system

Abstract

The system according to this embodiment aims to enable elderly people to live safe and fulfilling lives even when living alone. [Solution] The system according to the embodiment comprises a conversation unit, a notification unit, a health management unit, and an emotion recognition unit. The conversation unit has a conversation function that utilizes speech recognition and natural language processing. The notification unit has an emergency notification system that works in conjunction with an anomaly detection sensor. The health management unit provides health management support based on behavioral pattern analysis. The emotion recognition unit provides individualized support utilizing emotion recognition technology.

Description

Technical Field

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when an elderly person lives alone, sufficient safety and mental support are not provided, and there is room for improvement.

[0005] The system according to the embodiment aims to enable an elderly person to live a safe and fulfilling life even when living alone.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a conversation unit, a notification unit, a health management unit, and an emotion recognition unit. The conversation unit has a conversation function that utilizes speech recognition and natural language processing. The notification unit has an emergency notification system that works in conjunction with an anomaly detection sensor. The health management unit provides health management support based on behavioral pattern analysis. The emotion recognition unit provides individualized support utilizing emotion recognition technology. [Effects of the Invention]

[0007] The system according to this embodiment can enable elderly people to live safe and fulfilling lives even when living alone. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is designed to enable elderly people to live safe and fulfilling lives even when living alone. This system aims to provide emotional support through conversation and monitor daily safety through AI. Specifically, it is equipped with a conversation function that utilizes speech recognition and natural language processing. This allows elderly people to enjoy everyday conversations with the AI ​​and reduce feelings of loneliness. For example, if an elderly person asks, "What's the weather like today?", the AI ​​can reply, "It's sunny today. It's a good day for a walk." It also includes an emergency notification system linked to an anomaly detection sensor. This allows the AI ​​to automatically issue an emergency notification if an anomaly is detected. For example, if an elderly person falls, the sensor detects the anomaly, and the AI ​​notifies family members or emergency services. Furthermore, it is equipped with a function to support health management based on behavioral pattern analysis. The AI ​​analyzes the behavioral patterns of elderly people and monitors their health status. For example, the AI ​​analyzes the eating and exercise patterns of elderly people and provides health management advice. It also provides individualized support using the AI's emotion recognition technology. This allows the AI ​​to understand the emotional state of elderly people and provide appropriate support. For example, if an elderly person is feeling sad, the AI ​​will ask, "Is there anything I can help you with?" and offer emotional support. This system is integrated with multiple sensors to provide comprehensive safety management. For instance, it works in conjunction with door and window sensors, fire alarms, etc., to respond immediately if an abnormality occurs. Furthermore, it also provides preventative measures through predictive analysis. The AI ​​predicts the behavioral patterns of elderly people and provides advice to prevent danger. For example, the AI ​​might advise, "You've been staying up late lately, so try to go to bed earlier." This system can reduce feelings of loneliness among the elderly and lessen the burden of monitoring on family members. It also continuously monitors the safety and health of the elderly and can respond immediately in emergencies. This provides an environment where elderly people can live with peace of mind even if they live alone. In short, this system enables elderly people to live safe and fulfilling lives even if they live alone.

[0029] The system according to this embodiment comprises a conversation unit, a notification unit, a health management unit, and an emotion recognition unit. The conversation unit has a conversation function that utilizes speech recognition and natural language processing. For example, if an elderly person asks, "What's the weather like today?", the AI ​​can respond, "It's sunny today. It's a good day for a walk." The conversation unit can also reduce feelings of loneliness by having the AI ​​engage in everyday conversations with the elderly person. For example, if an elderly person asks, "What should I eat today?", the AI ​​can advise, "It would be good to eat plenty of vegetables today." Furthermore, the conversation unit can also have the AI ​​estimate the elderly person's emotions and engage in conversation appropriate to those emotions. For example, if an elderly person is sad, the AI ​​can ask, "Is there anything I can help you with?" The notification unit includes an emergency notification system linked to an anomaly detection sensor. For example, if an elderly person falls, the sensor can detect the anomaly, and the AI ​​can notify family members or emergency services. Furthermore, the notification unit can also issue emergency notifications if it detects abnormalities such as fires or gas leaks. For example, if a fire alarm is activated, the AI ​​can notify family members or the fire department. The health management unit provides health management support based on behavioral pattern analysis. For example, the AI ​​can analyze the eating and exercise patterns of elderly people and provide health management advice. The health management unit can also monitor the health status of elderly people and notify if abnormalities are detected. For example, the health management unit can monitor the heart rate and body temperature of elderly people and notify if there are any abnormalities. The emotion recognition unit provides individualized support using emotion recognition technology. For example, the AI ​​can understand the emotional state of elderly people and take appropriate action. For example, if an elderly person is sad, the AI ​​can ask, "Is there anything I can help you with?" and provide emotional support. As a result, the system according to this embodiment can enable elderly people to live safe and fulfilling lives even if they live alone.

[0030] The conversational unit features conversational capabilities that utilize speech recognition and natural language processing. Specifically, the conversational unit uses a high-performance microphone and speech recognition software to accurately recognize what elderly people are saying. The speech recognition technology uses advanced algorithms to remove background noise and clearly capture the speaker's voice. The recognized speech data is sent to a natural language processing (NLP) engine, which generates appropriate responses that are relevant to the context. For example, if an elderly person asks, "What's the weather like today?", the NLP engine analyzes the question and retrieves the latest weather data from the internet to provide weather information. The AI ​​can then respond, "It's sunny today. It's a good day for a walk." The conversational unit also aims to alleviate feelings of loneliness among the elderly through everyday conversation. For example, if an elderly person asks, "What should I eat today?", the AI ​​can advise, "You should eat plenty of vegetables today." Such conversations allow the AI ​​to learn the elderly person's dietary preferences and health condition, enabling it to provide individually customized advice. Furthermore, the conversational unit integrates emotion recognition technology, allowing it to estimate emotions from the elderly person's tone of voice and word choice. For example, if an elderly person is feeling sad, the AI ​​can respond appropriately to their emotions by asking, "Is there anything I can help you with?" This allows the conversational AI to support the mental health of the elderly and improve their quality of life.

[0031] The notification unit is equipped with an emergency notification system that works in conjunction with anomaly detection sensors. Specifically, the notification unit collects data from multiple sensors installed in the living space of the elderly person and detects anomalies. For example, pressure sensors and acceleration sensors installed on the floor can detect anomalies if the elderly person falls. When a sensor detects an anomaly, the data is immediately transmitted to the AI, which then sends a notification to pre-configured contacts (family or emergency services). Notifications are made through multiple communication methods such as telephone, SMS, and email, ensuring that information is reliably transmitted. The notification unit can also detect anomalies such as fires and gas leaks. For example, if a fire alarm is activated, the signal is transmitted to the AI, which then sends an emergency notification to family or the fire department. Furthermore, the notification unit monitors the health status of the elderly person and sends a notification if an anomaly is detected. For example, in cooperation with the health management unit, it can monitor the elderly person's heart rate and body temperature and immediately send a notification if an anomaly is detected. In this way, the notification unit ensures the safety of the elderly person and enables a rapid response in emergencies.

[0032] The Health Management Department provides health management support based on behavioral pattern analysis. Specifically, the Health Management Department collects data on the daily lives of elderly individuals, and AI analyzes this data to monitor their health status. For example, the AI ​​can analyze the eating and exercise patterns of elderly individuals and provide health management advice. Eating data is collected from smart kitchen devices and meal logging apps, and exercise data is obtained from wearable devices. The AI ​​integrates this data to recommend a balanced diet and appropriate exercise levels. The Health Management Department can also continuously monitor the vital signs of elderly individuals (heart rate, body temperature, blood pressure, etc.) and notify them if abnormalities are detected. For example, if the heart rate suddenly increases, the AI ​​will immediately detect the abnormality and notify family members and medical institutions. Furthermore, the Health Management Department can analyze long-term health trends based on past data and predict future health risks. In this way, the Health Management Department can comprehensively support the health of elderly individuals and realize preventative health management.

