System

Abstract

An object of a system according to an embodiment is to comprehensively support the life of an elderly person and cooperate with a child or a primary care doctor.SOLUTION: In general, according to one embodiment, a system includes a necklace-type input device, a smartphone, a generated AI, a voice recognizer, and a voice recognizer. The necklace type device is worn by a wearer. The smartphone cooperates with the necklace-type device. The generation AI provides various support functions. The voice recognition unit interacts with the elderly person using a voice recognition technique. The data linkage unit links the data of the support content with the child or the family doctor.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] Previous technology had the problem of not providing sufficient systems that comprehensively support the lives of the elderly and connect them with their children and their family doctors.

[0005] The system according to the embodiment aims to provide comprehensive support for the lives of elderly people and to cooperate with their children and family doctors. [Means for solving the problem]

[0006] The system according to the embodiment comprises a necklace-type device, a smartphone, a generation AI, a voice recognition unit, and a data linkage unit. The necklace-type device is worn by the wearer. The smartphone links with the necklace-type device. The generation AI provides various support functions. The voice recognition unit uses voice recognition technology to converse with the elderly person. The data linkage unit links data on the support content with the child or their family doctor. [Effects of the Invention]

[0007] The system according to the embodiment provides comprehensive support for the lives of elderly people and can collaborate with their children and primary care doctors. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The elderly life support system according to an embodiment of the present invention is a system that provides multifaceted support for the lives of elderly people through a necklace-type device and smartphone that utilizes generative AI. This system supports various aspects of life, including alleviating loneliness, intellectual conversation, dietary management, medication management, health management, exercise promotion, sleep management, schedule management, and stress relief. As a result, the elderly life support system can reduce the risk of mental illnesses such as dementia and maintain physical and mental health.

[0029] An elderly life support system according to an embodiment includes a necklace-type device, a smartphone, a generation AI, a voice recognition unit that uses voice recognition technology to communicate with the elderly, and a data linkage unit that links support content data with children or a primary care doctor. The necklace-type device supports the elderly's daily life. For example, the necklace-type device is lightweight and easy to wear, and has communication functions such as Bluetooth and Wi-Fi. The necklace-type device also has built-in sensors that measure biometric data such as heart rate and body temperature. The smartphone links with the necklace-type device and processes the generation AI. For example, the smartphone receives data from the necklace-type device through a dedicated application and sends it to the generation AI. The generation AI supports the elderly's daily life using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI uses voice recognition technology to communicate with the elderly and reduce feelings of loneliness. The generation AI also provides functions such as dietary management and medication management. The voice recognition unit communicates with the elderly using voice recognition technology. For example, the voice recognition unit analyzes the elderly's speech in real time and sends the data to the generation AI. The voice recognition unit can also analyze the elderly person's tone of voice and speaking style to estimate their emotional state. The data linking unit links the support content data with children and their family doctor. For example, the data linking unit can share data via a cloud server, allowing family members and doctors to understand the elderly person's condition. This allows the elderly life support system to support the elderly in many ways and maintain their mental and physical health. For example, the system can reduce feelings of loneliness and promote intellectual activity. The system can also manage diet and medication to maintain health. The system also collects health data and notifies users if there are any abnormalities.

[0030] The speech recognition unit can learn the elderly person's past conversation history and generate questions based on their hobbies and interests. For example, the speech recognition unit uses a generation AI to analyze the elderly person's past conversation history and generate questions based on their hobbies and interests. For example, if an elderly person has previously mentioned that they like music, the unit can ask them a question such as, "Tell me about the music you've listened to recently." The speech recognition unit can also save past conversation history and encrypt it to protect privacy. For example, when saving voice data, the data is protected using AES encryption. This allows the system to generate dialogue based on the elderly person's hobbies and interests, reducing their sense of loneliness.

[0031] The data linking unit can refer to past conversations between the elderly person and their family and friends and provide common topics of conversation. For example, the generation AI can refer to past conversations between the elderly person and their family and friends and provide common topics of conversation. For example, it can ask a question such as "Tell me about the travel destinations you went on with your family" in reference to a trip that was discussed in a conversation with family. The data linking unit can also clarify the storage format of the voice data and the method of privacy protection. For example, it can encrypt and store the voice data to protect privacy. This can help reduce feelings of loneliness by providing common topics of conversation between the elderly person and their family and friends.

[0032] The data linking unit can suggest topics such as music and movies that the elderly person likes, to liven up the conversation. For example, the data linking unit uses a generation AI to suggest topics such as music and movies that the elderly person likes, to liven up the conversation. For example, it asks questions such as, "Tell me about the last movie you saw." The data linking unit can also analyze questionnaire surveys and viewing history to identify the preferences of the elderly person. For example, it can identify the favorite music genre based on the survey results. This makes it possible to liven up the conversation by suggesting topics such as music and movies that the elderly person likes.

[0033] Generative AI can learn the dietary history of elderly people and generate individualized meal plans that take nutritional balance into consideration. For example, generative AI can learn the dietary history of elderly people and generate individualized meal plans that take nutritional balance into consideration. For example, based on past meal content, it can make suggestions such as, "You didn't eat many vegetables today, so eat more tomorrow." Generative AI can also clarify how meal content is recorded and how data is saved. For example, it can record meal content with photos and save them in a database. This allows elderly people to maintain their health by providing individualized meal plans that take nutritional balance into consideration.

[0034] Generative AI can analyze photos of meal contents and automatically assess whether nutrients are in excess or insufficient. For example, generative AI can analyze photos of meal contents and automatically assess whether nutrients are in excess or insufficient. For example, it can analyze the amount of vegetables in the photo and make suggestions such as, "There are not many vegetables today, so eat more tomorrow." Generative AI can also analyze meal contents using image recognition technology and nutrient estimation methods. For example, it can use image recognition technology to identify ingredients and estimate the nutrient content. This allows users to maintain their health by analyzing photos of meal contents and automatically assessing whether nutrients are in excess or insufficient.

