system

Abstract

The system according to this embodiment aims to provide comprehensive support for elderly people living alone. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a meal management unit, a medication management unit, a health condition management unit, a sleep management unit, a schedule management unit, and a conversation unit. The data collection unit collects lifestyle data of the elderly. The analysis unit analyzes the data collected by the data collection unit and provides appropriate support. The meal management unit manages meals based on the data analyzed by the analysis unit. The medication management unit manages medication based on the data analyzed by the analysis unit. The health condition management unit manages health based on the data analyzed by the analysis unit. The sleep management unit manages sleep based on the data analyzed by the analysis unit. The schedule management unit manages schedules based on the data analyzed by the analysis unit. The conversation unit engages in conversation to alleviate loneliness and engages in intellectual conversation based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, in the case of the elderly living alone, comprehensive life support has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively support the life of the elderly living alone.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a meal management unit, a medication management unit, a health management unit, a sleep management unit, a schedule management unit, and a conversation unit. The data collection unit collects lifestyle data of elderly individuals. The analysis unit analyzes the data collected by the data collection unit and provides appropriate support. The meal management unit manages meals based on the data analyzed by the analysis unit. The medication management unit manages medications based on the data analyzed by the analysis unit. The health management unit manages health based on the data analyzed by the analysis unit. The sleep management unit manages sleep based on the data analyzed by the analysis unit. The schedule management unit manages schedules based on the data analyzed by the analysis unit. The conversation unit engages in activities to alleviate loneliness and engages in intellectual conversation based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide comprehensive support for elderly people 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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires data indicating the user input. [[ID=X]]

[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) An elderly support system according to an embodiment of the present invention is a system that supports elderly people living alone by utilizing generative AI. This elderly support system supports the daily lives of elderly people using smart accessories equipped with generative AI and a smartphone. This enables alleviation of loneliness, intellectual conversation, meal management, medication management, health management, exercise promotion, sleep management, schedule management, and stress relief. For example, smart accessories equipped with generative AI support the lives of elderly people. For example, smart accessories such as necklaces and bolo ties have built-in cameras, microphones, speakers, GPS, accelerometers, ambient light sensors, hearing aids, activity trackers, heart rate monitors, calorie counters, sleep monitors, blood oxygen monitors, etc. This allows for detailed monitoring of the elderly person's living situation. Next, the generative AI analyzes the elderly person's living data and provides appropriate support. For example, in meal management, it analyzes the amount of calories and nutrients from images of meals and suggests the next menu to eat. In medication management, it determines the type of medication and intake status from images of medications and points out missed doses or excessive intake. Furthermore, in health management, it records and analyzes vital signs such as heart rate, calories burned, and steps taken, and provides health advice and encourages exercise. The sleep management feature records bedtime, sleep quality, and wake-up time, and provides advice on appropriate bedtimes and wake-up times. Furthermore, the generative AI analyzes the content of elderly people's conversations to support loneliness and intellectual conversation. For example, it learns from conversations with the user (parent) and family members and provides automated conversations optimized for the user. It also detects signs of mental illness such as dementia and depression from the conversation content and provides referrals to specialists and stress management advice as needed. Finally, the generative AI shares the elderly person's lifestyle data with their children and family doctor to monitor their mental and physical health. For example, data such as meals, medications, physical condition, exercise, sleep, behavior, conversation summaries, and current location are stored on a server and can be viewed by children and family doctors on smartphones and personal computers. This allows family members living far away to understand the elderly person's situation and provide appropriate support. In this way, the elderly support system can support elderly people living alone and monitor their mental and physical health.

[0029] The elderly support system according to this embodiment comprises a data collection unit, an analysis unit, a meal management unit, a medication management unit, a health management unit, a sleep management unit, a schedule management unit, and a conversation unit. The data collection unit collects lifestyle data of the elderly. The data collection unit includes, for example, a camera, microphone, speaker, GPS, accelerometer, ambient light sensor, hearing aid, activity tracker, heart rate monitor, calorie counter, sleep monitor, and blood oxygen meter. The data collection unit can use these sensors to monitor the elderly person's lifestyle in detail. For example, the camera captures images of the elderly person eating, and the microphone collects their conversations. The speaker provides voice advice to the elderly person. The GPS acquires the elderly person's location information, and the accelerometer measures the elderly person's activity level. The ambient light sensor measures the brightness around the elderly person, and the hearing aid supports the elderly person's hearing. The activity tracker measures the elderly person's exercise level, and the heart rate monitor measures the elderly person's heart rate. The calorie counter measures the elderly person's calorie expenditure, and the sleep monitor measures the elderly person's sleep status. The blood oxygen meter measures the elderly person's blood oxygen concentration. The analysis unit analyzes the data collected by the data collection unit and provides appropriate support. The analysis unit uses generative AI to analyze the data. For example, the generative AI analyzes videos of elderly people's meals to analyze the amount of calories and nutrients. The generative AI analyzes videos of elderly people's medications to determine the type of medication and how it is being taken. The generative AI analyzes vital signs such as heart rate, calories burned, and steps taken by elderly people to provide health advice and encourage exercise. The generative AI analyzes sleep data of elderly people to advise on appropriate bedtimes and wake-up times. The generative AI analyzes the content of elderly people's conversations to support alleviating loneliness and intellectual conversation. The meal management unit manages meals based on the data analyzed by the analysis unit. For example, the meal management unit analyzes the amount of calories and nutrients from meal videos and suggests the next menu to eat. The medication management unit manages medications based on the data analyzed by the analysis unit. For example, the medication management unit determines the type of medication and how it is being taken from medication videos and points out missed doses or excessive intake. The Health Management Department manages health based on data analyzed by the Analysis Department. For example, the Health Management Department records and analyzes vital signs such as heart rate, calories burned, and steps taken, and provides health advice and encourages exercise.The sleep management unit performs sleep management based on data analyzed by the analysis unit. For example, the sleep management unit records bedtime, sleep quality, and wake-up time, and provides advice on appropriate bedtime and wake-up time. The schedule management unit performs schedule management based on data analyzed by the analysis unit. For example, the schedule management unit manages the schedule of elderly people and provides reminders. The conversation unit engages in conversations to alleviate loneliness and intellectual conversation based on data analyzed by the analysis unit. For example, the conversation unit learns from conversations with the user and family, and provides automated conversations optimized for the user. As a result, the elderly support system according to this embodiment can support elderly people living alone and monitor their mental and physical health.

[0030] The data collection unit collects lifestyle data for the elderly. The unit includes, for example, a camera, microphone, speaker, GPS, accelerometer, ambient light sensor, hearing aid, activity tracker, heart rate monitor, calorie counter, sleep monitor, and blood oxygen meter. These sensors are designed to monitor the elderly's lifestyle in detail. The camera captures images of the elderly's meals and records the content and amount of food consumed. The microphone collects the elderly's conversations and stores them as audio data. The speaker provides voice advice to the elderly and conveys necessary information in real time. GPS obtains the elderly's location information and is used to ensure their safety when they are out. The accelerometer measures the elderly's activity level and records their daily exercise. The ambient light sensor measures the brightness around the elderly and collects data to provide an appropriate lighting environment. The hearing aid supports the elderly's hearing, making conversations and ambient sounds clearer. The activity tracker measures the elderly's exercise level and understands their daily activity level. The heart rate monitor measures the elderly's heart rate and monitors their health. The calorie counter measures the elderly's calorie expenditure and manages the balance of energy consumption. Sleep monitors measure the sleep patterns of elderly individuals, recording sleep quality and duration. Blood oxygen monitors measure blood oxygen levels and monitor respiratory status. This allows the data collection unit to gather a wide range of data from various devices, enabling a real-time understanding of the elderly individuals' living conditions. Furthermore, the data collection unit can centrally manage this data and integrate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis unit and other management departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit and provides appropriate support. The analysis unit uses generative AI to analyze the data. Generative AI uses advanced machine learning algorithms to process the collected data in real time and detect patterns and anomalies. For example, generative AI analyzes videos of elderly people eating to analyze the amount of calories and nutrients. This allows it to determine whether elderly people are eating a balanced diet and to suggest dietary improvements as needed. Generative AI also analyzes videos of elderly people taking medication to determine the type of medication and how it is being taken. This helps prevent missed doses and overdoses. Furthermore, generative AI analyzes vital signs such as heart rate, calories burned, and steps taken by elderly people and provides health advice and encourages exercise. For example, if the heart rate is abnormally high or calories burned are low, it will provide advice to encourage appropriate exercise. Generative AI analyzes elderly people's sleep data and advises on appropriate bedtimes and wake-up times. This helps elderly people ensure quality sleep and maintain their health. Generative AI also analyzes the content of elderly people's conversations to support alleviating loneliness and intellectual conversation. For example, if an elderly person is feeling lonely, the system can suggest appropriate conversation topics to facilitate communication. This allows the analytics unit to quickly and accurately analyze the collected data and provide appropriate support to the elderly. Furthermore, the analytics unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For instance, it can predict fluctuations in specific health risks based on past health data and formulate future countermeasures. The analytics unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This enables the analytics unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The Meal Management Department manages meals based on data analyzed by the Analysis Department. For example, the Meal Management Department analyzes the amount of calories and nutrients from meal videos and suggests the next menu to eat. Specifically, a generating AI analyzes meal videos and evaluates the balance of calories and nutrients consumed. This allows the department to determine whether elderly individuals are consuming a balanced diet and suggest dietary improvements as needed. For example, if there is a deficiency in vitamins or minerals, it will suggest ingredients and recipes to supplement them. The Meal Management Department can also provide individually optimized menus, taking into account the elderly individual's dietary preferences and allergy information. This allows elderly individuals to enjoy healthy and satisfying meals. Furthermore, the Meal Management Department manages the timing and quantity of meals and provides advice to prevent overeating and nutritional deficiencies. For example, if the interval between meals is too short or the amount consumed is too large, it will suggest appropriate meal timing and quantity. In this way, the Meal Management Department can maintain the health of elderly individuals and improve their quality of life.

[0033] The Medication Management Department manages medications based on data analyzed by the Analysis Department. For example, the Medication Management Department can determine the type of medication and its intake status from images of medications, and point out missed doses or overdoses. Specifically, a generating AI analyzes images of medications to identify the type and dosage. This allows the department to verify whether elderly individuals are taking their medications correctly and provide reminders as needed. For example, when it is time to take medication, a reminder is sent via voice or notification to prevent missed doses. In addition, by recording medication intake status and analyzing intake patterns based on past data, overdoses and underdoses can be detected early. This allows the Medication Management Department to provide support to elderly individuals in taking medications appropriately and maintaining their health. Furthermore, the Medication Management Department also provides information on drug side effects and interactions to ensure that elderly individuals can use medications safely. For example, it provides advice on points to be aware of when taking specific medications and interactions with other medications. In this way, the Medication Management Department can comprehensively support the management of medications for elderly individuals and minimize health risks.

[0034] The Health Management Department manages health based on data analyzed by the Analysis Department. For example, the Health Management Department records and analyzes vital signs such as heart rate, calories burned, and steps taken, and provides health advice and encourages exercise. Specifically, a generating AI analyzes collected vital sign data to assess the health status of elderly individuals. This allows for the provision of information useful for daily health management and preventive medicine. For example, if a person's heart rate is abnormally high or their calorie expenditure is low, the department provides advice to encourage appropriate exercise. Furthermore, based on step count data, it can evaluate daily activity levels and propose specific exercise plans to prevent inactivity. In addition, the Health Management Department analyzes health trends based on past data to support long-term health management. For example, based on past heart rate data, it can predict fluctuations in health risks over a specific period and formulate future countermeasures. This allows the Health Management Department to comprehensively manage the health of elderly individuals and improve their quality of life.

[0035] The Sleep Management Department manages sleep based on data analyzed by the Analysis Department. For example, the Sleep Management Department records bedtime, sleep quality, and wake-up time, and provides advice on appropriate bedtimes and wake-up times. Specifically, a generating AI analyzes the collected sleep data and evaluates the sleep patterns of elderly individuals. This allows for the provision of specific advice to ensure high-quality sleep. For example, if bedtime is too late or sleep quality is poor, it suggests appropriate bedtimes and relaxation methods. It can also adjust wake-up times to avoid impacting daytime activities. Furthermore, the Sleep Management Department analyzes sleep trends based on past data to support long-term sleep management. For example, based on past sleep data, it can predict fluctuations in sleep quality over a specific period and plan future countermeasures. In this way, the Sleep Management Department can comprehensively manage the sleep of elderly individuals and improve their quality of life.