[0033] The emotion recognition unit provides personalized support utilizing emotion recognition technology. Specifically, it analyzes the emotional state of elderly individuals from their tone of voice, facial expressions, and word choice. Working in conjunction with speech recognition technology, the AI ​​can understand not only the content of what the elderly person says, but also the emotions behind it. For example, if an elderly person is sad, the AI ​​can ask, "Is there anything I can help you with?" and offer emotional support. The emotion recognition unit uses cameras and microphones to monitor changes in the elderly person's facial expressions and voice in real time, instantly detecting changes in their emotions. This allows the AI ​​to respond appropriately to the elderly person's emotional state. For example, if an elderly person is stressed, the AI ​​can offer advice on how to relax or suggest activities to change their mood. Furthermore, the emotion recognition unit can accumulate emotional data from elderly individuals and analyze long-term emotional trends. This allows for a comprehensive understanding of the elderly person's mental health and enables professional intervention when necessary. The emotion recognition unit plays a crucial role in providing emotional support so that elderly individuals can live safely and securely, even when living alone.

[0034] The system includes a sensor integration unit that integrates with multiple sensors to provide comprehensive safety management. This unit works in conjunction with sensors such as door and window sensors and fire alarms, enabling immediate response in the event of an anomaly. For example, if a door sensor detects an intruder, the AI ​​in the sensor integration unit can notify family members or the police. Similarly, if a fire alarm is activated, the AI ​​can notify family members or the fire department. Furthermore, if a gas leak sensor detects an anomaly, the AI ​​can notify family members or the gas company. This integration of multiple sensors enables comprehensive safety management.

[0035] The system includes a predictive analytics unit that provides preventative measures based on predictive analysis. For example, the predictive analytics unit can use AI to predict the behavioral patterns of elderly people and provide advice to prevent potential dangers. For instance, the AI ​​could advise, "You've been staying up late lately, so try to go to bed earlier." The predictive analytics unit can also use AI to predict the health status of elderly people and provide advice on health management. For example, the AI ​​could advise, "You haven't been getting enough exercise lately, so try to exercise a little bit every day." In this way, dangers can be prevented proactively through predictive analysis.

[0036] The conversational AI can engage in everyday conversations with the elderly using speech recognition and natural language processing. For example, if an elderly person asks, "What's the weather like today?", the AI ​​can reply, "It's sunny today. It's a good day for a walk." If an elderly person asks, "What should I eat today?", the AI ​​can advise, "You should eat plenty of vegetables today." Furthermore, if an elderly person confides, "I haven't been able to sleep at night lately," the AI ​​can advise, "To relax, it's a good idea to drink something warm before bed." This allows the elderly to enjoy everyday conversations with the AI ​​and reduce feelings of loneliness.

[0037] The notification unit can work in conjunction with anomaly detection sensors to send emergency notifications when an anomaly is detected. For example, if an elderly person falls, the sensor can detect the anomaly, and the AI ​​can notify family members or emergency services. The notification unit can also send emergency notifications when it detects anomalies such as fire or gas leaks. For example, if a fire alarm is activated, the AI ​​can notify family members or the fire department. Furthermore, if a door sensor detects an intruder, the AI ​​can notify family members or the police. This allows for rapid emergency notifications when an anomaly is detected.

[0038] The Health Management Department can monitor the health status of elderly individuals and provide health management advice based on behavioral pattern analysis. For example, the Health Management Department can use AI to analyze the eating and exercise patterns of elderly individuals and provide health management advice. For instance, the AI ​​could advise, "You haven't been exercising enough lately, so try to exercise a little bit every day." The Health Management Department can also use AI to monitor the health status of elderly individuals and notify them if any abnormalities are detected. For example, the Health Management Department can monitor the heart rate and body temperature of elderly individuals and notify them if any abnormalities are found. This allows for monitoring the health status of elderly individuals and providing appropriate health management advice.

[0039] The conversation function can analyze the past conversation history of elderly individuals and provide optimal conversation topics. For example, it can revisit topics related to hobbies the elderly person has discussed in the past. It can also provide topics related to news or events the elderly person has shown interest in in the past. Furthermore, it can revisit topics related to family members the elderly person has discussed in the past. By providing optimal conversation topics based on past conversation history, conversations with elderly individuals become more fulfilling.

[0040] The conversational AI can insert health management and safety advice at appropriate times, depending on the content of the conversation. For example, if an elderly person complains of feeling unwell, the AI ​​can recommend that they see a doctor. If an elderly person complains of lack of exercise, the AI ​​can suggest light exercise. Furthermore, if an elderly person talks about food, the AI ​​can suggest a balanced diet. In this way, by providing appropriate advice based on the conversation, it can support the health management and safety of the elderly.

[0041] The conversational AI can provide relevant information based on the elderly person's hobbies and interests during a conversation. For example, if the elderly person is interested in gardening, the AI ​​can provide information about seasonal flowers. If the elderly person is interested in cooking, the AI ​​can suggest new recipes. Furthermore, if the elderly person is interested in travel, the AI ​​can provide information about travel destinations. This enriches conversations by providing information tailored to the elderly person's hobbies and interests.

[0042] The conversational AI can offer suggestions to facilitate communication between elderly individuals and their families and friends during conversations. For example, if an elderly person is enjoying a conversation with their family, the AI ​​can suggest a video call with them. If the elderly person wishes to connect with friends, the AI ​​can suggest gatherings with friends. Furthermore, if an elderly person is feeling lonely, the AI ​​can suggest contacting family and friends. This can alleviate feelings of loneliness by promoting communication between elderly individuals and their families and friends.

[0043] The notification unit can analyze data from anomaly detection sensors in real time, enabling early detection of anomalies. For example, if a sensor detects a fall, the AI ​​can immediately send a notification. The notification unit can also send an emergency notification if a sensor detects a fire. Furthermore, if a sensor detects a gas leak, the AI ​​can send a warning. This real-time analysis of anomaly detection sensor data enables early detection of anomalies.

[0044] The notification unit can provide appropriate response procedures depending on the type of anomaly when it receives a notification. For example, if a fall is detected, the notification unit can provide instructions for the AI ​​to contact family or emergency services. If a fire is detected, the notification unit can also provide evacuation procedures. Furthermore, if a gas leak is detected, the notification unit can provide instructions for the AI ​​to shut off the gas valve. This enables a quick and appropriate response by providing response procedures tailored to the type of anomaly.

[0045] The notification unit can provide information to strengthen coordination with the elderly person's family and medical institutions when a notification is sent. For example, if an elderly person complains of feeling unwell, the AI ​​can provide information on how to contact a medical institution. The notification unit can also provide information on how to contact family members if the elderly person feels lonely. Furthermore, if an elderly person encounters an emergency, the AI ​​can provide emergency contact information. This strengthens coordination with the elderly person's family and medical institutions, enabling a quicker and more appropriate response in emergencies.

[0046] The notification unit can select the most appropriate notification method (voice, text, or visual) based on the elderly person's living environment. For example, if the elderly person has a visual impairment, the AI ​​can select a voice notification. If the elderly person has a hearing impairment, the AI ​​can select a text notification. Furthermore, if the elderly person has dementia, the AI ​​can select a visual notification. This ensures that notifications are received reliably by selecting the most appropriate notification method based on the elderly person's living environment.

[0047] The Health Management Department can analyze the behavioral patterns of elderly individuals in detail and detect changes in their health status early. For example, it can analyze their eating patterns to detect nutritional deficiencies early. It can also analyze their exercise patterns to detect lack of exercise early. Furthermore, it can analyze their sleep patterns to detect sleep disorders early. In this way, by analyzing the behavioral patterns of elderly individuals in detail, changes in their health status can be detected early.