[0035] Generative AI can suggest recipes that take into account ingredients from the elderly's local area. For example, it can suggest recipes that use vegetables that are in season in the area. Generative AI can also clarify a list of local specialties and how to obtain ingredients. For example, it can provide information on local farm shops. This allows elderly people to maintain their health by suggesting recipes that take into account ingredients from their local area.

[0036] Generative AI can learn an elderly person's medication history, analyze the effects and side effects of medication, and propose an optimal medication schedule. For example, generative AI can learn an elderly person's medication history, analyze the effects and side effects of medication, and propose an optimal medication schedule. For example, it can make suggestions such as, "Taking the medicine at this time will increase its effectiveness" based on past medication data. Generative AI can also clarify the method of medication record keeping and the format in which data is stored. For example, medication records can be saved digitally and accumulated in a database. This allows it to learn an elderly person's medication history, analyze the effects and side effects of medication, and propose an optimal medication schedule, thereby helping to maintain health.

[0037] The generation AI can send voice reminders when it's time to take medicine and confirm that it has been taken. For example, the generation AI can send a voice reminder when it's time to take medicine and confirm that it has been taken. For example, it can send a notification such as, "Now is the time to take your blood pressure medication." The generation AI can also set the content of the voice message and the timing of the reminder. For example, it can send a reminder just before it's time to take the medicine and a confirmation message after it has been taken. This makes it possible to prevent forgetting to take medicine by receiving a voice reminder when it's time to take the medicine and confirming that it has been taken.

[0038] Generative AI can share the medication status of elderly people with family and doctors in real time. For example, generative AI can share the medication status of elderly people with family and doctors in real time. For example, it can record the time and dosage of medication and notify family and doctors. Generative AI can also clarify the method of medication record and the format in which data is stored. For example, medication records can be saved digitally and accumulated in a database. This allows for proper medication management by sharing medication status with family and doctors in real time.

[0039] Generative AI can add a function to instantly answer questions about taking medication. For example, it can answer questions such as, "Should I take this medicine before meals?" Generative AI can also answer questions using natural language processing technology and answer databases. For example, it can store medication information in a database and provide appropriate answers to questions. This allows for appropriate medication management by instantly answering questions about taking medication.

[0040] Generative AI can analyze the health data of elderly people over the long term and develop algorithms to predict health risks. For example, generative AI can analyze the health data of elderly people over the long term and develop algorithms to predict health risks. For example, it can make predictions such as "Your risk of heart disease is increasing" based on heart rate and blood pressure data. Generative AI can also clarify how vital signs are recorded and how data is stored. For example, heart rate and blood pressure data can be saved digitally and accumulated in a database. This allows for long-term analysis of health data and prediction of health risks, helping to maintain health.

[0041] Generative AI can provide personalized health advice based on physical condition data. For example, Generative AI can provide personalized health advice based on the physical condition data of an elderly person. For example, it can give advice such as "Take a short rest and relax today" based on heart rate and blood pressure data. Generative AI can also clarify how to analyze physical condition data and the content of advice. For example, it can analyze vital sign data and provide appropriate health advice. This allows for the provision of personalized health advice based on physical condition data, helping to maintain health.

[0042] Generative AI can share the health data of elderly people with family and doctors in real time. Generative AI can, for example, record heart rate and blood pressure data and notify family and doctors. Generative AI can also clarify how vital signs are recorded and the format in which the data is stored. For example, heart rate and blood pressure data can be saved digitally and accumulated in a database. This allows for proper health management by sharing health data with family and doctors in real time.

[0043] Generative AI can add a function to instantly answer questions about health based on physical condition data. Generative AI can add a function to instantly answer questions about health based on physical condition data. For example, it can answer questions such as, "What should I do if my heart rate is high?" Generative AI can also answer questions using natural language processing technology and an answer database. For example, it can store health information in a database and provide appropriate answers to questions. This allows for appropriate health management by instantly answering health questions based on physical condition data.

[0044] Generative AI can add a function that analyzes posture and movements during exercise and provides guidance on correct form. For example, generative AI can add a function that analyzes posture and movements during exercise and provides guidance on correct form. For example, it can provide guidance such as, "Exercise with your back straight." Generative AI can also analyze posture and movement using motion analysis technology and posture evaluation criteria. For example, it can use motion analysis technology to evaluate posture during exercise and provide guidance on correct form. In this way, health can be maintained by analyzing posture and movements during exercise and providing guidance on correct form.

[0045] Generative AI can share an elderly person's exercise history with family and doctors in real time. For example, generative AI can share an elderly person's exercise history with family and doctors in real time. For example, it can record the type and time of exercise and notify family and doctors. Generative AI can also clarify how exercise content is recorded and the format in which data is saved. For example, it can record exercise content in digital format and save it in a database. This allows exercise history to be shared with family and doctors in real time, enabling appropriate exercise management.

[0046] Generative AI can add a function to instantly answer questions about exercise. For example, generative AI can answer questions such as, "How long should I do this exercise?" Generative AI can also answer questions using natural language processing technology and an answer database. For example, it can store information about exercise in a database and provide appropriate answers to questions. This allows for appropriate exercise management by instantly answering questions about exercise.

[0047] Generative AI can analyze the sleep data of elderly people over the long term and provide advice to improve sleep quality. For example, Generative AI can provide advice such as "Go to bed earlier tonight" based on past sleep data. Generative AI can also specify sleep tracking technology and data storage format. For example, sleep data can be recorded digitally and stored in a database. This allows for long-term analysis of sleep data and provides advice to improve sleep quality, helping to maintain health.