[0036] The Schedule Management Department manages schedules based on data analyzed by the Analysis Department. For example, the Schedule Management Department manages the schedules of elderly individuals and provides reminders. Specifically, a generation AI analyzes the elderly person's schedule data and reminds them of important appointments and events. This allows elderly individuals to live their daily lives smoothly without forgetting appointments. For example, it reminds them of medical appointments, medication times, and family appointments, and sends notifications at the appropriate time. The Schedule Management Department also optimizes the elderly person's schedule and provides advice to avoid excessive burden. For example, if there are too many consecutive appointments, it suggests appropriate rest periods and creates a balanced schedule. In this way, the Schedule Management Department can comprehensively support the lives of elderly individuals and improve their quality of life.

[0037] The conversation unit engages in intellectually stimulating conversations and alleviates loneliness based on data analyzed by the analysis unit. For example, the conversation unit learns from conversations with the user and their family, and then provides automated conversations optimized for the user. Specifically, the generative AI analyzes conversation data from the elderly and generates appropriate conversation topics and responses. This allows the elderly to enjoy daily communication without feeling lonely. For example, it suggests conversations based on the elderly person's hobbies and interests, providing intellectual stimulation. The conversation unit can also analyze the elderly person's emotional state and provide appropriate support. For example, if the elderly person is feeling stressed, it will suggest ways to relax or change their mood. Furthermore, the conversation unit supports communication with family members and facilitates contact with family members who live far away. In this way, the conversation unit can reduce feelings of loneliness in the elderly and provide support to maintain their mental health.

[0038] The data collection unit includes a camera, microphone, speaker, GPS, accelerometer, ambient light sensor, hearing aid, activity tracker, heart rate monitor, calorie counter, sleep monitor, and blood oxygen meter. The data collection unit can, for example, use the camera to capture images of an elderly person eating. The data collection unit can, for example, use the microphone to collect conversations of an elderly person. The data collection unit can, for example, use the speaker to provide voice advice to an elderly person. The data collection unit can, for example, use GPS to acquire the elderly person's location information. The data collection unit can, for example, use the accelerometer to measure the elderly person's activity level. The data collection unit can, for example, use the ambient light sensor to measure the brightness around an elderly person. The data collection unit can, for example, use a hearing aid to support the elderly person's hearing. The data collection unit can, for example, use the activity tracker to measure the elderly person's exercise level. The data collection unit can, for example, use a heart rate monitor to measure the elderly person's heart rate. The data collection unit can, for example, use a calorie counter to measure the elderly person's calorie consumption. The data collection unit can, for example, use a sleep monitor to measure the elderly person's sleep status. The data collection unit can, for example, measure the blood oxygen concentration of elderly individuals using a blood oxygen meter. By equipping the data collection unit with a variety of sensors, it can collect detailed lifestyle data of elderly individuals. Some or all of the above-described processes in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input video data captured by a camera into a generating AI and have the generating AI perform the process of extracting necessary information from the video data.

[0039] The meal management department can analyze the amount of calories and nutrients from a video of a meal and suggest the next menu item to eat. For example, the meal management department can analyze a video of a meal and calculate the amount of calories and nutrients. For example, the meal management department can consider the balance of calories and nutrients in order to suggest the next menu item to eat. For example, the meal management department can analyze a video of a meal and identify the types and quantities of ingredients. For example, the meal management department can analyze a video of a meal and calculate the amount of food consumed. As a result, the meal management department can suggest an appropriate menu by analyzing a video of a meal. Some or all of the above processes in the meal management department may be performed using AI, for example, or without AI. For example, the meal management department can input video data of a meal into a generating AI and have the generating AI perform the process of calculating the amount of calories and nutrients.

[0040] The drug management department can determine the type of medication and the dosage status from the video of the medication, and can point out missed doses or excessive doses. For example, the drug management department can analyze the video of the medication to identify the type of medication. For example, the drug management department can analyze the video of the medication to determine the dosage status. For example, the drug management department can analyze the video of the medication to point out missed doses. For example, the drug management department can analyze the video of the medication to point out excessive doses. In this way, the drug management department can appropriately manage medication dosage by analyzing the video of the medication. Some or all of the above processing in the drug management department may be performed using AI, for example, or without AI. For example, the drug management department can input video data of the medication into a generating AI and have the generating AI perform the processing to determine the type of medication and the dosage status.

[0041] The health management department can record and analyze vital signs such as heart rate, calories burned, and steps taken, and provide health advice and encourage exercise. For example, the health management department can record heart rate and analyze fluctuations in heart rate. For example, the health management department can record calories burned and analyze fluctuations in calories burned. For example, the health management department can record steps taken and analyze fluctuations in steps taken. For example, the health management department can provide health advice based on data such as heart rate, calories burned, and steps taken. For example, the health management department can encourage exercise based on data such as heart rate, calories burned, and steps taken. In this way, the health management department can provide health advice and encourage exercise by recording and analyzing vital signs. Some or all of the above processing in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input data on heart rate, calories burned, and steps taken into a generating AI and have the generating AI perform the processing of providing health advice and encouraging exercise.

[0042] The sleep management unit can record bedtime, sleep quality, and wake-up time, and provide advice on appropriate bedtime and wake-up time. For example, the sleep management unit can record bedtime and analyze fluctuations in bedtime. For example, the sleep management unit can record sleep quality and analyze fluctuations in sleep quality. For example, the sleep management unit can record wake-up time and analyze fluctuations in wake-up time. For example, the sleep management unit can provide advice on appropriate bedtime based on data on bedtime, sleep quality, and wake-up time. For example, the sleep management unit can provide advice on appropriate wake-up time based on data on bedtime, sleep quality, and wake-up time. In this way, the sleep management unit can provide appropriate sleep advice by recording and analyzing sleep data. Some or all of the above processing in the sleep management unit may be performed using AI, for example, or without AI. For example, the sleep management unit can input data on bedtime, sleep quality, and wake-up time into a generating AI and have the generating AI perform the process of providing advice on appropriate bedtime and wake-up time.

[0043] The conversation unit can learn from conversations with the user and their family and provide automated conversations optimized for the user. For example, the conversation unit can record conversations with the user and their family and learn from their content. For example, the conversation unit can analyze patterns of conversations with the user and their family and generate automated conversations optimized for the user. For example, the conversation unit can provide appropriate topics based on the user's interests and concerns. For example, the conversation unit can refer to the user's past conversation history and provide relevant topics. In this way, the conversation unit can provide conversations optimized for the user by learning from conversations. Some or all of the above processes in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input conversation data with the user and their family into a generating AI and have the generating AI perform the process of generating optimized automated conversations.

[0044] The data collection unit can analyze the elderly person's past behavioral patterns and select the optimal timing for data collection. For example, if the elderly person has a habit of taking a walk every morning, the data collection unit can collect data during the walk. For example, the data collection unit can collect data during the time when the elderly person is relaxing at night. For example, the data collection unit can collect data when the elderly person regularly visits a medical institution. In this way, the data collection unit can collect data at the optimal timing by analyzing past behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's past behavioral data into a generating AI and have the generating AI perform the process of selecting the optimal timing for data collection.

[0045] The data collection unit can automatically adjust the sensitivity of sensors in response to environmental changes during data collection. For example, the data collection unit can adjust the camera sensitivity if the brightness of the lighting changes. For example, the data collection unit can adjust the microphone sensitivity if the ambient noise level changes. For example, the data collection unit can adjust the sensitivity of the acceleration sensor if the activity level of an elderly person increases. In this way, the data collection unit can collect accurate data by adjusting the sensitivity of sensors in response to environmental changes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental change data into a generating AI and have the generating AI execute the process of automatically adjusting the sensitivity of the sensors.

[0046] The data collection unit can adjust the timing of data collection by taking family schedules into consideration. For example, the data collection unit can refrain from collecting data during times when family members are visiting. For example, the data collection unit can collect data during times when family members are out. For example, the data collection unit can refrain from collecting data during times when family members are spending time together. In this way, the data collection unit can optimize the timing of data collection by taking family schedules into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input family schedule data into a generating AI and have the generating AI perform the processing to adjust the timing of data collection.

[0047] The data collection unit can optimize the data collection content by referring to local weather information during data collection. For example, the data collection unit can prioritize collecting indoor activity data during rainy weather. For example, the data collection unit can prioritize collecting outdoor activity data during sunny weather. For example, the data collection unit can prioritize collecting body temperature and water intake data when the temperature is high. In this way, the data collection unit can optimize the data collection content by referring to local weather information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input local weather information data into a generating AI and have the generating AI perform the processing to optimize the data collection content.

[0048] The analysis unit can detect outliers by comparing current data with past data during analysis. For example, the analysis unit can detect an outlier if the heart rate of an elderly person is abnormally high compared to past data. For example, the analysis unit can detect an outlier if the number of steps taken by an elderly person is abnormally low compared to past data. For example, the analysis unit can detect an outlier if the sleep duration of an elderly person is abnormally short compared to past data. In this way, the analysis unit can accurately detect outliers by comparing them with past data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the process of detecting outliers.

[0049] The analysis unit can apply different analysis methods depending on the type of data during analysis. For example, the analysis unit can apply time series analysis to heart rate data. For example, the analysis unit can apply statistical analysis to step count data. For example, the analysis unit can apply pattern recognition to sleep data. By applying analysis methods appropriate to the type of data, the analysis unit can improve the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI execute an analysis method appropriate to the type of data.

[0050] The analysis unit can improve the accuracy of its analysis by incorporating feedback from the family during the analysis process. For example, the analysis unit can adjust its analysis algorithm based on feedback from the family. For example, the analysis unit can improve how it displays the analysis results by incorporating feedback from the family. For example, the analysis unit can review its data collection methods based on feedback from the family. As a result, the analysis unit improves its accuracy by incorporating feedback from the family. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input family feedback data into a generating AI and have the generating AI perform the process of adjusting the analysis algorithm.

[0051] The analysis unit can supplement its analysis results by referring to local medical data during the analysis process. For example, the analysis unit can supplement its analysis results based on local medical data. For example, the analysis unit can improve the accuracy of detecting outliers by referring to local medical data. For example, the analysis unit can provide health advice based on local medical data. In this way, the analysis unit can supplement its analysis results by referring to local medical data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input local medical data into a generating AI and have the generating AI perform the process of supplementing the analysis results.

[0052] The meal management department can optimize nutritional balance by referring to past meal history during meal management. For example, the meal management department can suggest a nutritionally balanced menu based on the past meal history of an elderly person. For example, the meal management department can suggest a menu that supplements a specific nutrient if it is deficient by referring to the past meal history of an elderly person. For example, the meal management department can suggest a menu that increases the variety of meals based on the past meal history of an elderly person. In this way, the meal management department can provide nutritionally balanced meals by referring to past meal history. Some or all of the above processes in the meal management department may be performed using AI, for example, or without AI. For example, the meal management department can input past meal history data into a generating AI and have the generating AI perform the process of optimizing nutritional balance.

[0053] The meal management department can propose menus that take seasonal ingredients into consideration when managing meals. For example, the meal management department can propose menus that use seasonal ingredients. For example, the meal management department can propose nutritionally balanced menus that adapt to seasonal changes. For example, the meal management department can propose menus that increase meal variety by using seasonal ingredients. In this way, the meal management department can increase meal variety and improve nutritional balance by taking seasonal ingredients into consideration. Some or all of the above processes in the meal management department may be performed using AI, for example, or without using AI. For example, the meal management department can input seasonal ingredient data into a generating AI and have the generating AI perform the process of proposing menus.

[0054] The meal management unit can suggest menus while considering the family's meal schedule. For example, the meal management unit can suggest menus that match the time when the family eats together. For example, the meal management unit can suggest nutritionally balanced menus while considering the family's meal schedule. For example, the meal management unit can suggest menus that increase the variety of meals according to the family's meal schedule. In this way, the meal management unit can enjoy meals with the family by considering the family's meal schedule. Some or all of the above processes in the meal management unit may be performed using AI, for example, or without AI. For example, the meal management unit can input family meal schedule data into a generating AI and have the generating AI perform the process of suggesting menus.