[0048] The Health Management Department can provide personalized advice to elderly individuals by referring to their past health data when offering health management advice. For example, the Health Management Department can refer to the results of past health checkups to provide appropriate advice. Furthermore, the Health Management Department can refer to the past medical history to provide appropriate advice. In addition, the Health Management Department can refer to the past exercise history to provide appropriate advice. This allows for personalized health management advice by referring to the elderly individual's past health data.

[0049] The Health Management Department can provide specific health management advice by considering the dietary and exercise history of elderly individuals. For example, it can refer to an elderly individual's dietary history and suggest a nutritionally balanced diet. It can also refer to an elderly individual's exercise history and suggest appropriate exercise. Furthermore, the Health Management Department can comprehensively analyze an elderly individual's dietary and exercise history and make health management suggestions. This allows for specific health management suggestions by considering the dietary and exercise history of elderly individuals.

[0050] The Health Management Department can provide appropriate advice when advising elderly individuals on health management, taking into account their living environment and seasonal changes. For example, the Health Management Department can suggest indoor exercise, taking into account the living environment of elderly individuals. Furthermore, the Health Management Department can suggest seasonally appropriate meals, considering seasonal changes. In addition, the Health Management Department can comprehensively analyze the living environment and seasonal changes of elderly individuals to provide appropriate advice. This allows for appropriate health management advice by considering the living environment and seasonal changes of elderly individuals.

[0051] The emotion recognition unit can improve accuracy by analyzing the elderly person's facial expressions and tone of voice in detail during emotion recognition. For example, the emotion recognition unit can accurately recognize emotions by analyzing the elderly person's facial expressions in detail. It can also accurately recognize emotions by analyzing the elderly person's tone of voice in detail. Furthermore, the emotion recognition unit can accurately recognize emotions by comprehensively analyzing the elderly person's facial expressions and tone of voice. As a result, the accuracy of emotion recognition is improved by analyzing the elderly person's facial expressions and tone of voice in detail.

[0052] The emotion recognition unit can provide individualized responses by referring to the elderly person's past emotional data during emotion recognition. For example, the emotion recognition unit can refer to the elderly person's past emotional data and provide appropriate responses. Furthermore, the emotion recognition unit can analyze the elderly person's past emotional patterns and provide appropriate responses. In addition, the emotion recognition unit can comprehensively analyze the elderly person's past emotional data and current emotions to provide appropriate responses. This enables individualized emotion recognition by referring to the elderly person's past emotional data.

[0053] The emotion recognition unit can analyze changes in emotions by considering the elderly person's living environment and daily events during emotion recognition. For example, the emotion recognition unit can analyze changes in emotions by considering the elderly person's living environment. It can also analyze changes in emotions by considering the elderly person's daily events. Furthermore, the emotion recognition unit can analyze changes in emotions by comprehensively analyzing the elderly person's living environment and daily events. This allows for an accurate analysis of changes in emotions by considering the elderly person's living environment and daily events.

[0054] The emotion recognition unit can make suggestions to facilitate communication between elderly individuals and their family and friends when it recognizes an emotion. For example, if an elderly person is feeling lonely, the emotion recognition unit can suggest communication with family and friends. It can also suggest conversation with family and friends if the elderly person is sad. Furthermore, if the elderly person is agitated, the emotion recognition unit can suggest calm conversation with family and friends. This can alleviate feelings of loneliness by promoting communication between elderly individuals and their family and friends.

[0055] The sensor integration unit can integrate data from multiple sensors to enable early detection of anomalies. For example, it can integrate data from a fall sensor and a heart rate sensor to enable early detection of a fall. It can also integrate data from a fire sensor and a gas leak sensor to enable early detection of a fire. Furthermore, it can integrate data from a door sensor and a window sensor to enable early detection of an intruder. In this way, early detection of anomalies becomes possible by integrating data from multiple sensors.

[0056] The sensor integration unit can propose the optimal sensor placement according to the living environment of the elderly person during sensor integration. For example, the sensor integration unit can propose the optimal placement of fall sensors considering the living environment of the elderly person. It can also propose the optimal placement of fire sensors considering the living environment of the elderly person. Furthermore, the sensor integration unit can propose the optimal placement of gas leak sensors considering the living environment of the elderly person. This enables more effective sensor placement by proposing the optimal sensor placement according to the living environment of the elderly person.

[0057] The predictive analytics department can analyze the behavioral patterns of older adults in detail and predict future risks. For example, it can analyze the eating patterns of older adults and predict the risk of nutritional deficiencies. It can also analyze the exercise patterns of older adults and predict the risk of inactivity. Furthermore, it can analyze the sleep patterns of older adults and predict the risk of sleep disorders. In this way, by analyzing the behavioral patterns of older adults in detail, it is possible to predict future risks.

[0058] The predictive analytics unit can perform risk predictions by considering the living environment and seasonal changes of elderly people during predictive analysis. For example, the predictive analytics unit can predict the risk of falls by considering the living environment of elderly people. It can also predict the risk of catching a cold by considering seasonal changes. Furthermore, the predictive analytics unit can perform risk predictions by comprehensively analyzing the living environment and seasonal changes of elderly people. This improves the accuracy of risk predictions by considering the living environment and seasonal changes of elderly people.

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

[0060] The conversation club can suggest relevant events and activities based on the hobbies and interests of senior citizens. For example, if a senior citizen is interested in gardening, the conversation club can suggest gardening events held in the neighborhood. If a senior citizen is interested in music, the conversation club can provide information on concerts happening nearby. Furthermore, if a senior citizen is interested in cooking, the conversation club can provide information on cooking classes. In this way, by suggesting events and activities based on the hobbies and interests of senior citizens, the club can enhance the quality of their lives.

[0061] The notification unit works in conjunction with the anomaly detection sensor to notify the elderly person themselves when an anomaly is detected. For example, if an elderly person falls, the sensor will detect the anomaly, and the AI ​​can send a notification to the elderly person's smartphone. It can also directly notify the elderly person if an anomaly such as a fire or gas leak is detected. Furthermore, if the door sensor detects an intruder's entry, the AI ​​can send a warning to the elderly person. This allows the elderly person to recognize an anomaly early and respond quickly.

[0062] The Health Management Department can create individualized health management plans based on the dietary and exercise history of elderly individuals. For example, it can analyze an elderly person's dietary history and propose a nutritionally balanced meal plan. It can also analyze an elderly person's exercise history and create an appropriate exercise plan. Furthermore, it can provide health management advice based on the results of elderly individuals' health checkups. This allows for the creation of individualized health management plans for elderly individuals, enabling more effective health management.

[0063] The notification unit works in conjunction with the anomaly detection sensor to notify elderly residents in the vicinity when an anomaly is detected. For example, if an elderly person falls, the sensor will detect the anomaly, and the AI ​​can send a notification to the neighbors. It can also notify neighbors when an anomaly such as a fire or gas leak is detected. Furthermore, if the door sensor detects an intruder, the AI ​​can send a warning to the neighbors. This allows neighbors to recognize an anomaly early and respond quickly.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The conversation unit features conversational capabilities utilizing speech recognition and natural language processing. For example, if an elderly person asks, "What's the weather like today?", the AI ​​can respond, "It's sunny today. It's a good day for a walk." The conversation unit can also alleviate feelings of loneliness by having the AI ​​engage in everyday conversations with the elderly. Furthermore, the conversation unit can estimate the elderly person's emotions and engage in conversations that respond accordingly. Step 2: The notification unit is equipped with an emergency notification system that works in conjunction with an anomaly detection sensor. For example, if an elderly person falls, the sensor will detect the anomaly, and the AI ​​can notify family members or emergency services. It can also send emergency notifications if it detects anomalies such as fire or gas leaks. Step 3: The Health Management Department provides health management support based on behavioral pattern analysis. For example, AI can analyze the eating and exercise patterns of elderly people and provide health management advice. The Health Management Department can also have AI monitor the health status of elderly people and notify them if an abnormality is detected. Step 4: The emotion recognition unit provides personalized support using emotion recognition technology. For example, the AI ​​can understand the emotional state of an elderly person and provide appropriate support. If an elderly person is sad, the AI ​​can ask, "Is there anything I can help you with?" and offer emotional support.