[0048] Generative AI can add a function to analyze environmental sounds and body movements during sleep and evaluate sleep quality. For example, if the environmental sounds are noisy, it can give advice such as "Sleep in a quiet environment." Generative AI can also analyze environmental sounds and body movements using voice recognition technology and motion analysis technology. For example, it can identify environmental sounds using voice recognition technology and evaluate body movements using motion analysis technology. This allows you to maintain your health by analyzing environmental sounds and body movements during sleep and evaluating sleep quality.

[0049] Generative AI can share an elderly person's sleep data with family and doctors in real time. For example, Generative AI can record sleep duration and quality and notify family and doctors. Generative AI can also specify sleep tracking technology and data storage format. For example, sleep data can be recorded digitally and stored in a database. This allows sleep data to be shared with family and doctors in real time, enabling appropriate health management.

[0050] Generative AI can add a function to instantly answer questions about sleep. For example, generative AI can answer questions such as, "What should I do to get a good night's sleep?" Generative AI can also answer questions using natural language processing technology and an answer database. For example, it can store information about sleep in a database and provide appropriate answers to questions. This allows for appropriate health management by instantly answering questions about sleep.

[0051] Generative AI can learn from the elderly person's past schedule history and provide individualized reminders. For example, it can remind them, "You have a doctor's appointment tomorrow," based on past schedule data. Generative AI can also clarify the use of calendar apps and the format in which data is saved. For example, it can record schedule data digitally and save it in a database. This allows it to learn from the elderly person's past schedule history and provide individualized reminders, enabling appropriate schedule management.

[0052] The generation AI can add a function to adjust the frequency and timing of reminders depending on the importance of the schedule. For example, the generation AI can add a function to adjust the frequency and timing of reminders depending on the importance of the schedule. For example, it can set more frequent reminders for important schedules. The generation AI can also clarify the type of schedule and how to set priority. For example, it can evaluate the importance of the schedule and set the frequency and timing of reminders. This allows for appropriate schedule management by adjusting the frequency and timing of reminders depending on the importance of the schedule.

[0053] Generative AI can share an elderly person's schedule with family and doctors in real time. For example, generative AI can record hospital appointments and important events and notify family and doctors. Generative AI can also clarify the use of calendar apps and the format in which data is saved. For example, it can record schedule data digitally and save it in a database. This allows for proper schedule management by sharing schedules with family and doctors in real time.

[0054] Generative AI can add a function to instantly answer questions about schedules. For example, generative AI can answer questions such as, "What are your plans for tomorrow?" Generative AI can also answer questions using natural language processing technology and an answer database. For example, it can store information about schedules in a database and provide appropriate answers to questions. This allows for proper schedule management by instantly answering questions about schedules.

[0055] Generative AI can analyze the stress levels of elderly people over the long term and provide personalized advice for stress reduction. For example, generative AI can provide advice such as "Try engaging in a relaxing activity today" based on past stress data. Generative AI can also clarify the type of stress indicator and evaluation method. For example, it can set an indicator for evaluating stress levels and analyze the data. This allows for long-term analysis of stress levels and provides personalized advice for stress reduction, helping to maintain health.

[0056] Generative AI can suggest relaxation music and guided meditations to relieve stress. For example, generative AI can suggest relaxation music to help the elderly relieve stress. For example, it might suggest, "Listen to this relaxation music to relax today." Generative AI can also clarify the specific content of the relaxation music and guided meditations and how they will be provided. For example, it can set the type of music and the content of the meditation guide, and provide them at the appropriate time. This allows health to be maintained by suggesting relaxation music and guided meditations to relieve stress.

[0057] Generative AI can share the stress levels of elderly people with family and doctors in real time. For example, generative AI can record changes in stress levels and notify family and doctors. Generative AI can also clarify the types of stress indicators and evaluation methods. For example, it can set indicators for evaluating stress levels and analyze the data. By sharing stress levels with family and doctors in real time, appropriate health management can be carried out.

[0058] Generative AI can add a function to instantly answer questions about stress. For example, generative AI can answer questions such as, "What can I do to reduce stress?" Generative AI can also answer questions using natural language processing technology and an answer database. For example, it can store information about stress in a database and provide appropriate answers to questions. This allows for immediate answers to questions about stress, enabling appropriate health management.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The elderly life support system can further include an exercise recording unit. The exercise recording unit records the amount and type of daily exercise of the elderly person and sends the data to the generation AI. For example, it uses a pedometer or accelerometer to measure walking distance and calories burned and accumulates the data. The exercise recording unit can also automatically classify the type and intensity of exercise and suggest an appropriate exercise plan. For example, if light exercise is needed, it may suggest, "Let's take a walk today." This can support the elderly person's exercise habits and help them maintain their health.

[0061] The elderly life support system can further include an emergency notification unit. The emergency notification unit monitors the elderly person's physical condition data and location information in real time, and notifies family members or doctors if an abnormality is detected. For example, if a sudden increase in heart rate or a fall is detected, a notification is sent immediately. The emergency notification unit can also set emergency response methods in advance and prompt appropriate responses. For example, it can send a message such as "A fall has been detected. Please check immediately." This ensures the safety of the elderly person and enables prompt response.

[0062] The elderly life support system can further include a hobby suggestion unit. The hobby suggestion unit learns the elderly person's past activity history and interests and suggests new hobbies and activities. For example, for an elderly person who used to like gardening, the unit may suggest, "Why not try growing a new plant?" The hobby suggestion unit can also provide information on local events and club activities. For example, it may provide information such as, "There's a gardening club event nearby." This can provide new enjoyment in the lives of the elderly and reduce feelings of loneliness.

[0063] The elderly life support system can further be equipped with a relaxation section. The relaxation section monitors the stress level of the elderly and provides relaxation music and meditation guides. For example, if stress is high, it may suggest, "Try listening to some relaxing music." The relaxation section can also provide guidance on breathing techniques and simple stretches. For example, it may suggest, "Take a deep breath and relax." This helps reduce stress in the elderly and maintain their physical and mental health.

