, detecting changes in emotions, and informing them of abnormalities.

2. The system of claim 1.

7. The monitoring function is Analyze the elderly person's walking patterns, predict the risk of falling, and issue a warning.

2. The system of claim 1.

8. The monitoring function is Monitor the emotional state of the elderly person and suggest relaxation methods when stress or anxiety increases 2. The system of claim 1.

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