[0066] (Example of form 2) The system according to an embodiment of the present invention is designed to enable elderly people to live safe and fulfilling lives even when living alone. This system aims to provide emotional support through conversation and monitor daily safety through AI. Specifically, it is equipped with a conversation function that utilizes speech recognition and natural language processing. This allows elderly people to enjoy everyday conversations with the AI ​​and reduce feelings of loneliness. For example, if an elderly person asks, "What's the weather like today?", the AI ​​can reply, "It's sunny today. It's a good day for a walk." It also includes an emergency notification system linked to an anomaly detection sensor. This allows the AI ​​to automatically issue an emergency notification if an anomaly is detected. For example, if an elderly person falls, the sensor detects the anomaly, and the AI ​​notifies family members or emergency services. Furthermore, it is equipped with a function to support health management based on behavioral pattern analysis. The AI ​​analyzes the behavioral patterns of elderly people and monitors their health status. For example, the AI ​​analyzes the eating and exercise patterns of elderly people and provides health management advice. It also provides individualized support using the AI's emotion recognition technology. This allows the AI ​​to understand the emotional state of elderly people and provide appropriate support. For example, if an elderly person is feeling sad, the AI ​​will ask, "Is there anything I can help you with?" and offer emotional support. This system is integrated with multiple sensors to provide comprehensive safety management. For instance, it works in conjunction with door and window sensors, fire alarms, etc., to respond immediately if an abnormality occurs. Furthermore, it also provides preventative measures through predictive analysis. The AI ​​predicts the behavioral patterns of elderly people and provides advice to prevent danger. For example, the AI ​​might advise, "You've been staying up late lately, so try to go to bed earlier." This system can reduce feelings of loneliness among the elderly and lessen the burden of monitoring on family members. It also continuously monitors the safety and health of the elderly and can respond immediately in emergencies. This provides an environment where elderly people can live with peace of mind even if they live alone. In short, this system enables elderly people to live safe and fulfilling lives even if they live alone.

[0067] The system according to this embodiment comprises a conversation unit, a notification unit, a health management unit, and an emotion recognition unit. The conversation unit has a conversation function that utilizes speech recognition and natural language processing. For example, if an elderly person asks, "What's the weather like today?", the AI ​​can respond, "It's sunny today. It's a good day for a walk." The conversation unit can also reduce feelings of loneliness by having the AI ​​engage in everyday conversations with the elderly person. For example, if an elderly person asks, "What should I eat today?", the AI ​​can advise, "It would be good to eat plenty of vegetables today." Furthermore, the conversation unit can also have the AI ​​estimate the elderly person's emotions and engage in conversation appropriate to those emotions. For example, if an elderly person is sad, the AI ​​can ask, "Is there anything I can help you with?" The notification unit includes an emergency notification system linked to an anomaly detection sensor. For example, if an elderly person falls, the sensor can detect the anomaly, and the AI ​​can notify family members or emergency services. Furthermore, the notification unit can also issue emergency notifications if it detects abnormalities such as fires or gas leaks. For example, if a fire alarm is activated, the AI ​​can notify family members or the fire department. The health management unit provides health management support based on behavioral pattern analysis. For example, the AI ​​can analyze the eating and exercise patterns of elderly people and provide health management advice. The health management unit can also monitor the health status of elderly people and notify if abnormalities are detected. For example, the health management unit can monitor the heart rate and body temperature of elderly people and notify if there are any abnormalities. The emotion recognition unit provides individualized support using emotion recognition technology. For example, the AI ​​can understand the emotional state of elderly people and take appropriate action. For example, if an elderly person is sad, the AI ​​can ask, "Is there anything I can help you with?" and provide emotional support. As a result, the system according to this embodiment can enable elderly people to live safe and fulfilling lives even if they live alone.

[0068] The conversational unit features conversational capabilities that utilize speech recognition and natural language processing. Specifically, the conversational unit uses a high-performance microphone and speech recognition software to accurately recognize what elderly people are saying. The speech recognition technology uses advanced algorithms to remove background noise and clearly capture the speaker's voice. The recognized speech data is sent to a natural language processing (NLP) engine, which generates appropriate responses that are relevant to the context. For example, if an elderly person asks, "What's the weather like today?", the NLP engine analyzes the question and retrieves the latest weather data from the internet to provide weather information. The AI ​​can then respond, "It's sunny today. It's a good day for a walk." The conversational unit also aims to alleviate feelings of loneliness among the elderly through everyday conversation. For example, if an elderly person asks, "What should I eat today?", the AI ​​can advise, "You should eat plenty of vegetables today." Such conversations allow the AI ​​to learn the elderly person's dietary preferences and health condition, enabling it to provide individually customized advice. Furthermore, the conversational unit integrates emotion recognition technology, allowing it to estimate emotions from the elderly person's tone of voice and word choice. For example, if an elderly person is feeling sad, the AI ​​can respond appropriately to their emotions by asking, "Is there anything I can help you with?" This allows the conversational AI to support the mental health of the elderly and improve their quality of life.

[0069] The notification unit is equipped with an emergency notification system that works in conjunction with anomaly detection sensors. Specifically, the notification unit collects data from multiple sensors installed in the living space of the elderly person and detects anomalies. For example, pressure sensors and acceleration sensors installed on the floor can detect anomalies if the elderly person falls. When a sensor detects an anomaly, the data is immediately transmitted to the AI, which then sends a notification to pre-configured contacts (family or emergency services). Notifications are made through multiple communication methods such as telephone, SMS, and email, ensuring that information is reliably transmitted. The notification unit can also detect anomalies such as fires and gas leaks. For example, if a fire alarm is activated, the signal is transmitted to the AI, which then sends an emergency notification to family or the fire department. Furthermore, the notification unit monitors the health status of the elderly person and sends a notification if an anomaly is detected. For example, in cooperation with the health management unit, it can monitor the elderly person's heart rate and body temperature and immediately send a notification if an anomaly is detected. In this way, the notification unit ensures the safety of the elderly person and enables a rapid response in emergencies.

[0070] The Health Management Department provides health management support based on behavioral pattern analysis. Specifically, the Health Management Department collects data on the daily lives of elderly individuals, and AI analyzes this data to monitor their health status. For example, the AI ​​can analyze the eating and exercise patterns of elderly individuals and provide health management advice. Eating data is collected from smart kitchen devices and meal logging apps, and exercise data is obtained from wearable devices. The AI ​​integrates this data to recommend a balanced diet and appropriate exercise levels. The Health Management Department can also continuously monitor the vital signs of elderly individuals (heart rate, body temperature, blood pressure, etc.) and notify them if abnormalities are detected. For example, if the heart rate suddenly increases, the AI ​​will immediately detect the abnormality and notify family members and medical institutions. Furthermore, the Health Management Department can analyze long-term health trends based on past data and predict future health risks. In this way, the Health Management Department can comprehensively support the health of elderly individuals and realize preventative health management.