[0064] The elderly life support system may further include a communication promotion unit. The communication promotion unit provides functions to promote communication between the elderly and their family and friends. For example, it can set a reminder to periodically send a message to family members. The communication promotion unit can also provide an interface that makes it easy to make video calls and send and receive messages. For example, it may suggest, "Try making a video call with your family today." This helps the elderly maintain social connections and reduce feelings of loneliness.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The necklace-type device supports the lifestyles of the elderly. For example, the necklace-type device is lightweight and easy to wear, and is equipped with communication functions such as Bluetooth and Wi-Fi. The necklace-type device also has built-in sensors that measure biometric data such as heart rate and body temperature. Step 2: The smartphone works in conjunction with the necklace-type device to process the generation AI. For example, the smartphone receives data from the necklace-type device through a dedicated application and sends it to the generation AI. Step 3: Generative AI uses text generation AI (e.g., LLM) and multimodal generation AI to support the lives of seniors. For example, generative AI can use voice recognition technology to converse with seniors and reduce their sense of loneliness. Generative AI can also provide functions such as dietary management and medication management. Step 4: The voice recognition unit uses voice recognition technology to converse with the elderly. For example, the voice recognition unit analyzes the elderly's speech in real time and sends it to the generation AI. The voice recognition unit can also analyze the elderly's tone of voice and speaking style to estimate their emotional state. Step 5: The data linking unit links the support data with the child and their family doctor. For example, the data linking unit can share data via a cloud server, allowing family members and doctors to understand the elderly person's condition.

[0067] (Example 2) The elderly life support system according to an embodiment of the present invention is a system that provides multifaceted support for the lives of elderly people through a necklace-type device and smartphone that utilizes generative AI. This system supports various aspects of life, including alleviating loneliness, intellectual conversation, dietary management, medication management, health management, exercise promotion, sleep management, schedule management, and stress relief. As a result, the elderly life support system can reduce the risk of mental illnesses such as dementia and maintain physical and mental health.

[0068] An elderly life support system according to an embodiment includes a necklace-type device, a smartphone, a generation AI, a voice recognition unit that uses voice recognition technology to communicate with the elderly, and a data linkage unit that links support content data with children or a primary care doctor. The necklace-type device supports the elderly's daily life. For example, the necklace-type device is lightweight and easy to wear, and has communication functions such as Bluetooth and Wi-Fi. The necklace-type device also has built-in sensors that measure biometric data such as heart rate and body temperature. The smartphone links with the necklace-type device and processes the generation AI. For example, the smartphone receives data from the necklace-type device through a dedicated application and sends it to the generation AI. The generation AI supports the elderly's daily life using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI uses voice recognition technology to communicate with the elderly and reduce feelings of loneliness. The generation AI also provides functions such as dietary management and medication management. The voice recognition unit communicates with the elderly using voice recognition technology. For example, the voice recognition unit analyzes the elderly's speech in real time and sends the data to the generation AI. The voice recognition unit can also analyze the elderly person's tone of voice and speaking style to estimate their emotional state. The data linking unit links the support content data with children and their family doctor. For example, the data linking unit can share data via a cloud server, allowing family members and doctors to understand the elderly person's condition. This allows the elderly life support system to support the elderly in many ways and maintain their mental and physical health. For example, the system can reduce feelings of loneliness and promote intellectual activity. The system can also manage diet and medication to maintain health. The system also collects health data and notifies users if there are any abnormalities.

[0069] The speech recognition unit can learn the elderly person's past conversation history and generate questions based on their hobbies and interests. For example, the speech recognition unit uses a generation AI to analyze the elderly person's past conversation history and generate questions based on their hobbies and interests. For example, if an elderly person has previously mentioned that they like music, the unit can ask them a question such as, "Tell me about the music you've listened to recently." The speech recognition unit can also save past conversation history and encrypt it to protect privacy. For example, when saving voice data, the data is protected using AES encryption. This allows the system to generate dialogue based on the elderly person's hobbies and interests, reducing their sense of loneliness.

[0070] The voice recognition unit can analyze the tone of voice and speaking style of an elderly person, estimate their emotional state, and provide an appropriate response. For example, the voice recognition unit uses a generation AI to analyze the tone of voice and speaking style of an elderly person in real time to estimate their emotional state. For example, if the voice sounds low, the unit can respond with something like, "Is there anything you're worried about?" The voice recognition unit can also analyze voice waveforms and extract frequency components to estimate their emotional state. For example, it can analyze the peaks and valleys of the voice waveform to evaluate the intensity of the emotion. This can reduce feelings of loneliness by providing an appropriate response according to the elderly person's emotional state.

[0071] The speech recognition unit uses emotion estimation functionality to analyze the emotions expressed by elderly people when they speak in real time and generate dialogue that elicits positive emotions. For example, the speech recognition unit uses a generation AI to analyze the emotions expressed by elderly people when they speak in real time and generate dialogue that elicits positive emotions. For example, if an elderly person is feeling depressed, the unit may generate a dialogue such as, "Would you like to talk about something fun you've had recently?" The speech recognition unit can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This can generate dialogue that elicits positive emotions in elderly people, thereby reducing their sense of loneliness.

[0072] The data linking unit can refer to past conversations between the elderly person and their family and friends and provide common topics of conversation. For example, the generation AI can refer to past conversations between the elderly person and their family and friends and provide common topics of conversation. For example, it can ask a question such as "Tell me about the travel destinations you went on with your family" in reference to a trip that was discussed in a conversation with family. The data linking unit can also clarify the storage format of the voice data and the method of privacy protection. For example, it can encrypt and store the voice data to protect privacy. This can help reduce feelings of loneliness by providing common topics of conversation between the elderly person and their family and friends.