[0055] The meal management department can optimize menus by referring to local food supply information during meal management. For example, the meal management department can propose nutritionally balanced menus based on local food supply information. For example, the meal management department can propose menus using seasonal ingredients by referring to local food supply information. For example, the meal management department can propose menus that increase the variety of meals based on local food supply information. In this way, by referring to local food supply information, the meal management department can reduce food waste and provide nutritionally balanced meals. Some or all of the above processes in the meal management department may be performed using AI, for example, or without AI. For example, the meal management department can input local food supply information data into a generating AI and have the generating AI execute the process of optimizing menus.

[0056] The medication management department can suggest the optimal dosage method by referring to past medication intake history during medication management. For example, the medication management department can suggest the optimal dosage method based on the past medication intake history of elderly individuals. For example, the medication management department can suggest methods to prevent missed doses by referring to the past medication intake history of elderly individuals. For example, the medication management department can suggest methods to prevent overdosing by referring to past medication intake history of elderly individuals. In this way, the medication management department can prevent missed doses and overdoses by referring to past medication intake history. Some or all of the above processes in the medication management department may be performed using AI, for example, or without AI. For example, the medication management department can input past medication intake history data into a generating AI and have the generating AI perform the process of suggesting the optimal dosage method.

[0057] The drug management unit can apply different management methods depending on the type of drug during drug management. For example, the drug management unit can set reminders to prevent missed doses for tablet drugs. For example, the drug management unit can suggest appropriate measurement methods for liquid drugs. For example, the drug management unit can suggest appropriate application methods for topical drugs. In this way, the drug management unit improves the accuracy of drug management by applying management methods according to the type of drug. Some or all of the above processes in the drug management unit may be performed using AI, for example, or without AI. For example, the drug management unit can input drug type data into a generating AI and have the generating AI execute the process of applying management methods.

[0058] The medication management department can improve the accuracy of medication management by incorporating feedback from family members. For example, the medication management department can adjust medication reminders based on feedback from family members. For example, the medication management department can improve medication management methods by incorporating family opinions. For example, the medication management department can review the timing of medication intake based on feedback from family members. In this way, the medication management department can improve the accuracy of medication management by incorporating feedback from family members. Some or all of the above processes in the medication management department may be performed using AI, for example, or not using AI. For example, the medication management department can input family feedback data into a generating AI and have the generating AI execute processes to improve the accuracy of management.

[0059] The drug management department can supplement its drug management methods by referring to local medical data during drug management. For example, the drug management department can supplement drug intake methods based on local medical data. For example, the drug management department can suggest methods to prevent drug side effects by referring to local medical data. For example, the drug management department can suggest methods to prevent drug interactions based on local medical data. In this way, the drug management department can supplement its drug management methods and improve accuracy by referring to local medical data. Some or all of the above processes in the drug management department may be performed using AI, for example, or without AI. For example, the drug management department can input local medical data into a generating AI and have the generating AI perform the processing to supplement the management methods.

[0060] The health management department can provide health advice by referring to past vital data during health management. For example, the health management department can suggest appropriate exercise based on the past heart rate data of an elderly person. For example, the health management department can suggest appropriate meals by referring to the past calorie expenditure data of an elderly person. For example, the health management department can suggest an appropriate amount of exercise based on the past step count data of an elderly person. In this way, the health management department can provide more appropriate health advice by referring to past vital data. Some or all of the above processing in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input past vital data into a generating AI and have the generating AI perform the process of providing health advice.

[0061] The health management department can adjust advice when managing health, taking into account seasonal changes. For example, the health management department can suggest appropriate exercise according to seasonal changes. For example, the health management department can suggest appropriate meals considering seasonal changes. For example, the health management department can suggest appropriate sleep duration according to seasonal changes. In this way, the health management department can provide more appropriate health advice by taking seasonal changes into account. Some or all of the above processes in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input seasonal change data into a generating AI and have the generating AI perform the process of adjusting the advice.

[0062] The health management unit can provide advice by referring to family health data during health management. For example, the health management unit can suggest appropriate exercise based on family health data. For example, the health management unit can suggest appropriate meals based on family health data. For example, the health management unit can suggest appropriate sleep duration based on family health data. In this way, the health management unit can provide more appropriate health advice by referring to family health data. Some or all of the above processes in the health management unit may be performed using AI, for example, or without AI. For example, the health management unit can input family health data into a generating AI and have the generating AI perform the process of providing advice.

[0063] The health management department can supplement its advice by referring to local health information when managing health. For example, the health management department can suggest appropriate exercise based on local health information. For example, the health management department can suggest appropriate meals based on local health information. For example, the health management department can suggest appropriate sleep duration based on local health information. In this way, the health management department can provide more appropriate health advice by referring to local health information. Some or all of the above processes in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input local health information data into a generating AI and have the generating AI perform the process of supplementing the advice.

[0064] The sleep management unit can suggest the optimal bedtime by referring to past sleep data during sleep management. For example, the sleep management unit can suggest the optimal bedtime based on the past sleep data of an elderly person. For example, the sleep management unit can suggest the optimal wake-up time by referring to the past sleep data of an elderly person. For example, the sleep management unit can suggest the optimal sleep environment based on the past sleep data of an elderly person. In this way, the sleep management unit can suggest a more appropriate bedtime by referring to past sleep data. Some or all of the above processes in the sleep management unit may be performed using AI, for example, or without using AI. For example, the sleep management unit can input past sleep data into a generating AI and have the generating AI perform the process of suggesting the optimal bedtime.

[0065] The sleep management unit can provide advice during sleep management, taking into account changes in ambient noise and light. For example, if the ambient noise is loud, the sleep management unit can provide advice on creating a quieter environment. For example, if the light changes drastically, the sleep management unit can suggest appropriate lighting. For example, the sleep management unit can suggest an optimal sleep environment by taking into account changes in ambient noise and light. In this way, the sleep management unit can provide a more appropriate sleep environment by taking into account changes in ambient noise and light. Some or all of the above processing in the sleep management unit may be performed using AI, for example, or without using AI. For example, the sleep management unit can input ambient noise and light change data into a generating AI and have the generating AI perform the process of providing advice.

[0066] The sleep management unit can provide advice by referring to family sleep data during sleep management. For example, the sleep management unit can suggest an appropriate bedtime based on family sleep data. For example, the sleep management unit can suggest an appropriate wake-up time based on family sleep data. For example, the sleep management unit can suggest an appropriate sleep environment based on family sleep data. In this way, the sleep management unit can provide more appropriate sleep advice by referring to family sleep data. Some or all of the above processes in the sleep management unit may be performed using AI, for example, or without AI. For example, the sleep management unit can input family sleep data into a generating AI and have the generating AI perform the process of providing advice.

[0067] The sleep management unit can supplement its advice by referring to local weather information during sleep management. For example, the sleep management unit can suggest an appropriate bedtime based on local weather information. For example, the sleep management unit can suggest an appropriate wake-up time based on local weather information. For example, the sleep management unit can suggest an appropriate sleep environment based on local weather information. In this way, the sleep management unit can provide more appropriate sleep advice by referring to local weather information. Some or all of the above processes in the sleep management unit may be performed using AI, for example, or without AI. For example, the sleep management unit can input local weather data into a generating AI and have the generating AI perform the process of supplementing the advice.

[0068] The schedule management unit can propose an optimal schedule by referring to past schedule history when managing schedules. For example, the schedule management unit can propose an optimal schedule based on the past schedule history of an elderly person. For example, the schedule management unit can prioritize a specific appointment by referring to the past schedule history of an elderly person. For example, the schedule management unit can increase the variety of appointments based on the past schedule history of an elderly person. As a result, the schedule management unit can propose a more appropriate schedule by referring to past schedule history. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input past schedule history data into a generating AI and have the generating AI execute the process of proposing an optimal schedule.

[0069] The schedule management unit can adjust schedules while considering seasonal events. For example, the schedule management unit can propose an optimal schedule by considering seasonal events. For example, the schedule management unit can increase the variety of appointments to match seasonal events. For example, the schedule management unit can prioritize specific appointments based on seasonal events. In this way, the schedule management unit can provide a more appropriate schedule by considering seasonal events. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input seasonal event data into a generating AI and have the generating AI perform the process of adjusting the schedule.

[0070] The schedule management unit can suggest schedules by referring to family schedules during schedule management. For example, the schedule management unit can adjust the schedules of elderly people based on family schedules. For example, the schedule management unit can prioritize time spent with family by referring to family schedules. For example, the schedule management unit can increase the variety of schedules based on family schedules. This allows the schedule management unit to prioritize time spent with family by referring to family schedules. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input family schedule data into a generating AI and have the generating AI perform the process of suggesting schedules.

[0071] The schedule management unit can supplement schedules by referring to local event information during schedule management. For example, the schedule management unit can propose an optimal schedule based on local event information. For example, the schedule management unit can increase the variety of schedules by referring to local event information. For example, the schedule management unit can prioritize specific schedules based on local event information. In this way, the schedule management unit can provide a more appropriate schedule by referring to local event information. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input local event information data into a generating AI and have the generating AI execute the process of supplementing schedules.

[0072] The conversation unit can provide optimal conversation content by referring to past conversation history. For example, the conversation unit can provide topics of interest based on the past conversation history of an elderly person. For example, the conversation unit can prioritize specific topics by referring to the past conversation history of an elderly person. For example, the conversation unit can increase the variety of conversations based on the past conversation history of an elderly person. In this way, the conversation unit can provide more appropriate conversation content by referring to past conversation history. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input past conversation history data into a generating AI and have the generating AI perform the processing to provide optimal conversation content.

[0073] The conversation unit can revitalize conversations by incorporating seasonal topics. The conversation unit can, for example, revitalize conversations by incorporating seasonal topics. The conversation unit can, for example, provide interesting topics in accordance with seasonal changes. The conversation unit can, for example, increase the variety of conversations based on seasonal topics. Thus, the conversation unit can increase the variety of conversations and revitalize conversations by incorporating seasonal topics. Some or all of the above-described processes in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input seasonal topic data into a generating AI and have the generating AI perform processes to revitalize conversations.

[0074] The conversation unit can provide conversation content by referring to the family's conversation history. For example, the conversation unit can provide topics of interest based on the family's conversation history. For example, the conversation unit can prioritize specific topics by referring to the family's conversation history. For example, the conversation unit can increase the variety of conversations based on the family's conversation history. This allows the conversation unit to provide more appropriate conversation content by referring to the family's conversation history. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input family conversation history data into a generating AI and have the generating AI perform the processing of providing conversation content.

[0075] The conversational unit can supplement the conversation content by referring to local news information. For example, the conversational unit can provide interesting topics based on local news information. For example, the conversational unit can prioritize specific topics by referring to local news information. For example, the conversational unit can increase the variety of conversations based on local news information. As a result, the conversational unit can provide more appropriate conversation content by referring to local news information. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input local news information data into a generating AI and have the generating AI perform the processing of supplementing the conversation content.

[0076] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0077] The elderly support system can also be equipped with an emergency notification unit. This unit monitors the elderly person's lifestyle data in real time and can quickly notify family members or medical institutions if an abnormality is detected. For example, if the heart rate suddenly increases, the emergency notification unit can immediately notify family members and arrange for an ambulance if necessary. Also, if a fall is detected, the emergency notification unit can send the elderly person's location information to family members or medical institutions to encourage a quick response. Furthermore, if medication is forgotten or taken in excess, the emergency notification unit can also notify family members and encourage appropriate action. In this way, the emergency notification unit ensures the safety of the elderly person and enables a quick response.

[0078] The elderly support system can also be equipped with a reminder function. This reminder function has the ability to remind elderly individuals of important tasks in their daily lives. For example, when it's time to take medication, the reminder function can notify the elderly person with voice or vibration. Similarly, when a scheduled medical appointment is approaching, the reminder function can notify the elderly person and encourage them to attend. Furthermore, the reminder function can support the elderly person's health management by reminding them of meal times and exercise times. In this way, the reminder function helps elderly individuals remember and complete important tasks.

[0079] The senior support system can also include an entertainment section. This section provides entertainment to enhance the lives of seniors. For example, it can offer relaxation and enjoyment by playing music or audiobooks. It can also stimulate cognitive functions by providing intellectual games such as puzzles and quizzes. Furthermore, it can support video calls with family and friends, helping seniors maintain social connections. In this way, the entertainment section can provide enjoyment and stimulation to the lives of seniors, reducing feelings of loneliness.

[0080] The senior support system can also include a learning support section. This section has functions to support seniors in learning new knowledge and skills. For example, it can provide online courses and webinars, allowing seniors to learn about areas of interest. It can also provide language learning apps to support seniors in learning new languages. Furthermore, it can create reading lists and study plans to help seniors continue their learning. In this way, the learning support section can satisfy seniors' intellectual curiosity and promote their self-growth.