[0071] The emotion recognition unit provides personalized support utilizing emotion recognition technology. Specifically, it analyzes the emotional state of elderly individuals from their tone of voice, facial expressions, and word choice. Working in conjunction with speech recognition technology, the AI ​​can understand not only the content of what the elderly person says, but also the emotions behind it. For example, if an elderly person is sad, the AI ​​can ask, "Is there anything I can help you with?" and offer emotional support. The emotion recognition unit uses cameras and microphones to monitor changes in the elderly person's facial expressions and voice in real time, instantly detecting changes in their emotions. This allows the AI ​​to respond appropriately to the elderly person's emotional state. For example, if an elderly person is stressed, the AI ​​can offer advice on how to relax or suggest activities to change their mood. Furthermore, the emotion recognition unit can accumulate emotional data from elderly individuals and analyze long-term emotional trends. This allows for a comprehensive understanding of the elderly person's mental health and enables professional intervention when necessary. The emotion recognition unit plays a crucial role in providing emotional support so that elderly individuals can live safely and securely, even when living alone.

[0072] The system includes a sensor integration unit that integrates with multiple sensors to provide comprehensive safety management. This unit works in conjunction with sensors such as door and window sensors and fire alarms, enabling immediate response in the event of an anomaly. For example, if a door sensor detects an intruder, the AI ​​in the sensor integration unit can notify family members or the police. Similarly, if a fire alarm is activated, the AI ​​can notify family members or the fire department. Furthermore, if a gas leak sensor detects an anomaly, the AI ​​can notify family members or the gas company. This integration of multiple sensors enables comprehensive safety management.

[0073] The system includes a predictive analytics unit that provides preventative measures based on predictive analysis. For example, the predictive analytics unit can use AI to predict the behavioral patterns of elderly people and provide advice to prevent potential dangers. For instance, the AI ​​could advise, "You've been staying up late lately, so try to go to bed earlier." The predictive analytics unit can also use AI to predict the health status of elderly people and provide advice on health management. For example, the AI ​​could advise, "You haven't been getting enough exercise lately, so try to exercise a little bit every day." In this way, dangers can be prevented proactively through predictive analysis.

[0074] The conversational AI can engage in everyday conversations with the elderly using speech recognition and natural language processing. For example, if an elderly person asks, "What's the weather like today?", the AI ​​can reply, "It's sunny today. It's a good day for a walk." If an elderly person asks, "What should I eat today?", the AI ​​can advise, "You should eat plenty of vegetables today." Furthermore, if an elderly person confides, "I haven't been able to sleep at night lately," the AI ​​can advise, "To relax, it's a good idea to drink something warm before bed." This allows the elderly to enjoy everyday conversations with the AI ​​and reduce feelings of loneliness.

[0075] The notification unit can work in conjunction with anomaly detection sensors to send emergency notifications when an anomaly is detected. For example, if an elderly person falls, the sensor can detect the anomaly, and the AI ​​can notify family members or emergency services. The notification unit can also send emergency notifications when it detects anomalies such as fire or gas leaks. For example, if a fire alarm is activated, the AI ​​can notify family members or the fire department. Furthermore, if a door sensor detects an intruder, the AI ​​can notify family members or the police. This allows for rapid emergency notifications when an anomaly is detected.

[0076] The Health Management Department can monitor the health status of elderly individuals and provide health management advice based on behavioral pattern analysis. For example, the Health Management Department can use AI to analyze the eating and exercise patterns of elderly individuals and provide health management advice. For instance, the AI ​​could advise, "You haven't been exercising enough lately, so try to exercise a little bit every day." The Health Management Department can also use AI to monitor the health status of elderly individuals and notify them if any abnormalities are detected. For example, the Health Management Department can monitor the heart rate and body temperature of elderly individuals and notify them if any abnormalities are found. This allows for monitoring the health status of elderly individuals and providing appropriate health management advice.

[0077] The emotion recognition unit can understand the emotional state of elderly people and respond appropriately by utilizing emotion recognition technology. For example, the emotion recognition unit's AI can understand the emotional state of elderly people and respond appropriately. For instance, if an elderly person is sad, the AI ​​can ask, "Is there anything I can help you with?" and offer emotional support. Also, if an elderly person is agitated, the AI ​​can encourage them to "calm down." Furthermore, if an elderly person is feeling lonely, the AI ​​can suggest, "Let's talk together." In this way, the emotional recognition unit can understand the emotional state of elderly people and respond appropriately. Emotion estimation is achieved using emotion estimation functions with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The conversational unit can estimate the emotions of elderly individuals and select conversation topics based on those estimated emotions. For example, if an elderly person is sad, the AI ​​can suggest topics related to happy memories from the past. Similarly, if an elderly person is agitated, the AI ​​can suggest topics that help them relax. Furthermore, if an elderly person is feeling lonely, the AI ​​can suggest stories about family and friends. This allows for more appropriate conversations by providing topics that match the elderly person's emotions.

[0079] The conversation function can analyze the past conversation history of elderly individuals and provide optimal conversation topics. For example, it can revisit topics related to hobbies the elderly person has discussed in the past. It can also provide topics related to news or events the elderly person has shown interest in in the past. Furthermore, it can revisit topics related to family members the elderly person has discussed in the past. By providing optimal conversation topics based on past conversation history, conversations with elderly individuals become more fulfilling.

[0080] The conversational AI can insert health management and safety advice at appropriate times, depending on the content of the conversation. For example, if an elderly person complains of feeling unwell, the AI ​​can recommend that they see a doctor. If an elderly person complains of lack of exercise, the AI ​​can suggest light exercise. Furthermore, if an elderly person talks about food, the AI ​​can suggest a balanced diet. In this way, by providing appropriate advice based on the conversation, it can support the health management and safety of the elderly.

[0081] The conversational unit can estimate the emotions of elderly individuals and adjust the tone and speed of the conversation based on the estimated emotions. For example, if an elderly person is depressed, the AI ​​can speak slowly in a gentle tone. Similarly, if an elderly person is agitated, the AI ​​can speak in a calm tone. Furthermore, if an elderly person is tired, the AI ​​can engage in short conversations in a quiet tone. This allows for more appropriate conversations by adjusting the tone and speed of the conversation according to the elderly person's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The conversational AI can provide relevant information based on the elderly person's hobbies and interests during a conversation. For example, if the elderly person is interested in gardening, the AI ​​can provide information about seasonal flowers. If the elderly person is interested in cooking, the AI ​​can suggest new recipes. Furthermore, if the elderly person is interested in travel, the AI ​​can provide information about travel destinations. This enriches conversations by providing information tailored to the elderly person's hobbies and interests.

[0083] The conversational AI can offer suggestions to facilitate communication between elderly individuals and their families and friends during conversations. For example, if an elderly person is enjoying a conversation with their family, the AI ​​can suggest a video call with them. If the elderly person wishes to connect with friends, the AI ​​can suggest gatherings with friends. Furthermore, if an elderly person is feeling lonely, the AI ​​can suggest contacting family and friends. This can alleviate feelings of loneliness by promoting communication between elderly individuals and their families and friends.

[0084] The notification unit can estimate the emotions of elderly individuals and adjust the content and method of notifications based on the estimated emotions. For example, if an elderly person is feeling anxious, the AI ​​can send a reassuring notification. Similarly, if an elderly person is relaxed, the AI ​​can send a calm notification. Furthermore, if an elderly person is agitated, the AI ​​can send a calm notification. This allows for more appropriate notifications by adjusting the content and method of notifications according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The notification unit can analyze data from anomaly detection sensors in real time, enabling early detection of anomalies. For example, if a sensor detects a fall, the AI ​​can immediately send a notification. The notification unit can also send an emergency notification if a sensor detects a fire. Furthermore, if a sensor detects a gas leak, the AI ​​can send a warning. This real-time analysis of anomaly detection sensor data enables early detection of anomalies.