[0073] The data linking unit can suggest topics such as music and movies that the elderly person likes, to liven up the conversation. For example, the data linking unit uses a generation AI to suggest topics such as music and movies that the elderly person likes, to liven up the conversation. For example, it asks questions such as, "Tell me about the last movie you saw." The data linking unit can also analyze questionnaire surveys and viewing history to identify the preferences of the elderly person. For example, it can identify the favorite music genre based on the survey results. This makes it possible to liven up the conversation by suggesting topics such as music and movies that the elderly person likes.

[0074] The data linking unit can use the emotion estimation function to monitor the emotions of elderly people during conversation in real time and provide topics that correspond to their emotions. For example, the data linking unit uses a generation AI to monitor the emotions of elderly people during conversation in real time and provide topics that correspond to their emotions. For example, if an elderly person is feeling depressed, the unit can provide topics such as, "Would you like to talk about something fun you've had recently?" The data linking unit can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This makes it possible to provide topics that correspond to the elderly person's emotions, thereby livening up the conversation.

[0075] Generative AI can learn the dietary history of elderly people and generate individualized meal plans that take nutritional balance into consideration. For example, generative AI can learn the dietary history of elderly people and generate individualized meal plans that take nutritional balance into consideration. For example, based on past meal content, it can make suggestions such as, "You didn't eat many vegetables today, so eat more tomorrow." Generative AI can also clarify how meal content is recorded and how data is saved. For example, it can record meal content with photos and save them in a database. This allows elderly people to maintain their health by providing individualized meal plans that take nutritional balance into consideration.

[0076] Generative AI can analyze photos of meal contents and automatically assess whether nutrients are in excess or insufficient. For example, generative AI can analyze photos of meal contents and automatically assess whether nutrients are in excess or insufficient. For example, it can analyze the amount of vegetables in the photo and make suggestions such as, "There are not many vegetables today, so eat more tomorrow." Generative AI can also analyze meal contents using image recognition technology and nutrient estimation methods. For example, it can use image recognition technology to identify ingredients and estimate the nutrient content. This allows users to maintain their health by analyzing photos of meal contents and automatically assessing whether nutrients are in excess or insufficient.

[0077] Using its emotion estimation function, the generative AI can analyze the emotions of elderly people while they are eating and make meal suggestions that will elicit positive emotions. For example, if an elderly person is feeling down, the generative AI can make a suggestion such as, "Let's cook your favorite dish today." The generative AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generative AI to analyze the emotions of elderly people while they are eating and make meal suggestions that will elicit positive emotions, thereby helping them maintain their health.

[0078] Generative AI can suggest recipes that take into account ingredients from the elderly's local area. For example, it can suggest recipes that use vegetables that are in season in the area. Generative AI can also clarify a list of local specialties and how to obtain ingredients. For example, it can provide information on local farm shops. This allows elderly people to maintain their health by suggesting recipes that take into account ingredients from their local area.

[0079] The generative AI can use its emotion estimation function to monitor the elderly's emotions in real time when suggesting meals and make meal suggestions based on their emotions. For example, if the elderly person is feeling depressed, the generative AI can suggest meals based on their emotions. For example, if the elderly person is feeling depressed, the generative AI can suggest, "Let's make your favorite dish today." The generative AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generative AI to monitor the elderly's emotions in real time when suggesting meals and make meal suggestions based on their emotions, thereby helping to maintain their health.

[0080] Generative AI can learn an elderly person's medication history, analyze the effects and side effects of medication, and propose an optimal medication schedule. For example, generative AI can learn an elderly person's medication history, analyze the effects and side effects of medication, and propose an optimal medication schedule. For example, it can make suggestions such as, "Taking the medicine at this time will increase its effectiveness" based on past medication data. Generative AI can also clarify the method of medication record keeping and the format in which data is stored. For example, medication records can be saved digitally and accumulated in a database. This allows it to learn an elderly person's medication history, analyze the effects and side effects of medication, and propose an optimal medication schedule, thereby helping to maintain health.

[0081] The generation AI can send voice reminders when it's time to take medicine and confirm that it has been taken. For example, the generation AI can send a voice reminder when it's time to take medicine and confirm that it has been taken. For example, it can send a notification such as, "Now is the time to take your blood pressure medication." The generation AI can also set the content of the voice message and the timing of the reminder. For example, it can send a reminder just before it's time to take the medicine and a confirmation message after it has been taken. This makes it possible to prevent forgetting to take medicine by receiving a voice reminder when it's time to take the medicine and confirming that it has been taken.

[0082] Using its emotion estimation function, the generative AI can analyze the emotions of elderly people when taking medicine and provide reminders that elicit positive emotions. For example, if the elderly person is feeling depressed, the generative AI can provide a reminder such as, "Take a short break and relax after taking your medicine." The generative AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This can analyze the emotions of elderly people when taking medicine and provide reminders that elicit positive emotions, thereby encouraging them to take their medicine.

[0083] Generative AI can share the medication status of elderly people with family and doctors in real time. For example, generative AI can share the medication status of elderly people with family and doctors in real time. For example, it can record the time and dosage of medication and notify family and doctors. Generative AI can also clarify the method of medication record and the format in which data is stored. For example, medication records can be saved digitally and accumulated in a database. This allows for proper medication management by sharing medication status with family and doctors in real time.

[0084] Generative AI can add a function to instantly answer questions about taking medication. For example, it can answer questions such as, "Should I take this medicine before meals?" Generative AI can also answer questions using natural language processing technology and answer databases. For example, it can store medication information in a database and provide appropriate answers to questions. This allows for appropriate medication management by instantly answering questions about taking medication.

[0085] Using its emotion estimation function, the generative AI can monitor the emotions of elderly people in real time when taking medicine and provide reminders based on their emotions. For example, if the elderly person is feeling depressed, the generative AI can provide a reminder such as, "Take a short break and relax after taking your medicine." The generative AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generative AI to monitor the emotions of elderly people in real time when taking medicine and provide reminders based on their emotions, enabling appropriate medication management.