[0081] The senior support system can also include a community liaison department. This department has functions to support seniors in maintaining connections with their local community. For example, it can provide information on local events and activities, increasing opportunities for seniors to participate. It can also introduce volunteer activities and hobby groups, supporting seniors in making new friends. Furthermore, it can provide information on local support services and medical institutions, ensuring seniors receive the support they need. In this way, the community liaison department helps seniors maintain connections with their community and prevent isolation.

[0082] The following briefly describes the processing flow for example form 1.

[0083] Step 1: The data collection unit collects lifestyle data from the elderly person. The data collection unit includes, for example, a camera, microphone, speaker, GPS, accelerometer, ambient light sensor, hearing aid, activity tracker, heart rate monitor, calorie counter, sleep monitor, and blood oxygen meter. These sensors are used to monitor the elderly person's lifestyle in detail. For example, the camera captures images of the elderly person eating, and the microphone collects their conversations. The speaker provides voice advice to the elderly person. The GPS obtains the elderly person's location information, and the accelerometer measures their activity level. The ambient light sensor measures the brightness around the elderly person, and the hearing aid supports their hearing. The activity tracker measures the elderly person's exercise level, and the heart rate monitor measures their heart rate. The calorie counter measures the elderly person's calorie expenditure, and the sleep monitor measures their sleep status. The blood oxygen meter measures the elderly person's blood oxygen concentration. Step 2: The analysis unit analyzes the data collected by the collection unit and provides appropriate support. The analysis unit uses generative AI to analyze the data. For example, the generative AI analyzes videos of elderly people eating to analyze the amount of calories and nutrients. The generative AI analyzes videos of elderly people taking medication to determine the type of medication and how it is being taken. The generative AI analyzes vital signs such as heart rate, calories burned, and steps taken by elderly people to provide health advice and encourage exercise. The generative AI analyzes sleep data of elderly people to advise on appropriate bedtimes and wake-up times. The generative AI analyzes the content of elderly people's conversations to support alleviating loneliness and facilitating intellectual conversation. Step 3: The dietary management department manages meals based on the data analyzed by the analysis department. For example, the dietary management department analyzes the amount of calories and nutrients from the video of the meal and suggests the next menu to eat. Step 4: The medication management department manages medications based on the data analyzed by the analysis department. For example, the medication management department can determine the type of medication and the dosage status from images of the medication and point out missed doses or excessive intake. Step 5: The Health Management Department manages health based on the data analyzed by the Analysis Department. For example, the Health Management Department records and analyzes vital signs such as heart rate, calories burned, and steps taken, and provides health advice and encourages exercise. Step 6: The sleep management department manages sleep based on the data analyzed by the analysis department. For example, the sleep management department records bedtime, sleep quality, and wake-up time, and provides advice on appropriate bedtime and wake-up times. Step 7: The schedule management unit manages schedules based on the data analyzed by the analysis unit. For example, the schedule management unit manages the schedules of elderly people and provides reminders. Step 8: The conversation unit engages in conversational and intellectually stimulating discussions based on the data analyzed by the analysis unit. For example, the conversation unit learns from conversations with the user and their family, and then engages in automated conversations optimized for the user.

[0084] (Example of form 2) An elderly support system according to an embodiment of the present invention is a system that supports elderly people living alone by utilizing generative AI. This elderly support system supports the daily lives of elderly people using smart accessories equipped with generative AI and a smartphone. This enables alleviation of loneliness, intellectual conversation, meal management, medication management, health management, exercise promotion, sleep management, schedule management, and stress relief. For example, smart accessories equipped with generative AI support the lives of elderly people. For example, smart accessories such as necklaces and bolo ties have built-in cameras, microphones, speakers, GPS, accelerometers, ambient light sensors, hearing aids, activity trackers, heart rate monitors, calorie counters, sleep monitors, blood oxygen monitors, etc. This allows for detailed monitoring of the elderly person's living situation. Next, the generative AI analyzes the elderly person's living data and provides appropriate support. For example, in meal management, it analyzes the amount of calories and nutrients from images of meals and suggests the next menu to eat. In medication management, it determines the type of medication and intake status from images of medications and points out missed doses or excessive intake. Furthermore, in health management, it records and analyzes vital signs such as heart rate, calories burned, and steps taken, and provides health advice and encourages exercise. The sleep management feature records bedtime, sleep quality, and wake-up time, and provides advice on appropriate bedtimes and wake-up times. Furthermore, the generative AI analyzes the content of elderly people's conversations to support loneliness and intellectual conversation. For example, it learns from conversations with the user (parent) and family members and provides automated conversations optimized for the user. It also detects signs of mental illness such as dementia and depression from the conversation content and provides referrals to specialists and stress management advice as needed. Finally, the generative AI shares the elderly person's lifestyle data with their children and family doctor to monitor their mental and physical health. For example, data such as meals, medications, physical condition, exercise, sleep, behavior, conversation summaries, and current location are stored on a server and can be viewed by children and family doctors on smartphones and personal computers. This allows family members living far away to understand the elderly person's situation and provide appropriate support. In this way, the elderly support system can support elderly people living alone and monitor their mental and physical health.

[0085] The elderly support system according to this embodiment comprises a data collection unit, an analysis unit, a meal management unit, a medication management unit, a health management unit, a sleep management unit, a schedule management unit, and a conversation unit. The data collection unit collects lifestyle data of the elderly. The data collection unit includes, for example, a camera, microphone, speaker, GPS, accelerometer, ambient light sensor, hearing aid, activity tracker, heart rate monitor, calorie counter, sleep monitor, and blood oxygen meter. The data collection unit can use these sensors to monitor the elderly person's lifestyle in detail. For example, the camera captures images of the elderly person eating, and the microphone collects their conversations. The speaker provides voice advice to the elderly person. The GPS acquires the elderly person's location information, and the accelerometer measures the elderly person's activity level. The ambient light sensor measures the brightness around the elderly person, and the hearing aid supports the elderly person's hearing. The activity tracker measures the elderly person's exercise level, and the heart rate monitor measures the elderly person's heart rate. The calorie counter measures the elderly person's calorie expenditure, and the sleep monitor measures the elderly person's sleep status. The blood oxygen meter measures the elderly person's blood oxygen concentration. The analysis unit analyzes the data collected by the data collection unit and provides appropriate support. The analysis unit uses generative AI to analyze the data. For example, the generative AI analyzes videos of elderly people's meals to analyze the amount of calories and nutrients. The generative AI analyzes videos of elderly people's medications to determine the type of medication and how it is being taken. The generative AI analyzes vital signs such as heart rate, calories burned, and steps taken by elderly people to provide health advice and encourage exercise. The generative AI analyzes sleep data of elderly people to advise on appropriate bedtimes and wake-up times. The generative AI analyzes the content of elderly people's conversations to support alleviating loneliness and intellectual conversation. The meal management unit manages meals based on the data analyzed by the analysis unit. For example, the meal management unit analyzes the amount of calories and nutrients from meal videos and suggests the next menu to eat. The medication management unit manages medications based on the data analyzed by the analysis unit. For example, the medication management unit determines the type of medication and how it is being taken from medication videos and points out missed doses or excessive intake. The Health Management Department manages health based on data analyzed by the Analysis Department. For example, the Health Management Department records and analyzes vital signs such as heart rate, calories burned, and steps taken, and provides health advice and encourages exercise.The sleep management unit performs sleep management based on data analyzed by the analysis unit. For example, the sleep management unit records bedtime, sleep quality, and wake-up time, and provides advice on appropriate bedtime and wake-up time. The schedule management unit performs schedule management based on data analyzed by the analysis unit. For example, the schedule management unit manages the schedule of elderly people and provides reminders. The conversation unit engages in conversations to alleviate loneliness and intellectual conversation based on data analyzed by the analysis unit. For example, the conversation unit learns from conversations with the user and family, and provides automated conversations optimized for the user. As a result, the elderly support system according to this embodiment can support elderly people living alone and monitor their mental and physical health.

[0086] The data collection unit collects lifestyle data for the elderly. The unit includes, for example, a camera, microphone, speaker, GPS, accelerometer, ambient light sensor, hearing aid, activity tracker, heart rate monitor, calorie counter, sleep monitor, and blood oxygen meter. These sensors are designed to monitor the elderly's lifestyle in detail. The camera captures images of the elderly's meals and records the content and amount of food consumed. The microphone collects the elderly's conversations and stores them as audio data. The speaker provides voice advice to the elderly and conveys necessary information in real time. GPS obtains the elderly's location information and is used to ensure their safety when they are out. The accelerometer measures the elderly's activity level and records their daily exercise. The ambient light sensor measures the brightness around the elderly and collects data to provide an appropriate lighting environment. The hearing aid supports the elderly's hearing, making conversations and ambient sounds clearer. The activity tracker measures the elderly's exercise level and understands their daily activity level. The heart rate monitor measures the elderly's heart rate and monitors their health. The calorie counter measures the elderly's calorie expenditure and manages the balance of energy consumption. Sleep monitors measure the sleep patterns of elderly individuals, recording sleep quality and duration. Blood oxygen monitors measure blood oxygen levels and monitor respiratory status. This allows the data collection unit to gather a wide range of data from various devices, enabling a real-time understanding of the elderly individuals' living conditions. Furthermore, the data collection unit can centrally manage this data and integrate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis unit and other management departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0087] The analysis unit analyzes the data collected by the collection unit and provides appropriate support. The analysis unit uses generative AI to analyze the data. Generative AI uses advanced machine learning algorithms to process the collected data in real time and detect patterns and anomalies. For example, generative AI analyzes videos of elderly people eating to analyze the amount of calories and nutrients. This allows it to determine whether elderly people are eating a balanced diet and to suggest dietary improvements as needed. Generative AI also analyzes videos of elderly people taking medication to determine the type of medication and how it is being taken. This helps prevent missed doses and overdoses. Furthermore, generative AI analyzes vital signs such as heart rate, calories burned, and steps taken by elderly people and provides health advice and encourages exercise. For example, if the heart rate is abnormally high or calories burned are low, it will provide advice to encourage appropriate exercise. Generative AI analyzes elderly people's sleep data and advises on appropriate bedtimes and wake-up times. This helps elderly people ensure quality sleep and maintain their health. Generative AI also analyzes the content of elderly people's conversations to support alleviating loneliness and intellectual conversation. For example, if an elderly person is feeling lonely, the system can suggest appropriate conversation topics to facilitate communication. This allows the analytics unit to quickly and accurately analyze the collected data and provide appropriate support to the elderly. Furthermore, the analytics unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For instance, it can predict fluctuations in specific health risks based on past health data and formulate future countermeasures. The analytics unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This enables the analytics unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0088] The Meal Management Department manages meals based on data analyzed by the Analysis Department. For example, the Meal Management Department analyzes the amount of calories and nutrients from meal videos and suggests the next menu to eat. Specifically, a generating AI analyzes meal videos and evaluates the balance of calories and nutrients consumed. This allows the department to determine whether elderly individuals are consuming a balanced diet and suggest dietary improvements as needed. For example, if there is a deficiency in vitamins or minerals, it will suggest ingredients and recipes to supplement them. The Meal Management Department can also provide individually optimized menus, taking into account the elderly individual's dietary preferences and allergy information. This allows elderly individuals to enjoy healthy and satisfying meals. Furthermore, the Meal Management Department manages the timing and quantity of meals and provides advice to prevent overeating and nutritional deficiencies. For example, if the interval between meals is too short or the amount consumed is too large, it will suggest appropriate meal timing and quantity. In this way, the Meal Management Department can maintain the health of elderly individuals and improve their quality of life.

[0089] The Medication Management Department manages medications based on data analyzed by the Analysis Department. For example, the Medication Management Department can determine the type of medication and its intake status from images of medications, and point out missed doses or overdoses. Specifically, a generating AI analyzes images of medications to identify the type and dosage. This allows the department to verify whether elderly individuals are taking their medications correctly and provide reminders as needed. For example, when it is time to take medication, a reminder is sent via voice or notification to prevent missed doses. In addition, by recording medication intake status and analyzing intake patterns based on past data, overdoses and underdoses can be detected early. This allows the Medication Management Department to provide support to elderly individuals in taking medications appropriately and maintaining their health. Furthermore, the Medication Management Department also provides information on drug side effects and interactions to ensure that elderly individuals can use medications safely. For example, it provides advice on points to be aware of when taking specific medications and interactions with other medications. In this way, the Medication Management Department can comprehensively support the management of medications for elderly individuals and minimize health risks.