[0086] The notification unit can provide appropriate response procedures depending on the type of anomaly when it receives a notification. For example, if a fall is detected, the notification unit can provide instructions for the AI ​​to contact family or emergency services. If a fire is detected, the notification unit can also provide evacuation procedures. Furthermore, if a gas leak is detected, the notification unit can provide instructions for the AI ​​to shut off the gas valve. This enables a quick and appropriate response by providing response procedures tailored to the type of anomaly.

[0087] The notification unit can estimate the emotions of elderly individuals and determine the priority of notifications based on those estimated emotions. For example, if an elderly individual is feeling anxious, the notification unit can prioritize sending important notifications. It can also send normal notifications if the elderly individual is relaxed. Furthermore, if the elderly individual is agitated, the notification unit can prioritize sending urgent notifications. This allows for the prioritization of important notifications based on the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The notification unit can provide information to strengthen coordination with the elderly person's family and medical institutions when a notification is sent. For example, if an elderly person complains of feeling unwell, the AI ​​can provide information on how to contact a medical institution. The notification unit can also provide information on how to contact family members if the elderly person feels lonely. Furthermore, if an elderly person encounters an emergency, the AI ​​can provide emergency contact information. This strengthens coordination with the elderly person's family and medical institutions, enabling a quicker and more appropriate response in emergencies.

[0089] The notification unit can select the most appropriate notification method (voice, text, or visual) based on the elderly person's living environment. For example, if the elderly person has a visual impairment, the AI ​​can select a voice notification. If the elderly person has a hearing impairment, the AI ​​can select a text notification. Furthermore, if the elderly person has dementia, the AI ​​can select a visual notification. This ensures that notifications are received reliably by selecting the most appropriate notification method based on the elderly person's living environment.

[0090] The Health Management Department can estimate the emotions of elderly individuals and adjust health management advice based on those estimated emotions. For example, if an elderly person is feeling stressed, the AI ​​can suggest ways to relax. Similarly, if an elderly person is tired, the AI ​​can suggest rest. Furthermore, if an elderly person is feeling energetic, the AI ​​can suggest exercise. This allows for more appropriate health management by providing health management advice tailored to the emotions of elderly individuals. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The Health Management Department can analyze the behavioral patterns of elderly individuals in detail and detect changes in their health status early. For example, it can analyze their eating patterns to detect nutritional deficiencies early. It can also analyze their exercise patterns to detect lack of exercise early. Furthermore, it can analyze their sleep patterns to detect sleep disorders early. In this way, by analyzing the behavioral patterns of elderly individuals in detail, changes in their health status can be detected early.

[0092] The Health Management Department can provide personalized advice to elderly individuals by referring to their past health data when offering health management advice. For example, the Health Management Department can refer to the results of past health checkups to provide appropriate advice. Furthermore, the Health Management Department can refer to the past medical history to provide appropriate advice. In addition, the Health Management Department can refer to the past exercise history to provide appropriate advice. This allows for personalized health management advice by referring to the elderly individual's past health data.

[0093] The Health Management Department can estimate the emotions of elderly individuals and determine health management priorities based on those estimated emotions. For example, if an elderly person is stressed, the Health Management Department can prioritize stress reduction. Similarly, if an elderly person is tired, the Health Management Department can prioritize rest. Furthermore, if an elderly person is energetic, the Health Management Department can prioritize exercise. This allows for more appropriate health management by prioritizing health management according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The Health Management Department can provide specific health management advice by considering the dietary and exercise history of elderly individuals. For example, it can refer to an elderly individual's dietary history and suggest a nutritionally balanced diet. It can also refer to an elderly individual's exercise history and suggest appropriate exercise. Furthermore, the Health Management Department can comprehensively analyze an elderly individual's dietary and exercise history and make health management suggestions. This allows for specific health management suggestions by considering the dietary and exercise history of elderly individuals.

[0095] The Health Management Department can provide appropriate advice when advising elderly individuals on health management, taking into account their living environment and seasonal changes. For example, the Health Management Department can suggest indoor exercise, taking into account the living environment of elderly individuals. Furthermore, the Health Management Department can suggest seasonally appropriate meals, considering seasonal changes. In addition, the Health Management Department can comprehensively analyze the living environment and seasonal changes of elderly individuals to provide appropriate advice. This allows for appropriate health management advice by considering the living environment and seasonal changes of elderly individuals.

[0096] The emotion recognition unit can estimate the emotions of elderly individuals and take appropriate action based on the estimated emotions. For example, if an elderly person is sad, the AI ​​can offer words of comfort. If an elderly person is agitated, the AI ​​can encourage them to calm down. Furthermore, if an elderly person is feeling lonely, the AI ​​can continue the conversation. This allows the AI ​​to provide emotional support by responding appropriately to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The emotion recognition unit can improve accuracy by analyzing the elderly person's facial expressions and tone of voice in detail during emotion recognition. For example, the emotion recognition unit can accurately recognize emotions by analyzing the elderly person's facial expressions in detail. It can also accurately recognize emotions by analyzing the elderly person's tone of voice in detail. Furthermore, the emotion recognition unit can accurately recognize emotions by comprehensively analyzing the elderly person's facial expressions and tone of voice. As a result, the accuracy of emotion recognition is improved by analyzing the elderly person's facial expressions and tone of voice in detail.

[0098] The emotion recognition unit can provide individualized responses by referring to the elderly person's past emotional data during emotion recognition. For example, the emotion recognition unit can refer to the elderly person's past emotional data and provide appropriate responses. Furthermore, the emotion recognition unit can analyze the elderly person's past emotional patterns and provide appropriate responses. In addition, the emotion recognition unit can comprehensively analyze the elderly person's past emotional data and current emotions to provide appropriate responses. This enables individualized emotion recognition by referring to the elderly person's past emotional data.

[0099] The emotion recognition unit can estimate the emotions of elderly individuals and adjust the method of displaying the emotion recognition results based on the estimated emotions. For example, if an elderly person is depressed, the emotion recognition unit can display the emotion recognition results in gentle words. It can also display the emotion recognition results in calm words if the elderly person is agitated. Furthermore, if an elderly person is feeling lonely, the emotion recognition unit can display the emotion recognition results in encouraging words. This allows for more appropriate responses by adjusting the method of displaying the emotion recognition results according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The emotion recognition unit can analyze changes in emotions by considering the elderly person's living environment and daily events during emotion recognition. For example, the emotion recognition unit can analyze changes in emotions by considering the elderly person's living environment. It can also analyze changes in emotions by considering the elderly person's daily events. Furthermore, the emotion recognition unit can analyze changes in emotions by comprehensively analyzing the elderly person's living environment and daily events. This allows for an accurate analysis of changes in emotions by considering the elderly person's living environment and daily events.

[0101] The emotion recognition unit can make suggestions to facilitate communication between elderly individuals and their family and friends when it recognizes an emotion. For example, if an elderly person is feeling lonely, the emotion recognition unit can suggest communication with family and friends. It can also suggest conversation with family and friends if the elderly person is sad. Furthermore, if the elderly person is agitated, the emotion recognition unit can suggest calm conversation with family and friends. This can alleviate feelings of loneliness by promoting communication between elderly individuals and their family and friends.

[0102] The sensor integration unit can estimate the emotions of elderly individuals and adjust the frequency of sensor data collection based on the estimated emotions. For example, if an elderly individual is feeling anxious, the sensor integration unit can increase the frequency of sensor data collection. Conversely, if an elderly individual is relaxed, the sensor integration unit can return the frequency of sensor data collection to normal. Furthermore, if an elderly individual is agitated, the sensor integration unit can adjust the frequency of sensor data collection. This allows for more appropriate data collection by adjusting the frequency of sensor data collection according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The sensor integration unit can integrate data from multiple sensors to enable early detection of anomalies. For example, it can integrate data from a fall sensor and a heart rate sensor to enable early detection of a fall. It can also integrate data from a fire sensor and a gas leak sensor to enable early detection of a fire. Furthermore, it can integrate data from a door sensor and a window sensor to enable early detection of an intruder. In this way, early detection of anomalies becomes possible by integrating data from multiple sensors.