[0086] Generative AI can analyze the health data of elderly people over the long term and develop algorithms to predict health risks. For example, generative AI can analyze the health data of elderly people over the long term and develop algorithms to predict health risks. For example, it can make predictions such as "Your risk of heart disease is increasing" based on heart rate and blood pressure data. Generative AI can also clarify how vital signs are recorded and how data is stored. For example, heart rate and blood pressure data can be saved digitally and accumulated in a database. This allows for long-term analysis of health data and prediction of health risks, helping to maintain health.

[0087] Generative AI can provide personalized health advice based on physical condition data. For example, Generative AI can provide personalized health advice based on the physical condition data of an elderly person. For example, it can give advice such as "Take a short rest and relax today" based on heart rate and blood pressure data. Generative AI can also clarify how to analyze physical condition data and the content of advice. For example, it can analyze vital sign data and provide appropriate health advice. This allows for the provision of personalized health advice based on physical condition data, helping to maintain health.

[0088] The generative AI can use its emotion estimation function to analyze the emotions of elderly people when collecting health data and provide advice that elicits positive emotions. For example, the generative AI can analyze the emotions of elderly people when collecting health data and provide advice that elicits positive emotions. For example, if an elderly person is feeling depressed, it can provide advice such as, "Today, do something you enjoy and relax." The generative AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generative AI to analyze the emotions of elderly people when collecting health data and provide advice that elicits positive emotions, thereby helping them maintain their health.

[0089] Generative AI can share the health data of elderly people with family and doctors in real time. Generative AI can, for example, record heart rate and blood pressure data and notify family and doctors. Generative AI can also clarify how vital signs are recorded and the format in which the data is stored. For example, heart rate and blood pressure data can be saved digitally and accumulated in a database. This allows for proper health management by sharing health data with family and doctors in real time.

[0090] Generative AI can add a function to instantly answer questions about health based on physical condition data. Generative AI can add a function to instantly answer questions about health based on physical condition data. For example, it can answer questions such as, "What should I do if my heart rate is high?" Generative AI can also answer questions using natural language processing technology and an answer database. For example, it can store health information in a database and provide appropriate answers to questions. This allows for appropriate health management by instantly answering health questions based on physical condition data.

[0091] Using its emotion estimation function, the generation AI can monitor the emotions of elderly people in real time while collecting health data and provide advice based on their emotions. For example, the generation AI can monitor the emotions of elderly people in real time while collecting health data and provide advice based on their emotions. For example, if they are feeling depressed, it can provide advice such as, "Today, do something you enjoy and relax." The generation AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generation AI to monitor the emotions of elderly people in real time while collecting health data and provide advice based on their emotions, enabling appropriate health management.

[0092] Generative AI can add a function that analyzes posture and movements during exercise and provides guidance on correct form. For example, generative AI can add a function that analyzes posture and movements during exercise and provides guidance on correct form. For example, it can provide guidance such as, "Exercise with your back straight." Generative AI can also analyze posture and movement using motion analysis technology and posture evaluation criteria. For example, it can use motion analysis technology to evaluate posture during exercise and provide guidance on correct form. In this way, health can be maintained by analyzing posture and movements during exercise and providing guidance on correct form.

[0093] Using its emotion estimation function, the generative AI can analyze the emotions of elderly people while they exercise and suggest exercises that will elicit positive emotions. For example, if an elderly person is feeling down, the generative AI might suggest, "Today, let's exercise while listening to your favorite music." The generative AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generative AI to analyze the emotions of elderly people while they exercise and suggest exercises that will elicit positive emotions, thereby helping them maintain their health.

[0094] Generative AI can share an elderly person's exercise history with family and doctors in real time. For example, generative AI can share an elderly person's exercise history with family and doctors in real time. For example, it can record the type and time of exercise and notify family and doctors. Generative AI can also clarify how exercise content is recorded and the format in which data is saved. For example, it can record exercise content in digital format and save it in a database. This allows exercise history to be shared with family and doctors in real time, enabling appropriate exercise management.

[0095] Generative AI can add a function to instantly answer questions about exercise. For example, generative AI can answer questions such as, "How long should I do this exercise?" Generative AI can also answer questions using natural language processing technology and an answer database. For example, it can store information about exercise in a database and provide appropriate answers to questions. This allows for appropriate exercise management by instantly answering questions about exercise.

[0096] Using its emotion estimation function, the generation AI can monitor the emotions of elderly people in real time while they exercise and suggest exercises based on their emotions. For example, if an elderly person is feeling depressed, the generation AI can suggest exercises based on their emotions, such as, "Listen to your favorite music while you exercise today." The generation AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generation AI to monitor the emotions of elderly people in real time while they exercise and suggest exercises based on their emotions, enabling appropriate exercise management.

[0097] Generative AI can analyze the sleep data of elderly people over the long term and provide advice to improve sleep quality. For example, Generative AI can provide advice such as "Go to bed earlier tonight" based on past sleep data. Generative AI can also specify sleep tracking technology and data storage format. For example, sleep data can be recorded digitally and stored in a database. This allows for long-term analysis of sleep data and provides advice to improve sleep quality, helping to maintain health.

[0098] Generative AI can add a function to analyze environmental sounds and body movements during sleep and evaluate sleep quality. For example, if the environmental sounds are noisy, it can give advice such as "Sleep in a quiet environment." Generative AI can also analyze environmental sounds and body movements using voice recognition technology and motion analysis technology. For example, it can identify environmental sounds using voice recognition technology and evaluate body movements using motion analysis technology. This allows you to maintain your health by analyzing environmental sounds and body movements during sleep and evaluating sleep quality.

[0099] The generative AI can use its emotion estimation function to analyze the emotions of elderly people when sleep data is collected and provide advice that will elicit positive emotions. For example, the generative AI can analyze the emotions of elderly people when sleep data is collected and provide advice that will elicit positive emotions. For example, if an elderly person is feeling depressed, it can provide advice such as, "Today, do something you enjoy and relax." The generative AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generative AI to analyze the emotions of elderly people when sleep data is collected and provide advice that will elicit positive emotions, thereby maintaining their health.