[0090] The Health Management Department manages health based on data analyzed by the Analysis Department. For example, the Health Management Department records and analyzes vital signs such as heart rate, calories burned, and steps taken, and provides health advice and encourages exercise. Specifically, a generating AI analyzes collected vital sign data to assess the health status of elderly individuals. This allows for the provision of information useful for daily health management and preventive medicine. For example, if a person's heart rate is abnormally high or their calorie expenditure is low, the department provides advice to encourage appropriate exercise. Furthermore, based on step count data, it can evaluate daily activity levels and propose specific exercise plans to prevent inactivity. In addition, the Health Management Department analyzes health trends based on past data to support long-term health management. For example, based on past heart rate data, it can predict fluctuations in health risks over a specific period and formulate future countermeasures. This allows the Health Management Department to comprehensively manage the health of elderly individuals and improve their quality of life.

[0091] The Sleep Management Department manages sleep based on data analyzed by the Analysis Department. For example, the Sleep Management Department records bedtime, sleep quality, and wake-up time, and provides advice on appropriate bedtimes and wake-up times. Specifically, a generating AI analyzes the collected sleep data and evaluates the sleep patterns of elderly individuals. This allows for the provision of specific advice to ensure high-quality sleep. For example, if bedtime is too late or sleep quality is poor, it suggests appropriate bedtimes and relaxation methods. It can also adjust wake-up times to avoid impacting daytime activities. Furthermore, the Sleep Management Department analyzes sleep trends based on past data to support long-term sleep management. For example, based on past sleep data, it can predict fluctuations in sleep quality over a specific period and plan future countermeasures. In this way, the Sleep Management Department can comprehensively manage the sleep of elderly individuals and improve their quality of life.

[0092] The Schedule Management Department manages schedules based on data analyzed by the Analysis Department. For example, the Schedule Management Department manages the schedules of elderly individuals and provides reminders. Specifically, a generation AI analyzes the elderly person's schedule data and reminds them of important appointments and events. This allows elderly individuals to live their daily lives smoothly without forgetting appointments. For example, it reminds them of medical appointments, medication times, and family appointments, and sends notifications at the appropriate time. The Schedule Management Department also optimizes the elderly person's schedule and provides advice to avoid excessive burden. For example, if there are too many consecutive appointments, it suggests appropriate rest periods and creates a balanced schedule. In this way, the Schedule Management Department can comprehensively support the lives of elderly individuals and improve their quality of life.

[0093] The conversation unit engages in intellectually stimulating conversations and alleviates loneliness based on data analyzed by the analysis unit. For example, the conversation unit learns from conversations with the user and their family, and then provides automated conversations optimized for the user. Specifically, the generative AI analyzes conversation data from the elderly and generates appropriate conversation topics and responses. This allows the elderly to enjoy daily communication without feeling lonely. For example, it suggests conversations based on the elderly person's hobbies and interests, providing intellectual stimulation. The conversation unit can also analyze the elderly person's emotional state and provide appropriate support. For example, if the elderly person is feeling stressed, it will suggest ways to relax or change their mood. Furthermore, the conversation unit supports communication with family members and facilitates contact with family members who live far away. In this way, the conversation unit can reduce feelings of loneliness in the elderly and provide support to maintain their mental health.

[0094] The data collection unit includes a camera, microphone, speaker, GPS, accelerometer, ambient light sensor, hearing aid, activity tracker, heart rate monitor, calorie counter, sleep monitor, and blood oxygen meter. The data collection unit can, for example, use the camera to capture images of an elderly person eating. The data collection unit can, for example, use the microphone to collect conversations of an elderly person. The data collection unit can, for example, use the speaker to provide voice advice to an elderly person. The data collection unit can, for example, use GPS to acquire the elderly person's location information. The data collection unit can, for example, use the accelerometer to measure the elderly person's activity level. The data collection unit can, for example, use the ambient light sensor to measure the brightness around an elderly person. The data collection unit can, for example, use a hearing aid to support the elderly person's hearing. The data collection unit can, for example, use the activity tracker to measure the elderly person's exercise level. The data collection unit can, for example, use a heart rate monitor to measure the elderly person's heart rate. The data collection unit can, for example, use a calorie counter to measure the elderly person's calorie consumption. The data collection unit can, for example, use a sleep monitor to measure the elderly person's sleep status. The data collection unit can, for example, measure the blood oxygen concentration of elderly individuals using a blood oxygen meter. By equipping the data collection unit with a variety of sensors, it can collect detailed lifestyle data of elderly individuals. Some or all of the above-described processes in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input video data captured by a camera into a generating AI and have the generating AI perform the process of extracting necessary information from the video data.

[0095] The meal management department can analyze the amount of calories and nutrients from a video of a meal and suggest the next menu item to eat. For example, the meal management department can analyze a video of a meal and calculate the amount of calories and nutrients. For example, the meal management department can consider the balance of calories and nutrients in order to suggest the next menu item to eat. For example, the meal management department can analyze a video of a meal and identify the types and quantities of ingredients. For example, the meal management department can analyze a video of a meal and calculate the amount of food consumed. As a result, the meal management department can suggest an appropriate menu by analyzing a video of a meal. Some or all of the above processes in the meal management department may be performed using AI, for example, or without AI. For example, the meal management department can input video data of a meal into a generating AI and have the generating AI perform the process of calculating the amount of calories and nutrients.

[0096] The drug management department can determine the type of medication and the dosage status from the video of the medication, and can point out missed doses or excessive doses. For example, the drug management department can analyze the video of the medication to identify the type of medication. For example, the drug management department can analyze the video of the medication to determine the dosage status. For example, the drug management department can analyze the video of the medication to point out missed doses. For example, the drug management department can analyze the video of the medication to point out excessive doses. In this way, the drug management department can appropriately manage medication dosage by analyzing the video of the medication. Some or all of the above processing in the drug management department may be performed using AI, for example, or without AI. For example, the drug management department can input video data of the medication into a generating AI and have the generating AI perform the processing to determine the type of medication and the dosage status.

[0097] The health management department can record and analyze vital signs such as heart rate, calories burned, and steps taken, and provide health advice and encourage exercise. For example, the health management department can record heart rate and analyze fluctuations in heart rate. For example, the health management department can record calories burned and analyze fluctuations in calories burned. For example, the health management department can record steps taken and analyze fluctuations in steps taken. For example, the health management department can provide health advice based on data such as heart rate, calories burned, and steps taken. For example, the health management department can encourage exercise based on data such as heart rate, calories burned, and steps taken. In this way, the health management department can provide health advice and encourage exercise by recording and analyzing vital signs. Some or all of the above processing in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input data on heart rate, calories burned, and steps taken into a generating AI and have the generating AI perform the processing of providing health advice and encouraging exercise.

[0098] The sleep management unit can record bedtime, sleep quality, and wake-up time, and provide advice on appropriate bedtime and wake-up time. For example, the sleep management unit can record bedtime and analyze fluctuations in bedtime. For example, the sleep management unit can record sleep quality and analyze fluctuations in sleep quality. For example, the sleep management unit can record wake-up time and analyze fluctuations in wake-up time. For example, the sleep management unit can provide advice on appropriate bedtime based on data on bedtime, sleep quality, and wake-up time. For example, the sleep management unit can provide advice on appropriate wake-up time based on data on bedtime, sleep quality, and wake-up time. In this way, the sleep management unit can provide appropriate sleep advice by recording and analyzing sleep data. Some or all of the above processing in the sleep management unit may be performed using AI, for example, or without AI. For example, the sleep management unit can input data on bedtime, sleep quality, and wake-up time into a generating AI and have the generating AI perform the process of providing advice on appropriate bedtime and wake-up time.

[0099] The conversation unit can learn from conversations with the user and their family and provide automated conversations optimized for the user. For example, the conversation unit can record conversations with the user and their family and learn from their content. For example, the conversation unit can analyze patterns of conversations with the user and their family and generate automated conversations optimized for the user. For example, the conversation unit can provide appropriate topics based on the user's interests and concerns. For example, the conversation unit can refer to the user's past conversation history and provide relevant topics. In this way, the conversation unit can provide conversations optimized for the user by learning from conversations. Some or all of the above processes in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input conversation data with the user and their family into a generating AI and have the generating AI perform the process of generating optimized automated conversations.

[0100] The data collection unit can estimate the emotions of elderly individuals and adjust the frequency of data collection based on the estimated emotions. For example, if an elderly individual is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if an elderly individual is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if an elderly individual is anxious, the data collection unit can appropriately adjust the frequency of data collection to provide a sense of security. In this way, the data collection unit can collect detailed data while reducing the burden by adjusting the frequency of data collection according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly individual's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0101] The data collection unit can analyze the elderly person's past behavioral patterns and select the optimal timing for data collection. For example, if the elderly person has a habit of taking a walk every morning, the data collection unit can collect data during the walk. For example, the data collection unit can collect data during the time when the elderly person is relaxing at night. For example, the data collection unit can collect data when the elderly person regularly visits a medical institution. In this way, the data collection unit can collect data at the optimal timing by analyzing past behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's past behavioral data into a generating AI and have the generating AI perform the process of selecting the optimal timing for data collection.

[0102] The data collection unit can automatically adjust the sensitivity of sensors in response to environmental changes during data collection. For example, the data collection unit can adjust the camera sensitivity if the brightness of the lighting changes. For example, the data collection unit can adjust the microphone sensitivity if the ambient noise level changes. For example, the data collection unit can adjust the sensitivity of the acceleration sensor if the activity level of an elderly person increases. In this way, the data collection unit can collect accurate data by adjusting the sensitivity of sensors in response to environmental changes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental change data into a generating AI and have the generating AI execute the process of automatically adjusting the sensitivity of the sensors.

[0103] The data collection unit can estimate the emotions of elderly individuals and determine the priority of data to collect based on the estimated emotions. For example, if an elderly individual is stressed, the data collection unit can prioritize the collection of heart rate and blood pressure data. For example, if an elderly individual is relaxed, the data collection unit can prioritize the collection of activity level and calorie expenditure data. For example, if an elderly individual is anxious, the data collection unit can prioritize the collection of sleep data. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0104] The data collection unit can adjust the timing of data collection by taking family schedules into consideration. For example, the data collection unit can refrain from collecting data during times when family members are visiting. For example, the data collection unit can collect data during times when family members are out. For example, the data collection unit can refrain from collecting data during times when family members are spending time together. In this way, the data collection unit can optimize the timing of data collection by taking family schedules into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input family schedule data into a generating AI and have the generating AI perform the processing to adjust the timing of data collection.

[0105] The data collection unit can optimize the data collection content by referring to local weather information during data collection. For example, the data collection unit can prioritize collecting indoor activity data during rainy weather. For example, the data collection unit can prioritize collecting outdoor activity data during sunny weather. For example, the data collection unit can prioritize collecting body temperature and water intake data when the temperature is high. In this way, the data collection unit can optimize the data collection content by referring to local weather information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input local weather information data into a generating AI and have the generating AI perform the processing to optimize the data collection content.

[0106] The analysis unit can estimate the emotions of elderly individuals and adjust the analysis algorithm based on the estimated emotions. For example, if an elderly individual is experiencing stress, the analysis unit can prioritize stress-related data in its analysis. For example, if an elderly individual is relaxed, the analysis unit can analyze overall health data in a balanced manner. For example, if an elderly individual is experiencing anxiety, the analysis unit can prioritize data that provides a sense of security in its analysis. This allows the analysis unit to provide more appropriate analysis results by adjusting the analysis algorithm according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0107] The analysis unit can detect outliers by comparing current data with past data during analysis. For example, the analysis unit can detect an outlier if the heart rate of an elderly person is abnormally high compared to past data. For example, the analysis unit can detect an outlier if the number of steps taken by an elderly person is abnormally low compared to past data. For example, the analysis unit can detect an outlier if the sleep duration of an elderly person is abnormally short compared to past data. In this way, the analysis unit can accurately detect outliers by comparing them with past data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the process of detecting outliers.

[0108] The analysis unit can apply different analysis methods depending on the type of data during analysis. For example, the analysis unit can apply time series analysis to heart rate data. For example, the analysis unit can apply statistical analysis to step count data. For example, the analysis unit can apply pattern recognition to sleep data. By applying analysis methods appropriate to the type of data, the analysis unit can improve the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI execute an analysis method appropriate to the type of data.