[0104] The sensor integration unit can estimate the emotions of elderly individuals and adjust the display method of sensor data based on the estimated emotions. For example, if an elderly individual is feeling anxious, the sensor integration unit can display the sensor data in detail. Conversely, if an elderly individual is relaxed, the sensor integration unit can display the sensor data concisely. Furthermore, if an elderly individual is agitated, the sensor integration unit can provide the sensor data in a calm display manner. This allows for more appropriate data display by adjusting the sensor data display method according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The sensor integration unit can propose the optimal sensor placement according to the living environment of the elderly person during sensor integration. For example, the sensor integration unit can propose the optimal placement of fall sensors considering the living environment of the elderly person. It can also propose the optimal placement of fire sensors considering the living environment of the elderly person. Furthermore, the sensor integration unit can propose the optimal placement of gas leak sensors considering the living environment of the elderly person. This enables more effective sensor placement by proposing the optimal sensor placement according to the living environment of the elderly person.

[0106] The predictive analytics unit can estimate the emotions of elderly individuals and adjust the predictive analysis results based on the estimated emotions. For example, if an elderly individual is feeling anxious, the predictive analytics unit can provide detailed predictive analysis results. It can also provide concise results if the elderly individual is relaxed. Furthermore, if the elderly individual is agitated, the predictive analytics unit can provide results in a calm manner. This allows for more appropriate predictive analysis by adjusting the results according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The predictive analytics department can analyze the behavioral patterns of older adults in detail and predict future risks. For example, it can analyze the eating patterns of older adults and predict the risk of nutritional deficiencies. It can also analyze the exercise patterns of older adults and predict the risk of inactivity. Furthermore, it can analyze the sleep patterns of older adults and predict the risk of sleep disorders. In this way, by analyzing the behavioral patterns of older adults in detail, it is possible to predict future risks.

[0108] The predictive analytics unit can estimate the emotions of elderly individuals and prioritize predictive analytics based on these estimated emotions. For example, if an elderly individual is feeling anxious, the predictive analytics unit can prioritize providing important predictive analytics. It can also provide standard predictive analytics if the elderly individual is relaxed. Furthermore, if the elderly individual is agitated, the predictive analytics unit can prioritize providing urgent predictive analytics. This allows for the prioritization of important predictive analytics based on the elderly individual's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The predictive analytics unit can perform risk predictions by considering the living environment and seasonal changes of elderly people during predictive analysis. For example, the predictive analytics unit can predict the risk of falls by considering the living environment of elderly people. It can also predict the risk of catching a cold by considering seasonal changes. Furthermore, the predictive analytics unit can perform risk predictions by comprehensively analyzing the living environment and seasonal changes of elderly people. This improves the accuracy of risk predictions by considering the living environment and seasonal changes of elderly people.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The conversation club can suggest relevant events and activities based on the hobbies and interests of senior citizens. For example, if a senior citizen is interested in gardening, the conversation club can suggest gardening events held in the neighborhood. If a senior citizen is interested in music, the conversation club can provide information on concerts happening nearby. Furthermore, if a senior citizen is interested in cooking, the conversation club can provide information on cooking classes. In this way, by suggesting events and activities based on the hobbies and interests of senior citizens, the club can enhance the quality of their lives.

[0112] The notification unit works in conjunction with the anomaly detection sensor to notify the elderly person themselves when an anomaly is detected. For example, if an elderly person falls, the sensor will detect the anomaly, and the AI ​​can send a notification to the elderly person's smartphone. It can also directly notify the elderly person if an anomaly such as a fire or gas leak is detected. Furthermore, if the door sensor detects an intruder's entry, the AI ​​can send a warning to the elderly person. This allows the elderly person to recognize an anomaly early and respond quickly.

[0113] The Health Management Department can create individualized health management plans based on the dietary and exercise history of elderly individuals. For example, it can analyze an elderly person's dietary history and propose a nutritionally balanced meal plan. It can also analyze an elderly person's exercise history and create an appropriate exercise plan. Furthermore, it can provide health management advice based on the results of elderly individuals' health checkups. This allows for the creation of individualized health management plans for elderly individuals, enabling more effective health management.

[0114] The emotion recognition unit can estimate the emotions of elderly individuals and play appropriate music based on those estimates. For example, if an elderly person is feeling depressed, the emotion recognition unit can play relaxing music. If an elderly person is feeling agitated, the unit can play calming music. Furthermore, if an elderly person is feeling lonely, the unit can play cheerful music. This allows for emotional stability by providing music tailored to the elderly person's emotions.

[0115] The conversational unit can estimate the emotions of elderly individuals and, based on those estimates, provide appropriate jokes and humor. For example, if an elderly person is feeling down, the conversational unit can offer a lighthearted joke to bring a smile to their face. If an elderly person is excited, the conversational unit can help them relax with humorous conversation. Furthermore, if an elderly person is feeling lonely, the conversational unit can brighten their mood by offering enjoyable topics. In this way, by providing jokes and humor tailored to the emotions of elderly individuals, the unit can provide emotional support.

[0116] The notification unit works in conjunction with the anomaly detection sensor to notify elderly residents in the vicinity when an anomaly is detected. For example, if an elderly person falls, the sensor will detect the anomaly, and the AI ​​can send a notification to the neighbors. It can also notify neighbors when an anomaly such as a fire or gas leak is detected. Furthermore, if the door sensor detects an intruder, the AI ​​can send a warning to the neighbors. This allows neighbors to recognize an anomaly early and respond quickly.

[0117] The Health Management Department can estimate the emotions of elderly individuals and, based on those estimates, suggest appropriate relaxation methods. For example, if an elderly person is feeling stressed, the Health Management Department can suggest deep breathing or meditation. If an elderly person is tired, the Health Management Department can suggest light stretching. Furthermore, if an elderly person is feeling energetic, the Health Management Department can suggest walking or light exercise. In this way, by providing relaxation methods tailored to the emotions of elderly individuals, the department can support their physical and mental health.

[0118] The emotion recognition unit can estimate the emotions of elderly people and recommend appropriate movies and television programs based on those estimates. For example, if an elderly person is depressed, the emotion recognition unit can recommend a relaxing movie. If an elderly person is agitated, the emotion recognition unit can recommend a calming television program. Furthermore, if an elderly person is feeling lonely, the emotion recognition unit can recommend a cheerful movie. In this way, by providing movies and television programs that match the emotions of elderly people, it is possible to promote emotional stability.

[0119] The conversation function can estimate the emotions of elderly individuals and, based on those estimates, suggest appropriate reading material. For example, if an elderly person is feeling depressed, the conversation function can suggest a relaxing book. If an elderly person is agitated, the conversation function can suggest a calming book. Furthermore, if an elderly person is feeling lonely, the conversation function can suggest a cheerful book. In this way, by suggesting reading material that matches the emotions of elderly individuals, it can provide emotional support.