[0100] Generative AI can share an elderly person's sleep data with family and doctors in real time. For example, Generative AI can record sleep duration and quality and notify family and doctors. Generative AI can also specify sleep tracking technology and data storage format. For example, sleep data can be recorded digitally and stored in a database. This allows sleep data to be shared with family and doctors in real time, enabling appropriate health management.

[0101] Generative AI can add a function to instantly answer questions about sleep. For example, generative AI can answer questions such as, "What should I do to get a good night's sleep?" Generative AI can also answer questions using natural language processing technology and an answer database. For example, it can store information about sleep in a database and provide appropriate answers to questions. This allows for appropriate health management by instantly answering questions about sleep.

[0102] The generation AI can use its emotion estimation function to monitor the emotions of elderly people in real time while collecting sleep data and provide advice based on their emotions. For example, if an elderly person is feeling depressed, the generation AI can provide advice such as, "Today, do something you enjoy and relax." The generation AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generation AI to monitor the emotions of elderly people in real time while collecting sleep data and provide advice based on their emotions, enabling appropriate health management.

[0103] Generative AI can learn from the elderly person's past schedule history and provide individualized reminders. For example, it can remind them, "You have a doctor's appointment tomorrow," based on past schedule data. Generative AI can also clarify the use of calendar apps and the format in which data is saved. For example, it can record schedule data digitally and save it in a database. This allows it to learn from the elderly person's past schedule history and provide individualized reminders, enabling appropriate schedule management.

[0104] The generation AI can add a function to adjust the frequency and timing of reminders depending on the importance of the schedule. For example, the generation AI can add a function to adjust the frequency and timing of reminders depending on the importance of the schedule. For example, it can set more frequent reminders for important schedules. The generation AI can also clarify the type of schedule and how to set priority. For example, it can evaluate the importance of the schedule and set the frequency and timing of reminders. This allows for appropriate schedule management by adjusting the frequency and timing of reminders depending on the importance of the schedule.

[0105] Generative AI can share an elderly person's schedule with family and doctors in real time. For example, generative AI can record hospital appointments and important events and notify family and doctors. Generative AI can also clarify the use of calendar apps and the format in which data is saved. For example, it can record schedule data digitally and save it in a database. This allows for proper schedule management by sharing schedules with family and doctors in real time.

[0106] Generative AI can add a function to instantly answer questions about schedules. For example, generative AI can answer questions such as, "What are your plans for tomorrow?" Generative AI can also answer questions using natural language processing technology and an answer database. For example, it can store information about schedules in a database and provide appropriate answers to questions. This allows for proper schedule management by instantly answering questions about schedules.

[0107] The generation AI can use its emotion estimation function to monitor the emotions of elderly people in real time during schedule management and provide reminders according to their emotions. For example, the generation AI can monitor the emotions of elderly people in real time during schedule management and provide reminders according to their emotions. For example, if they are feeling down, it can provide a reminder such as, "There's a fun event tomorrow." The generation AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generation AI to monitor the emotions of elderly people in real time during schedule management and provide reminders according to their emotions, enabling appropriate schedule management.

[0108] Generative AI can analyze the stress levels of elderly people over the long term and provide personalized advice for stress reduction. For example, generative AI can provide advice such as "Try engaging in a relaxing activity today" based on past stress data. Generative AI can also clarify the type of stress indicator and evaluation method. For example, it can set an indicator for evaluating stress levels and analyze the data. This allows for long-term analysis of stress levels and provides personalized advice for stress reduction, helping to maintain health.

[0109] Generative AI can suggest relaxation music and guided meditations to relieve stress. For example, generative AI can suggest relaxation music to help the elderly relieve stress. For example, it might suggest, "Listen to this relaxation music to relax today." Generative AI can also clarify the specific content of the relaxation music and guided meditations and how they will be provided. For example, it can set the type of music and the content of the meditation guide, and provide them at the appropriate time. This allows health to be maintained by suggesting relaxation music and guided meditations to relieve stress.

[0110] The generative AI can use its emotion estimation function to analyze the emotions of elderly people when monitoring stress levels and provide advice that will elicit positive emotions. For example, the generative AI can analyze the emotions of elderly people when monitoring stress levels and provide advice that will elicit positive emotions. For example, if an elderly person is feeling depressed, it can provide advice such as, "Today, do something you enjoy and relax." The generative AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generative AI to analyze the emotions of elderly people when monitoring stress levels and provide advice that will elicit positive emotions, thereby maintaining their health.

[0111] Generative AI can share the stress levels of elderly people with family and doctors in real time. For example, generative AI can record changes in stress levels and notify family and doctors. Generative AI can also clarify the types of stress indicators and evaluation methods. For example, it can set indicators for evaluating stress levels and analyze the data. By sharing stress levels with family and doctors in real time, appropriate health management can be carried out.

[0112] Generative AI can add a function to instantly answer questions about stress. For example, generative AI can answer questions such as, "What can I do to reduce stress?" Generative AI can also answer questions using natural language processing technology and an answer database. For example, it can store information about stress in a database and provide appropriate answers to questions. This allows for immediate answers to questions about stress, enabling appropriate health management.

[0113] The generative AI can use its emotion estimation function to monitor the emotions of elderly people in real time when monitoring stress levels and provide advice based on those emotions. For example, when monitoring stress levels, the generative AI can monitor the emotions of elderly people in real time and provide advice based on those emotions. For example, if an elderly person is feeling depressed, the generative AI can provide advice such as, "Today, do something you enjoy and relax." The generative AI can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the generative AI to monitor the emotions of elderly people in real time when monitoring stress levels and provide advice based on those emotions, enabling appropriate health management.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The elderly life support system can further include an exercise recording unit. The exercise recording unit records the amount and type of daily exercise of the elderly person and sends the data to the generation AI. For example, it uses a pedometer or accelerometer to measure walking distance and calories burned and accumulates the data. The exercise recording unit can also automatically classify the type and intensity of exercise and suggest an appropriate exercise plan. For example, if light exercise is needed, it may suggest, "Let's take a walk today." This can support the elderly person's exercise habits and help them maintain their health.