[0109] The analysis unit can estimate the emotions of elderly individuals and adjust the display method of the analysis results based on the estimated emotions. For example, if an elderly individual is feeling stressed, the analysis unit can provide a simple and highly visible display method. For example, if an elderly individual is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if an elderly individual is feeling anxious, the analysis unit can provide a display method that provides a sense of security. In this way, the analysis unit improves visibility by adjusting the display method of the analysis results according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0110] The analysis unit can improve the accuracy of its analysis by incorporating feedback from the family during the analysis process. For example, the analysis unit can adjust its analysis algorithm based on feedback from the family. For example, the analysis unit can improve how it displays the analysis results by incorporating feedback from the family. For example, the analysis unit can review its data collection methods based on feedback from the family. As a result, the analysis unit improves its accuracy by incorporating feedback from the family. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input family feedback data into a generating AI and have the generating AI perform the process of adjusting the analysis algorithm.

[0111] The analysis unit can supplement its analysis results by referring to local medical data during the analysis process. For example, the analysis unit can supplement its analysis results based on local medical data. For example, the analysis unit can improve the accuracy of detecting outliers by referring to local medical data. For example, the analysis unit can provide health advice based on local medical data. In this way, the analysis unit can supplement its analysis results by referring to local medical data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input local medical data into a generating AI and have the generating AI perform the process of supplementing the analysis results.

[0112] The meal management department can estimate the emotions of elderly individuals and propose meal menus based on those estimated emotions. For example, if an elderly individual is feeling stressed, the meal management department can propose a menu using ingredients that have a relaxing effect. For example, if an elderly individual is relaxed, the meal management department can propose a nutritionally balanced menu. For example, if an elderly individual is feeling anxious, the meal management department can propose a menu using ingredients that provide a sense of security. In this way, the meal management department improves meal satisfaction by proposing appropriate meal menus according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the meal management department may be performed using AI, for example, or without AI. For example, the meal management department can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0113] The meal management department can optimize nutritional balance by referring to past meal history during meal management. For example, the meal management department can suggest a nutritionally balanced menu based on the past meal history of an elderly person. For example, the meal management department can suggest a menu that supplements a specific nutrient if it is deficient by referring to the past meal history of an elderly person. For example, the meal management department can suggest a menu that increases the variety of meals based on the past meal history of an elderly person. In this way, the meal management department can provide nutritionally balanced meals by referring to past meal history. Some or all of the above processes in the meal management department may be performed using AI, for example, or without AI. For example, the meal management department can input past meal history data into a generating AI and have the generating AI perform the process of optimizing nutritional balance.

[0114] The meal management department can propose menus that take seasonal ingredients into consideration when managing meals. For example, the meal management department can propose menus that use seasonal ingredients. For example, the meal management department can propose nutritionally balanced menus that adapt to seasonal changes. For example, the meal management department can propose menus that increase meal variety by using seasonal ingredients. In this way, the meal management department can increase meal variety and improve nutritional balance by taking seasonal ingredients into consideration. Some or all of the above processes in the meal management department may be performed using AI, for example, or without using AI. For example, the meal management department can input seasonal ingredient data into a generating AI and have the generating AI perform the process of proposing menus.

[0115] The meal management department can estimate the emotions of elderly individuals and adjust meal timing based on the estimated emotions. For example, if an elderly individual is feeling stressed, the meal management department can suggest meals at times when they can relax. For example, if an elderly individual is relaxed, the meal management department can suggest meals at normal meal times. For example, if an elderly individual is feeling anxious, the meal management department can suggest meals at times when they can provide a sense of security. In this way, the meal management department improves meal satisfaction by adjusting meal timing according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the meal management department may be performed using AI, for example, or without AI. For example, the meal management department can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0116] The meal management unit can suggest menus while considering the family's meal schedule. For example, the meal management unit can suggest menus that match the time when the family eats together. For example, the meal management unit can suggest nutritionally balanced menus while considering the family's meal schedule. For example, the meal management unit can suggest menus that increase the variety of meals according to the family's meal schedule. In this way, the meal management unit can enjoy meals with the family by considering the family's meal schedule. Some or all of the above processes in the meal management unit may be performed using AI, for example, or without AI. For example, the meal management unit can input family meal schedule data into a generating AI and have the generating AI perform the process of suggesting menus.

[0117] The meal management department can optimize menus by referring to local food supply information during meal management. For example, the meal management department can propose nutritionally balanced menus based on local food supply information. For example, the meal management department can propose menus using seasonal ingredients by referring to local food supply information. For example, the meal management department can propose menus that increase the variety of meals based on local food supply information. In this way, by referring to local food supply information, the meal management department can reduce food waste and provide nutritionally balanced meals. Some or all of the above processes in the meal management department may be performed using AI, for example, or without AI. For example, the meal management department can input local food supply information data into a generating AI and have the generating AI execute the process of optimizing menus.

[0118] The medication management department can estimate the emotions of elderly individuals and adjust the timing of medication intake based on the estimated emotions. For example, if an elderly individual is feeling stressed, the medication management department can suggest taking the medication during a time when they can relax. For example, if an elderly individual is relaxed, the medication management department can suggest taking the medication at the usual time. For example, if an elderly individual is feeling anxious, the medication management department can suggest taking the medication during a time when they can provide a sense of security. In this way, the medication management department can maximize the effectiveness of the medication by adjusting the timing of medication intake according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the medication management department may be performed using AI, for example, or not using AI. For example, the medication management department can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0119] The medication management department can suggest the optimal dosage method by referring to past medication intake history during medication management. For example, the medication management department can suggest the optimal dosage method based on the past medication intake history of elderly individuals. For example, the medication management department can suggest methods to prevent missed doses by referring to the past medication intake history of elderly individuals. For example, the medication management department can suggest methods to prevent overdosing by referring to past medication intake history of elderly individuals. In this way, the medication management department can prevent missed doses and overdoses by referring to past medication intake history. Some or all of the above processes in the medication management department may be performed using AI, for example, or without AI. For example, the medication management department can input past medication intake history data into a generating AI and have the generating AI perform the process of suggesting the optimal dosage method.

[0120] The drug management unit can apply different management methods depending on the type of drug during drug management. For example, the drug management unit can set reminders to prevent missed doses for tablet drugs. For example, the drug management unit can suggest appropriate measurement methods for liquid drugs. For example, the drug management unit can suggest appropriate application methods for topical drugs. In this way, the drug management unit improves the accuracy of drug management by applying management methods according to the type of drug. Some or all of the above processes in the drug management unit may be performed using AI, for example, or without AI. For example, the drug management unit can input drug type data into a generating AI and have the generating AI execute the process of applying management methods.

[0121] The medication management unit can estimate the emotions of elderly individuals and adjust medication reminders based on those estimated emotions. For example, if an elderly individual is stressed, the medication management unit can set a reminder in a relaxing voice. For example, if an elderly individual is relaxed, the medication management unit can set a reminder in a normal voice. For example, if an elderly individual is anxious, the medication management unit can set a reminder in a reassuring voice. This allows the medication management unit to ensure that elderly individuals do not forget to take their medication by adjusting reminders according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the medication management unit may be performed using AI, for example, or without AI. For example, the medication management unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0122] The medication management department can improve the accuracy of medication management by incorporating feedback from family members. For example, the medication management department can adjust medication reminders based on feedback from family members. For example, the medication management department can improve medication management methods by incorporating family opinions. For example, the medication management department can review the timing of medication intake based on feedback from family members. In this way, the medication management department can improve the accuracy of medication management by incorporating feedback from family members. Some or all of the above processes in the medication management department may be performed using AI, for example, or not using AI. For example, the medication management department can input family feedback data into a generating AI and have the generating AI execute processes to improve the accuracy of management.

[0123] The drug management department can supplement its drug management methods by referring to local medical data during drug management. For example, the drug management department can supplement drug intake methods based on local medical data. For example, the drug management department can suggest methods to prevent drug side effects by referring to local medical data. For example, the drug management department can suggest methods to prevent drug interactions based on local medical data. In this way, the drug management department can supplement its drug management methods and improve accuracy by referring to local medical data. Some or all of the above processes in the drug management department may be performed using AI, for example, or without AI. For example, the drug management department can input local medical data into a generating AI and have the generating AI perform the processing to supplement the management methods.

[0124] 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 individual is feeling stressed, the health management department can suggest exercises that have a relaxing effect. For example, if an elderly individual is relaxed, the health management department can suggest regular exercises. For example, if an elderly individual is feeling anxious, the health management department can suggest exercises that provide a sense of security. In this way, the health management department can provide more appropriate health advice by adjusting health management advice according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0125] The health management department can provide health advice by referring to past vital data during health management. For example, the health management department can suggest appropriate exercise based on the past heart rate data of an elderly person. For example, the health management department can suggest appropriate meals by referring to the past calorie expenditure data of an elderly person. For example, the health management department can suggest an appropriate amount of exercise based on the past step count data of an elderly person. In this way, the health management department can provide more appropriate health advice by referring to past vital data. Some or all of the above processing in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input past vital data into a generating AI and have the generating AI perform the process of providing health advice.

[0126] The health management department can adjust advice when managing health, taking into account seasonal changes. For example, the health management department can suggest appropriate exercise according to seasonal changes. For example, the health management department can suggest appropriate meals considering seasonal changes. For example, the health management department can suggest appropriate sleep duration according to seasonal changes. In this way, the health management department can provide more appropriate health advice by taking seasonal changes into account. Some or all of the above processes in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input seasonal change data into a generating AI and have the generating AI perform the process of adjusting the advice.

[0127] The health management department can estimate the emotions of elderly individuals and determine the priority of health management based on the estimated emotions. For example, if an elderly individual is experiencing stress, the health management department can prioritize stress management. For example, if an elderly individual is relaxed, the health management department can prioritize overall health management. For example, if an elderly individual is feeling anxious, the health management department can prioritize health management that provides a sense of security. In this way, the health management department can provide more appropriate health management by determining the priority of health management according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0128] The health management unit can provide advice by referring to family health data during health management. For example, the health management unit can suggest appropriate exercise based on family health data. For example, the health management unit can suggest appropriate meals based on family health data. For example, the health management unit can suggest appropriate sleep duration based on family health data. In this way, the health management unit can provide more appropriate health advice by referring to family health data. Some or all of the above processes in the health management unit may be performed using AI, for example, or without AI. For example, the health management unit can input family health data into a generating AI and have the generating AI perform the process of providing advice.

[0129] The health management department can supplement its advice by referring to local health information when managing health. For example, the health management department can suggest appropriate exercise based on local health information. For example, the health management department can suggest appropriate meals based on local health information. For example, the health management department can suggest appropriate sleep duration based on local health information. In this way, the health management department can provide more appropriate health advice by referring to local health information. Some or all of the above processes in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input local health information data into a generating AI and have the generating AI perform the process of supplementing the advice.

[0130] The sleep management unit can estimate the emotions of elderly individuals and adjust sleep advice based on the estimated emotions. For example, if an elderly individual is stressed, the sleep management unit can provide relaxing sleep advice. For example, if an elderly individual is relaxed, the sleep management unit can provide standard sleep advice. For example, if an elderly individual is anxious, the sleep management unit can provide reassuring sleep advice. In this way, the sleep management unit can provide more appropriate sleep advice by adjusting it according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sleep management unit may be performed using AI, for example, or without AI. For example, the sleep management unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0131] The sleep management unit can suggest the optimal bedtime by referring to past sleep data during sleep management. For example, the sleep management unit can suggest the optimal bedtime based on the past sleep data of an elderly person. For example, the sleep management unit can suggest the optimal wake-up time by referring to the past sleep data of an elderly person. For example, the sleep management unit can suggest the optimal sleep environment based on the past sleep data of an elderly person. In this way, the sleep management unit can suggest a more appropriate bedtime by referring to past sleep data. Some or all of the above processes in the sleep management unit may be performed using AI, for example, or without using AI. For example, the sleep management unit can input past sleep data into a generating AI and have the generating AI perform the process of suggesting the optimal bedtime.

[0132] The sleep management unit can provide advice during sleep management, taking into account changes in ambient noise and light. For example, if the ambient noise is loud, the sleep management unit can provide advice on creating a quieter environment. For example, if the light changes drastically, the sleep management unit can suggest appropriate lighting. For example, the sleep management unit can suggest an optimal sleep environment by taking into account changes in ambient noise and light. In this way, the sleep management unit can provide a more appropriate sleep environment by taking into account changes in ambient noise and light. Some or all of the above processing in the sleep management unit may be performed using AI, for example, or without using AI. For example, the sleep management unit can input ambient noise and light change data into a generating AI and have the generating AI perform the process of providing advice.