[0120] The Health Management Department can estimate the emotions of elderly individuals and, based on those estimates, suggest appropriate meals. For example, if an elderly person is stressed, the Health Management Department can suggest a relaxing meal. If an elderly person is tired, the Health Management Department can suggest a nutritious meal. Furthermore, if an elderly person is energetic, the Health Management Department can suggest a balanced meal. In this way, by suggesting meals tailored to the emotions of elderly individuals, the department can support their physical and mental health.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The conversation unit features conversational capabilities utilizing speech recognition and natural language processing. For example, if an elderly person asks, "What's the weather like today?", the AI ​​can respond, "It's sunny today. It's a good day for a walk." The conversation unit can also alleviate feelings of loneliness by having the AI ​​engage in everyday conversations with the elderly. Furthermore, the conversation unit can estimate the elderly person's emotions and engage in conversations that respond accordingly. Step 2: The notification unit is equipped with an emergency notification system that works in conjunction with an anomaly detection sensor. For example, if an elderly person falls, the sensor will detect the anomaly, and the AI ​​can notify family members or emergency services. It can also send emergency notifications if it detects anomalies such as fire or gas leaks. Step 3: The Health Management Department provides health management support based on behavioral pattern analysis. For example, AI can analyze the eating and exercise patterns of elderly people and provide health management advice. The Health Management Department can also have AI monitor the health status of elderly people and notify them if an abnormality is detected. Step 4: The emotion recognition unit provides personalized support using emotion recognition technology. For example, the AI ​​can understand the emotional state of an elderly person and provide appropriate support. If an elderly person is sad, the AI ​​can ask, "Is there anything I can help you with?" and offer emotional support.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the conversation unit, notification unit, health management unit, and emotion recognition unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the conversation unit is implemented by the control unit 46A of the smart device 14 and provides a conversation function utilizing speech recognition and natural language processing. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an emergency notification system linked to an anomaly detection sensor. The health management unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides health management support based on behavioral pattern analysis. The emotion recognition unit is implemented by the control unit 46A of the smart device 14 and provides individualized support utilizing emotion recognition technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the conversation unit, notification unit, health management unit, and emotion recognition unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the conversation unit is implemented by the control unit 46A of the smart glasses 214 and provides a conversation function utilizing speech recognition and natural language processing. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an emergency notification system linked to an anomaly detection sensor. The health management unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides health management support based on behavioral pattern analysis. The emotion recognition unit is implemented by the control unit 46A of the smart glasses 214 and provides individualized support utilizing emotion recognition technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the conversation unit, notification unit, health management unit, and emotion recognition unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the conversation unit is implemented by the control unit 46A of the headset terminal 314 and provides conversational functionality utilizing speech recognition and natural language processing. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an emergency notification system linked to an anomaly detection sensor. The health management unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides health management support based on behavioral pattern analysis. The emotion recognition unit is implemented by the control unit 46A of the headset terminal 314 and provides individualized support utilizing emotion recognition technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the conversation unit, notification unit, health management unit, and emotion recognition unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the conversation unit is implemented by the control unit 46A of the robot 414 and provides conversational functionality utilizing speech recognition and natural language processing. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an emergency notification system linked to an anomaly detection sensor. The health management unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides health management support based on behavioral pattern analysis. The emotion recognition unit is implemented by the control unit 46A of the robot 414 and provides individualized support utilizing emotion recognition technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has 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 ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to 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] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A conversation unit equipped with conversational functions utilizing speech recognition and natural language processing, A notification unit equipped with an emergency notification system linked to an anomaly detection sensor, The Health Management Department provides health management support based on behavioral pattern analysis, It includes an emotion recognition unit that provides individualized support using emotion recognition technology. A system characterized by the following features. (Note 2) It features a sensor integration unit that integrates with multiple sensors to perform comprehensive safety management. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a predictive analysis unit that provides preventative measures based on predictive analysis. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned conversation section is, Using speech recognition and natural language processing to engage in everyday conversations with the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, It works in conjunction with an anomaly detection sensor to send an emergency notification when an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned health management department, Based on behavioral pattern analysis, we monitor the health status of older adults and provide health management advice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The emotion recognition unit, Using emotion recognition technology to understand the emotional state of elderly people and provide appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned conversation section is, The system estimates the emotions of elderly individuals and selects conversation topics based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned conversation section is, Analyzes the past conversation history of elderly people and provides optimal conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned conversation section is, Depending on the content of the conversation, insert health management and safety advice at appropriate times. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned conversation section is, It estimates the emotions of elderly people and adjusts the tone and speed of conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned conversation section is, During conversations, provide relevant information based on the hobbies and interests of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned conversation section is, During conversations, make suggestions to facilitate communication between elderly people and their family and friends. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, The system estimates the emotions of elderly individuals and adjusts the content and method of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, Data from anomaly detection sensors is analyzed in real time to enable early detection of anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, When a notification is sent, appropriate response procedures will be provided depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, The system estimates the emotions of older adults and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, When notifying, provide information to strengthen collaboration with the elderly person's family and medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When sending notifications, the most appropriate notification method will be selected according to the living environment of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned health management department, The system estimates the emotions of older adults and adjusts health management advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned health management department, By analyzing the behavioral patterns of the elderly in detail, changes in their health status can be detected early. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned health management department, When providing health management advice, we refer to the elderly person's past health data to provide individualized support. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned health management department, The system estimates the emotions of older adults and determines health management priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned health management department, When providing health management advice, specific suggestions should be made considering the elderly person's dietary and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned health management department, When providing health management advice, we offer appropriate advice that takes into account the living environment and seasonal changes of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 26) The emotion recognition unit, To estimate the emotions of elderly people and to take appropriate action based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The emotion recognition unit, To improve accuracy during emotion recognition, the system analyzes the facial expressions and tone of voice of elderly individuals in detail. The system described in Appendix 1, characterized by the features described herein. (Note 28) The emotion recognition unit, When recognizing emotions, individualized responses are provided by referring to the elderly person's past emotional data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The emotion recognition unit, Adjusting the method for estimating the emotions of older adults and displaying the emotion recognition results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The emotion recognition unit, When recognizing emotions, we analyze changes in emotions by considering the living environment and daily events of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 31) The emotion recognition unit, This document proposes ways to facilitate communication between elderly individuals and their family and friends during the process of recognizing emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned sensor integration unit is The system estimates the emotions of elderly individuals and adjusts the frequency of sensor data collection based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned sensor integration unit is Data from multiple sensors is integrated to enable early detection of anomalies. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned sensor integration unit is The system estimates the emotions of elderly individuals and adjusts the display method of sensor data based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned sensor integration unit is When integrating sensors, we propose the optimal sensor placement according to the living environment of the elderly. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned predictive analysis unit, We estimate the emotions of older adults and adjust the results of predictive analysis based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned predictive analysis unit, We analyze the behavioral patterns of the elderly in detail and predict future risks. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned predictive analysis unit, This process involves estimating the emotions of older adults and prioritizing predictive analytics based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned predictive analysis unit, When performing predictive analysis, risk predictions should take into account the living environment of elderly people and seasonal changes. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A conversation unit equipped with conversational functions utilizing speech recognition and natural language processing, A notification unit equipped with an emergency notification system linked to an anomaly detection sensor, The Health Management Department provides health management support based on behavioral pattern analysis, It includes an emotion recognition unit that provides individualized support using emotion recognition technology. A system characterized by the following features.

2. It features a sensor integration unit that integrates with multiple sensors to perform comprehensive safety management. The system according to feature 1.

3. It includes a predictive analysis unit that provides preventative measures based on predictive analysis. The system according to feature 1.

4. The aforementioned conversation section is, Using speech recognition and natural language processing to engage in everyday conversations with the elderly. The system according to feature 1.

5. The aforementioned notification unit, It works in conjunction with an anomaly detection sensor to send an emergency notification when an anomaly is detected. The system according to feature 1.

6. The aforementioned health management department, Based on behavioral pattern analysis, we monitor the health status of older adults and provide health management advice. The system according to feature 1.

7. The emotion recognition unit, Using emotion recognition technology to understand the emotional state of elderly people and provide appropriate support. The system according to feature 1.

8. The aforementioned conversation section is, The system estimates the emotions of elderly individuals and selects conversation topics based on those estimated emotions. The system according to feature 1.

9. The aforementioned conversation section is, Analyzes the past conversation history of elderly people and provides optimal conversation content. The system according to feature 1.

Images