[0116] The elderly life support system can further include an emergency notification unit. The emergency notification unit monitors the elderly person's physical condition data and location information in real time, and notifies family members or doctors if an abnormality is detected. For example, if a sudden increase in heart rate or a fall is detected, a notification is sent immediately. The emergency notification unit can also set emergency response methods in advance and prompt appropriate responses. For example, it can send a message such as "A fall has been detected. Please check immediately." This ensures the safety of the elderly person and enables prompt response.

[0117] The elderly life support system can further include a hobby suggestion unit. The hobby suggestion unit learns the elderly person's past activity history and interests and suggests new hobbies and activities. For example, for an elderly person who used to like gardening, the unit may suggest, "Why not try growing a new plant?" The hobby suggestion unit can also provide information on local events and club activities. For example, it may provide information such as, "There's a gardening club event nearby." This can provide new enjoyment in the lives of the elderly and reduce feelings of loneliness.

[0118] The elderly life support system can further be equipped with a relaxation section. The relaxation section monitors the stress level of the elderly and provides relaxation music and meditation guides. For example, if stress is high, it may suggest, "Try listening to some relaxing music." The relaxation section can also provide guidance on breathing techniques and simple stretches. For example, it may suggest, "Take a deep breath and relax." This helps reduce stress in the elderly and maintain their physical and mental health.

[0119] The elderly life support system may further include a communication promotion unit. The communication promotion unit provides functions to promote communication between the elderly and their family and friends. For example, it can set a reminder to periodically send a message to family members. The communication promotion unit can also provide an interface that makes it easy to make video calls and send and receive messages. For example, it may suggest, "Try making a video call with your family today." This helps the elderly maintain social connections and reduce feelings of loneliness.

[0120] The determination unit can estimate the elderly person's emotions and suggest appropriate relaxation methods based on the estimated emotions. For example, if the elderly person is feeling depressed, the unit can suggest, "Try listening to relaxing music." The determination unit can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the unit to suggest relaxation methods that correspond to the elderly person's emotions, thereby maintaining their physical and mental health.

[0121] The determination unit can estimate the elderly person's emotions and suggest an appropriate exercise plan based on the estimated emotions. For example, if the elderly person is feeling depressed, the unit may suggest, "Try taking a short walk today." The determination unit can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the system to suggest an exercise plan that matches the elderly person's emotions, thereby helping to maintain their physical and mental health.

[0122] The determination unit can estimate the elderly person's emotions and suggest an appropriate meal plan based on the estimated emotions. For example, if the elderly person is feeling depressed, the unit can suggest, "Let's make your favorite dish today." The determination unit can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the system to suggest meal plans that correspond to the elderly person's emotions, thereby maintaining their physical and mental health.

[0123] The determination unit can estimate the elderly person's emotions and provide appropriate sleep advice based on the estimated emotions. For example, if the elderly person is feeling depressed, the unit can provide advice such as "Go to bed early tonight and relax." The determination unit can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the elderly person to maintain their physical and mental health by providing sleep advice that suits their emotions.

[0124] The determination unit can estimate the elderly person's emotions and suggest appropriate stress relief methods based on the estimated emotions. For example, if the elderly person is feeling depressed, the unit can suggest, "Listen to your favorite music today to relax." The determination unit can also estimate emotions using voice feature extraction and emotion classification algorithms. For example, it can analyze the pitch and tempo of the voice and assign emotion labels. This allows the system to suggest stress relief methods that correspond to the elderly person's emotions, thereby maintaining their physical and mental health.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The necklace-type device supports the lifestyles of the elderly. For example, the necklace-type device is lightweight and easy to wear, and is equipped with communication functions such as Bluetooth and Wi-Fi. The necklace-type device also has built-in sensors that measure biometric data such as heart rate and body temperature. Step 2: The smartphone works in conjunction with the necklace-type device to process the generation AI. For example, the smartphone receives data from the necklace-type device through a dedicated application and sends it to the generation AI. Step 3: Generative AI uses text generation AI (e.g., LLM) and multimodal generation AI to support the lives of seniors. For example, generative AI can use voice recognition technology to converse with seniors and reduce their sense of loneliness. Generative AI can also provide functions such as dietary management and medication management. Step 4: The voice recognition unit uses voice recognition technology to converse with the elderly. For example, the voice recognition unit analyzes the elderly's speech in real time and sends it to the generation AI. The voice recognition unit can also analyze the elderly's tone of voice and speaking style to estimate their emotional state. Step 5: The data linking unit links the support data with the child and their family doctor. For example, the data linking unit can share data via a cloud server, allowing family members and doctors to understand the elderly person's condition.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0171] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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]

[0194] 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 necklace-type device; A smartphone and Generative AI and a voice recognition unit that uses voice recognition technology to converse with the elderly; and a data linking unit that links data on support content with the child or the family doctor. A system characterized by:

2. The data linking unit Refer to past conversations with the elderly person's family or friends to provide common topics of conversation The system of claim 1 .

3. The generated AI is The system learns the elderly person's dietary history and generates an individualized meal plan that takes nutritional balance into consideration. The system of claim 1 .

4. The generated AI is The system learns the elderly person's medication history, analyzes the effects and side effects of the medication, and proposes an optimal medication schedule. The system of claim 1 .

5. The generated AI is Analyze the sleep data of the elderly over the long term and provide advice to improve sleep quality The system of claim 1 .

6. The voice recognition unit Analyzing the tone of voice or the manner of speaking of the elderly person to estimate their emotional state and respond accordingly. The system of claim 1 .

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