[0133] The sleep management unit can estimate the emotions of elderly individuals and adjust the sleep environment based on the estimated emotions. For example, if an elderly individual is feeling stressed, the sleep management unit can provide a relaxing environment. For example, if an elderly individual is relaxed, the sleep management unit can provide a normal sleep environment. For example, if an elderly individual is feeling anxious, the sleep management unit can provide a reassuring environment. In this way, the sleep management unit can provide a better sleep environment by adjusting the sleep environment according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sleep management unit may be performed using AI, for example, or without AI. For example, the sleep management unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0134] The sleep management unit can provide advice by referring to family sleep data during sleep management. For example, the sleep management unit can suggest an appropriate bedtime based on family sleep data. For example, the sleep management unit can suggest an appropriate wake-up time based on family sleep data. For example, the sleep management unit can suggest an appropriate sleep environment based on family sleep data. In this way, the sleep management unit can provide more appropriate sleep advice by referring to family sleep data. Some or all of the above processes in the sleep management unit may be performed using AI, for example, or without AI. For example, the sleep management unit can input family sleep data into a generating AI and have the generating AI perform the process of providing advice.

[0135] The sleep management unit can supplement its advice by referring to local weather information during sleep management. For example, the sleep management unit can suggest an appropriate bedtime based on local weather information. For example, the sleep management unit can suggest an appropriate wake-up time based on local weather information. For example, the sleep management unit can suggest an appropriate sleep environment based on local weather information. In this way, the sleep management unit can provide more appropriate sleep advice by referring to local weather information. Some or all of the above processes in the sleep management unit may be performed using AI, for example, or without AI. For example, the sleep management unit can input local weather data into a generating AI and have the generating AI perform the process of supplementing the advice.

[0136] The schedule management unit can estimate the emotions of elderly individuals and adjust the priority of appointments based on the estimated emotions. For example, if an elderly individual is feeling stressed, the schedule management unit can prioritize relaxing appointments. For example, if an elderly individual is relaxed, the schedule management unit can prioritize regular appointments. For example, if an elderly individual is feeling anxious, the schedule management unit can prioritize appointments that provide a sense of security. This allows the schedule management unit to manage schedules more effectively by adjusting the priority of appointments according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0137] The schedule management unit can propose an optimal schedule by referring to past schedule history when managing schedules. For example, the schedule management unit can propose an optimal schedule based on the past schedule history of an elderly person. For example, the schedule management unit can prioritize a specific appointment by referring to the past schedule history of an elderly person. For example, the schedule management unit can increase the variety of appointments based on the past schedule history of an elderly person. As a result, the schedule management unit can propose a more appropriate schedule by referring to past schedule history. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input past schedule history data into a generating AI and have the generating AI execute the process of proposing an optimal schedule.

[0138] The schedule management unit can adjust schedules while considering seasonal events. For example, the schedule management unit can propose an optimal schedule by considering seasonal events. For example, the schedule management unit can increase the variety of appointments to match seasonal events. For example, the schedule management unit can prioritize specific appointments based on seasonal events. In this way, the schedule management unit can provide a more appropriate schedule by considering seasonal events. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input seasonal event data into a generating AI and have the generating AI perform the process of adjusting the schedule.

[0139] The schedule management unit can estimate the emotions of elderly individuals and adjust schedule reminders based on the estimated emotions. For example, if an elderly individual is feeling stressed, the schedule management unit can set a reminder in a relaxing voice. For example, if an elderly individual is relaxed, the schedule management unit can set a reminder in a normal voice. For example, if an elderly individual is feeling anxious, the schedule management unit can set a reminder in a reassuring voice. In this way, the schedule management unit can ensure that appointments are not forgotten by adjusting reminders according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0140] The schedule management unit can suggest schedules by referring to family schedules during schedule management. For example, the schedule management unit can adjust the schedules of elderly people based on family schedules. For example, the schedule management unit can prioritize time spent with family by referring to family schedules. For example, the schedule management unit can increase the variety of schedules based on family schedules. This allows the schedule management unit to prioritize time spent with family by referring to family schedules. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input family schedule data into a generating AI and have the generating AI perform the process of suggesting schedules.

[0141] The schedule management unit can supplement schedules by referring to local event information during schedule management. For example, the schedule management unit can propose an optimal schedule based on local event information. For example, the schedule management unit can increase the variety of schedules by referring to local event information. For example, the schedule management unit can prioritize specific schedules based on local event information. In this way, the schedule management unit can provide a more appropriate schedule by referring to local event information. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input local event information data into a generating AI and have the generating AI execute the process of supplementing schedules.

[0142] The conversation unit can estimate the emotions of elderly people and adjust the conversation content based on the estimated emotions. For example, if an elderly person is feeling stressed, the conversation unit can offer relaxing topics. For example, if an elderly person is relaxed, the conversation unit can offer normal conversation content. For example, if an elderly person is feeling anxious, the conversation unit can offer topics that provide reassurance. In this way, the conversation unit can provide more appropriate conversation by adjusting the conversation content according to the emotions of elderly people. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input facial expression data of elderly people into a generative AI and have the generative AI perform emotion estimation.

[0143] The conversation unit can provide optimal conversation content by referring to past conversation history. For example, the conversation unit can provide topics of interest based on the past conversation history of an elderly person. For example, the conversation unit can prioritize specific topics by referring to the past conversation history of an elderly person. For example, the conversation unit can increase the variety of conversations based on the past conversation history of an elderly person. In this way, the conversation unit can provide more appropriate conversation content by referring to past conversation history. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input past conversation history data into a generating AI and have the generating AI perform the processing to provide optimal conversation content.

[0144] The conversation unit can revitalize conversations by incorporating seasonal topics. The conversation unit can, for example, revitalize conversations by incorporating seasonal topics. The conversation unit can, for example, provide interesting topics in accordance with seasonal changes. The conversation unit can, for example, increase the variety of conversations based on seasonal topics. Thus, the conversation unit can increase the variety of conversations and revitalize conversations by incorporating seasonal topics. Some or all of the above-described processes in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input seasonal topic data into a generating AI and have the generating AI perform processes to revitalize conversations.

[0145] The conversation unit can estimate the emotions of elderly individuals and adjust the frequency of conversation based on the estimated emotions. For example, if an elderly individual is feeling stressed, the conversation unit can reduce the frequency of conversation. For example, if an elderly individual is relaxed, the conversation unit can maintain a normal frequency of conversation. For example, if an elderly individual is feeling anxious, the conversation unit can increase the frequency of conversation to provide reassurance. In this way, the conversation unit can provide more appropriate conversation by adjusting the frequency of conversation according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0146] The conversation unit can provide conversation content by referring to the family's conversation history. For example, the conversation unit can provide topics of interest based on the family's conversation history. For example, the conversation unit can prioritize specific topics by referring to the family's conversation history. For example, the conversation unit can increase the variety of conversations based on the family's conversation history. This allows the conversation unit to provide more appropriate conversation content by referring to the family's conversation history. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input family conversation history data into a generating AI and have the generating AI perform the processing of providing conversation content.

[0147] The conversational unit can supplement the conversation content by referring to local news information. For example, the conversational unit can provide interesting topics based on local news information. For example, the conversational unit can prioritize specific topics by referring to local news information. For example, the conversational unit can increase the variety of conversations based on local news information. As a result, the conversational unit can provide more appropriate conversation content by referring to local news information. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input local news information data into a generating AI and have the generating AI perform the processing of supplementing the conversation content.

[0148] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0149] The elderly support system can also be equipped with an emergency notification unit. This unit monitors the elderly person's lifestyle data in real time and can quickly notify family members or medical institutions if an abnormality is detected. For example, if the heart rate suddenly increases, the emergency notification unit can immediately notify family members and arrange for an ambulance if necessary. Also, if a fall is detected, the emergency notification unit can send the elderly person's location information to family members or medical institutions to encourage a quick response. Furthermore, if medication is forgotten or taken in excess, the emergency notification unit can also notify family members and encourage appropriate action. In this way, the emergency notification unit ensures the safety of the elderly person and enables a quick response.

[0150] The elderly support system can also be equipped with a reminder function. This reminder function has the ability to remind elderly individuals of important tasks in their daily lives. For example, when it's time to take medication, the reminder function can notify the elderly person with voice or vibration. Similarly, when a scheduled medical appointment is approaching, the reminder function can notify the elderly person and encourage them to attend. Furthermore, the reminder function can support the elderly person's health management by reminding them of meal times and exercise times. In this way, the reminder function helps elderly individuals remember and complete important tasks.

[0151] The senior support system can also include an entertainment section. This section provides entertainment to enhance the lives of seniors. For example, it can offer relaxation and enjoyment by playing music or audiobooks. It can also stimulate cognitive functions by providing intellectual games such as puzzles and quizzes. Furthermore, it can support video calls with family and friends, helping seniors maintain social connections. In this way, the entertainment section can provide enjoyment and stimulation to the lives of seniors, reducing feelings of loneliness.

[0152] The senior support system can also include a learning support section. This section has functions to support seniors in learning new knowledge and skills. For example, it can provide online courses and webinars, allowing seniors to learn about areas of interest. It can also provide language learning apps to support seniors in learning new languages. Furthermore, it can create reading lists and study plans to help seniors continue their learning. In this way, the learning support section can satisfy seniors' intellectual curiosity and promote their self-growth.

[0153] The senior support system can also include a community liaison department. This department has functions to support seniors in maintaining connections with their local community. For example, it can provide information on local events and activities, increasing opportunities for seniors to participate. It can also introduce volunteer activities and hobby groups, supporting seniors in making new friends. Furthermore, it can provide information on local support services and medical institutions, ensuring seniors receive the support they need. In this way, the community liaison department helps seniors maintain connections with their community and prevent isolation.

[0154] The data collection unit can estimate the emotions of elderly individuals and adjust the frequency of data collection based on the estimated emotions. For example, if an elderly individual is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if an elderly individual is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if an elderly individual is feeling anxious, the data collection unit can appropriately adjust the frequency of data collection to provide a sense of security. In this way, the data collection unit can collect detailed data while reducing the burden by adjusting the frequency of data collection according to the emotions of elderly individuals.

[0155] The analysis unit can estimate the emotions of elderly individuals and adjust the analysis algorithm based on the estimated emotions. For example, if an elderly individual is experiencing stress, the analysis unit can prioritize stress-related data in its analysis. For example, if an elderly individual is relaxed, the analysis unit can analyze overall health data in a balanced manner. For example, if an elderly individual is experiencing anxiety, the analysis unit can prioritize data that provides a sense of security in its analysis. In this way, the analysis unit can provide more appropriate analysis results by adjusting the analysis algorithm according to the emotions of elderly individuals.

[0156] The Meal Management Department can estimate the emotions of elderly individuals and propose meal menus based on those estimates. For example, if an elderly person is feeling stressed, the Meal Management Department can propose a menu using ingredients that have a relaxing effect. For example, if an elderly person is relaxed, the Meal Management Department can propose a nutritionally balanced menu. For example, if an elderly person is feeling anxious, the Meal Management Department can propose a menu using ingredients that provide a sense of security. In this way, the Meal Management Department can improve meal satisfaction by proposing appropriate meal menus according to the emotions of elderly individuals.

[0157] 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 Health Management Department can suggest exercises that have a relaxing effect. For example, if an elderly person is relaxed, the Health Management Department can suggest regular exercises. For example, if an elderly person is feeling anxious, the Health Management Department can suggest exercises that provide a sense of security. In this way, the Health Management Department can provide more appropriate health advice by adjusting health management advice according to the emotions of elderly individuals.

[0158] The conversation unit can estimate the emotions of elderly individuals and adjust the conversation content based on those estimated emotions. For example, if an elderly person is feeling stressed, the conversation unit can offer relaxing topics. For example, if an elderly person is relaxed, the conversation unit can offer normal conversation topics. For example, if an elderly person is feeling anxious, the conversation unit can offer topics that provide reassurance. In this way, the conversation unit can provide more appropriate conversation by adjusting the conversation content according to the emotions of elderly individuals.

[0159] The following briefly describes the processing flow for example form 2.

[0160] Step 1: The data collection unit collects lifestyle data from the elderly person. The data collection unit includes, for example, a camera, microphone, speaker, GPS, accelerometer, ambient light sensor, hearing aid, activity tracker, heart rate monitor, calorie counter, sleep monitor, and blood oxygen meter. These sensors are used to monitor the elderly person's lifestyle in detail. For example, the camera captures images of the elderly person eating, and the microphone collects their conversations. The speaker provides voice advice to the elderly person. The GPS obtains the elderly person's location information, and the accelerometer measures their activity level. The ambient light sensor measures the brightness around the elderly person, and the hearing aid supports their hearing. The activity tracker measures the elderly person's exercise level, and the heart rate monitor measures their heart rate. The calorie counter measures the elderly person's calorie expenditure, and the sleep monitor measures their sleep status. The blood oxygen meter measures the elderly person's blood oxygen concentration. Step 2: The analysis unit analyzes the data collected by the collection unit and provides appropriate support. The analysis unit uses generative AI to analyze the data. For example, the generative AI analyzes videos of elderly people eating to analyze the amount of calories and nutrients. The generative AI analyzes videos of elderly people taking medication to determine the type of medication and how it is being taken. The generative AI analyzes vital signs such as heart rate, calories burned, and steps taken by elderly people to provide health advice and encourage exercise. The generative AI analyzes sleep data of elderly people to advise on appropriate bedtimes and wake-up times. The generative AI analyzes the content of elderly people's conversations to support alleviating loneliness and facilitating intellectual conversation. Step 3: The dietary management department manages meals based on the data analyzed by the analysis department. For example, the dietary management department analyzes the amount of calories and nutrients from the video of the meal and suggests the next menu to eat. Step 4: The medication management department manages medications based on the data analyzed by the analysis department. For example, the medication management department can determine the type of medication and the dosage status from images of the medication and point out missed doses or excessive intake. Step 5: The Health Management Department manages health based on the data analyzed by the Analysis Department. For example, the Health Management Department records and analyzes vital signs such as heart rate, calories burned, and steps taken, and provides health advice and encourages exercise. Step 6: The sleep management department manages sleep based on the data analyzed by the analysis department. For example, the sleep management department records bedtime, sleep quality, and wake-up time, and provides advice on appropriate bedtime and wake-up times. Step 7: The schedule management unit manages schedules based on the data analyzed by the analysis unit. For example, the schedule management unit manages the schedules of elderly people and provides reminders. Step 8: The conversation unit engages in conversational and intellectually stimulating discussions based on the data analyzed by the analysis unit. For example, the conversation unit learns from conversations with the user and their family, and then engages in automated conversations optimized for the user.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, meal management unit, medication management unit, health management unit, sleep management unit, schedule management unit, and conversation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects lifestyle data of the elderly using the camera 42 and microphone 38B of the smart device 14 and processes the data by the control unit 46A. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides appropriate support. The meal management unit, medication management unit, health management unit, sleep management unit, schedule management unit, and conversation unit are implemented by, for example, the specific processing unit 290 of the data processing unit 12, and perform various management and support based on the analyzed data. This makes it possible to support elderly people living alone and monitor their mental and physical health. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Each of the multiple elements described above, including the data collection unit, analysis unit, meal management unit, medication management unit, health management unit, sleep management unit, schedule management unit, and conversation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects lifestyle data of the elderly using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides appropriate support. The meal management unit, medication management unit, health management unit, sleep management unit, schedule management unit, and conversation unit are implemented, for example, by the specific processing unit 290 of the data processing unit 12, and perform various management and support based on the analyzed data. This makes it possible to support elderly people living alone and monitor their mental and physical health. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0181] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] Each of the multiple elements described above, including the data collection unit, analysis unit, meal management unit, medication management unit, health management unit, sleep management unit, schedule management unit, and conversation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects lifestyle data of the elderly using the camera 42 and microphone 238 of the headset terminal 314 and processes the data by the control unit 46A. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides appropriate support. The meal management unit, medication management unit, health management unit, sleep management unit, schedule management unit, and conversation unit are implemented by, for example, the specific processing unit 290 of the data processing unit 12, which performs various management and support based on the analyzed data. This makes it possible to support elderly people living alone and monitor their mental and physical health. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0197] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] Each of the multiple elements described above, including the data collection unit, analysis unit, meal management unit, medication management unit, health management unit, sleep management unit, schedule management unit, and conversation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects lifestyle data of the elderly using the camera 42 and microphone 238 of the robot 414 and processes the data by the control unit 46A. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides appropriate support. The meal management unit, medication management unit, health management unit, sleep management unit, schedule management unit, and conversation unit are implemented by, for example, the specific processing unit 290 of the data processing unit 12, which performs various management and support based on the analyzed data. This makes it possible to support elderly people living alone and monitor their mental and physical health. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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."

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] (Note 1) A data collection department that collects lifestyle data of the elderly, An analysis unit analyzes the data collected by the aforementioned collection unit and provides appropriate support, Based on the data analyzed by the aforementioned analysis unit, a meal management unit performs meal management, Based on the data analyzed by the aforementioned analysis unit, a drug management unit performs drug management, Based on the data analyzed by the aforementioned analysis unit, a health management unit performs health management, Based on the data analyzed by the aforementioned analysis unit, a sleep management unit performs sleep management, Based on the data analyzed by the aforementioned analysis unit, a schedule management unit performs schedule management, The system includes a conversation unit that engages in conversations to alleviate loneliness and engage in intellectual conversations based on data analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It features a camera, microphone, speaker, GPS, accelerometer, ambient light sensor, hearing aid, activity tracker, heart rate monitor, calorie counter, sleep tracker, and blood oxygen meter. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned Food Management Department, The system analyzes the calorie and nutrient content of meals from video footage and suggests what you should eat next. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned drug management department, The system analyzes images of medications to determine the type of medication and the dosage status, and then identifies missed doses or excessive intake. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned health management department, It records and analyzes vital signs such as heart rate, calories burned, and steps taken, and provides health advice and encourages exercise. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned sleep management department, Record your bedtime, sleep quality, and wake-up time, and receive advice on appropriate bedtimes and wake-up times. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned conversation section is, It learns from conversations with users and their families and performs automated conversations optimized for the user. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the emotions of older adults and adjust the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the past behavioral patterns of elderly individuals to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the sensor sensitivity is automatically adjusted in response to changes in the environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system estimates the emotions of older adults and prioritizes the data to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, adjust the timing of data collection to take family schedules into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the collected content is optimized by referring to local weather information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the emotions of elderly individuals and adjusts the analysis algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, anomalies are detected by comparing them with past data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the emotions of elderly individuals and adjusts the display method of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We incorporate family feedback during the analysis to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the analysis results are supplemented by referring to local medical data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned Food Management Department, This system estimates the emotions of elderly people and suggests meal menus based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned Food Management Department, When managing your diet, refer to your past meal history to optimize nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Food Management Department, When managing your diet, we will suggest menus that take seasonal ingredients into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Food Management Department, The system estimates the emotions of elderly people and adjusts meal timing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Food Management Department, When managing meals, we suggest menus that take the family's meal schedule into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Food Management Department, When managing meals, we optimize menus by referring to local food supply information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned drug management department, The system estimates the emotions of elderly individuals and adjusts the timing of medication administration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned drug management department, When managing medication, we refer to past medication history to suggest the optimal dosage method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned drug management department, When managing medications, different management methods should be applied depending on the type of medication. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned drug management department, It estimates the emotions of older adults and adjusts medication reminders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned drug management department, Improve medication management accuracy by incorporating feedback from family members. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned drug management department, When managing medications, refer to local medical data to supplement management methods. The system described in Appendix 1, characterized by the features described herein. (Note 32) The physical condition management unit estimates the emotions of the elderly and adjusts the advice on physical condition management based on the estimated emotions The system according to Appendix 1, characterized by the above. (Appendix 33) The physical condition management unit provides health advice by referring to past vital data during physical condition management The system according to Appendix 1, characterized by the above. (Appendix 34) The physical condition management unit adjusts the advice by considering seasonal changes during physical condition management The system according to Appendix 1, characterized by the above. (Appendix 35) The physical condition management unit estimates the emotions of the elderly and determines the priority of physical condition management based on the estimated emotions The system according to Appendix 1, characterized by the above. (Appendix 36) The physical condition management unit provides advice by referring to the health data of family members during physical condition management The system according to Appendix 1, characterized by the above. (Appendix 37) The physical condition management unit completes the advice by referring to the health information of the region during physical condition management The system according to Appendix 1, characterized by the above. (Appendix 38) The sleep management unit estimates the emotions of the elderly and adjusts the sleep advice based on the estimated emotions The system according to Appendix 1, characterized by the above. (Appendix 39) The sleep management unit proposes the optimal bedtime by referring to past sleep data during sleep management The system according to Appendix 1, characterized by the above. (Appendix 40) The sleep management unit When providing advice on sleep management, we take into account changes in ambient noise and light. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned sleep management department, The system estimates the emotions of elderly individuals and adjusts their sleep environment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned sleep management department, When managing sleep, we provide advice by referring to the family's sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned sleep management department, When managing sleep, refer to local weather information to supplement the advice. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned schedule management department, It estimates the emotions of elderly people and adjusts the priority of appointments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned schedule management department, When managing your schedule, refer to your past schedule history to suggest the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned schedule management department, When managing your schedule, adjust it to take seasonal events into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned schedule management department, It estimates the emotions of older adults and adjusts scheduled reminders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned schedule management department, When managing schedules, refer to family schedules to suggest appointments. The system according to Appendix 1, characterized in that... (Appendix 49) The schedule management unit supplements the schedule by referring to the event information of the region during schedule management The system according to Appendix 1, characterized in that... (Appendix 50) The conversation unit estimates the emotions of the elderly and adjusts the conversation content based on the estimated emotions The system according to Appendix 1, characterized in that... (Appendix 51) The conversation unit provides the optimal conversation content by referring to the past conversation history The system according to Appendix 1, characterized in that... (Appendix 52) The conversation unit incorporates seasonal topics to activate the conversation The system according to Appendix 1, characterized in that... (Appendix 53) The conversation unit estimates the emotions of the elderly and adjusts the conversation frequency based on the estimated emotions The system according to Appendix 1, characterized in that... (Appendix 54) The conversation unit provides the conversation content by referring to the conversation history of the family The system according to Appendix 1, characterized in that... (Appendix 55) The conversation unit supplements the conversation content by referring to the local news information The system according to Appendix 1, characterized in that...

Explanation of Reference Signs

[0233] 10, 210, 310, 410 Data Processing System 12 Data Processing Device 14 Smart Device 214 Smart Glasses 314 Headset-Type Terminal 414 Robots

Claims

1. A data collection department that collects lifestyle data of the elderly, An analysis unit analyzes the data collected by the aforementioned collection unit and provides appropriate support, Based on the data analyzed by the aforementioned analysis unit, a meal management unit performs meal management, Based on the data analyzed by the aforementioned analysis unit, a drug management unit performs drug management, Based on the data analyzed by the aforementioned analysis unit, a health management unit performs health management, Based on the data analyzed by the aforementioned analysis unit, a sleep management unit performs sleep management, Based on the data analyzed by the aforementioned analysis unit, a schedule management unit performs schedule management, The system includes a conversation unit that engages in conversations to alleviate loneliness and engage in intellectual conversations based on data analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is It features a camera, microphone, speaker, GPS, accelerometer, ambient light sensor, hearing aid, activity tracker, heart rate monitor, calorie counter, sleep tracker, and blood oxygen meter. The system according to feature 1.

3. The aforementioned Food Management Department, The system analyzes the calorie and nutrient content of meals from video footage and suggests what you should eat next. The system according to feature 1.

4. The aforementioned drug management department, The system analyzes images of medications to determine the type of medication and the dosage status, and then identifies missed doses or excessive intake. The system according to feature 1.

5. The aforementioned health management department, It records and analyzes vital signs such as heart rate, calories burned, and steps taken, and provides health advice and encourages exercise. The system according to feature 1.

6. The aforementioned sleep management department, Record your bedtime, sleep quality, and wake-up time, and receive advice on appropriate bedtimes and wake-up times. The system according to feature 1.

7. The aforementioned conversation section is, It learns from conversations with users and their families and performs automated conversations optimized for the user. The system according to feature 1.

8. The aforementioned collection unit is We estimate the emotions of older adults and adjust the frequency of data collection based on the estimated emotions. The system according to feature 1.

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