system

Abstract

The system according to this embodiment aims to enable elderly people living alone to properly manage their health and respond quickly in emergencies. [Solution] The system according to the embodiment comprises a monitoring unit, a reminder unit, a bridging unit, and a notification unit. The monitoring unit checks the health status. The reminder unit reminds the user of the schedule based on the health status checked by the monitoring unit. The bridging unit acts as a liaison with the government based on the schedule reminded by the reminder unit. The notification unit makes emergency notifications and contacts based on the information relayed by the bridging 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for elderly people living alone to appropriately manage their health conditions and respond promptly in case of emergencies.

[0005] The system according to the embodiment aims to enable elderly people living alone to appropriately manage their health conditions and respond promptly in case of emergencies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a reminder unit, a bridging unit, and a notification unit. The monitoring unit checks the user's health status. The reminder unit reminds the user of their schedule based on the health status checked by the monitoring unit. The bridging unit acts as a liaison with the government based on the schedule reminded by the reminder unit. The notification unit makes emergency notifications and communications based on the information relayed by the bridging unit. [Effects of the Invention]

[0007] The system according to this embodiment allows elderly people living alone to properly manage their health and respond quickly in emergencies. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The elderly support system according to an embodiment of the present invention is a service for elderly people living alone, and is a system that supports the lives of elderly people using AI. This elderly support system checks the health status through conversation with the AI ​​and monitoring of physical and mental condition. Next, it provides schedule reminders and acts as a liaison with local government in the residential area. Furthermore, in emergencies, it makes announcements and contacts relatives and friends. In this way, it helps elderly people to live without anxiety. For example, the AI ​​may ask the elderly person, "Have you been sleeping well lately?" or suggest exercises such as, "You can just sit up, but try lifting your legs." This allows the system to constantly monitor the health status of the elderly person and take necessary actions. Next, the AI ​​may give reminders such as, "Reservations for vaccinations have started. I will make a reservation for you," or provide information such as, "There are autumn courses at the XX Longevity Support Center." This helps elderly people not forget necessary procedures. Furthermore, the AI ​​takes emergency action such as, "ALART! Contact counseling or a care manager." This allows elderly people to live with peace of mind even in emergencies. Through this mechanism, it is possible to support elderly people to live without anxiety. For example, AI can maintain the health of elderly people by constantly monitoring their health and taking necessary actions. It can also ensure that elderly people don't forget necessary procedures by providing schedule reminders and acting as a liaison with government agencies. Furthermore, in emergencies, it can make notifications and contact relatives and friends, allowing elderly people to live with peace of mind. Thus, elderly support systems can help elderly people live without anxiety by checking their health, providing schedule reminders, acting as a liaison with government agencies, and making notifications and contacts in emergencies.

[0029] The elderly support system according to this embodiment comprises a monitoring unit, a reminder unit, a bridging unit, and a notification unit. The monitoring unit checks the health status. The monitoring unit checks the health status, for example, through conversation with AI and monitoring of physical and mental state. The AI ​​may ask the elderly person, "Have you been sleeping well lately?" or suggest exercises, such as, "You can just sit up, but try lifting your legs." This allows the monitoring unit to constantly understand the health status of the elderly person and take necessary actions. The reminder unit reminds the elderly person of schedules based on the health status checked by the monitoring unit. The reminder unit may remind the elderly person, for example, about vaccination appointment bookings or information about local events. The AI ​​may make reminders such as, "Vaccination appointment bookings have started. I'll make a reservation for you," or provide information such as, "There's an autumn course at the XX Longevity Support Center." This allows the reminder unit to ensure that the elderly person does not forget to complete necessary procedures. The bridging unit acts as a liaison with the government based on the schedule reminded by the reminder unit. The bridging unit may contact the government and support necessary procedures, for example. The AI ​​communicates with government agencies and provides information such as, "There are autumn courses at the XX Longevity Support Center." This allows the liaison department to smoothly handle necessary procedures for the elderly. The reporting department makes emergency reports and contacts based on the information relayed by the liaison department. For example, the reporting department will contact emergency services and the police in an emergency and contact relatives and friends. The AI ​​will perform emergency responses such as, "ALART! Contact counseling or care managers." This allows the reporting department to ensure that the elderly can live with peace of mind even in emergencies. In this way, the elderly support system according to this embodiment can support the elderly so that they can live without anxiety by checking their health status, reminding them of schedules, liaising with government agencies, and making emergency reports and contacts.

[0030] The monitoring department collects information to check the health status of elderly individuals through conversations with AI and monitoring of their physical and mental state. Specifically, the AI ​​asks elderly individuals everyday questions to understand their health status. For example, by asking, "Have you been sleeping well lately?", it checks the quality and quantity of their sleep, and by suggesting simple exercises such as, "You can just sit up, but try lifting your legs," it checks their physical movement and muscle strength. This allows the monitoring department to constantly monitor the health status of elderly individuals and take necessary actions. Furthermore, the AI ​​can also detect signs of stress and anxiety by analyzing the tone of voice, facial expressions, and movements of elderly individuals. For example, if the tone of voice is low and the facial expression is gloomy, the AI ​​can use this information to determine that the elderly person may be mentally unstable and suggest appropriate actions. In addition, by using sensors that monitor vital signs such as heart rate, blood pressure, and body temperature, it is possible to understand their health status in more detail. This allows the monitoring department to contribute not only to daily health checks but also to the early detection and prevention of abnormalities.

[0031] The reminder unit provides schedule reminders to seniors based on their health status, which is checked by the monitoring unit. Specifically, the AI ​​reminds seniors about vaccination appointments and local events. For example, by sending a reminder such as, "Vaccination appointments are now open. We'll make a reservation for you," seniors can remember to take necessary steps. Also, by providing information such as, "There's an autumn course at the XX Senior Support Center," seniors can increase their opportunities to participate in local events and activities. Furthermore, the reminder unit can also remind seniors about medication times and regular health check schedules. For example, by sending a notification such as, "It's time for your medication. Don't forget to take it," seniors can be supported in taking their medication correctly. In this way, the reminder unit can provide support to seniors in remembering necessary steps in their daily lives and maintaining their health.

[0032] The bridging department acts as a bridge between the elderly and the government, based on schedules reminded by the reminder department. Specifically, the AI ​​communicates with the government on behalf of the elderly and supports them with necessary procedures. For example, it can not only provide information such as, "There are autumn courses at the XX Longevity Support Center," but can also handle the course reservation process on their behalf. This eliminates the need for the elderly to handle complicated procedures themselves, allowing them to smoothly receive the services they need. The bridging department also plays a role in receiving notifications and communications from the government on behalf of the elderly and providing necessary information. For example, if there is an important notification from the government or a deadline for a procedure is approaching, the AI ​​will convey that information to the elderly and prompt them to take the necessary action. In this way, the bridging department supports the elderly in smoothly communicating with the government and completing all necessary procedures without fail. Furthermore, the bridging department can also collaborate with local volunteer groups and support services to ensure that the elderly receive the support they need.

[0033] The reporting unit makes emergency calls and contacts based on information relayed by the bridging unit. Specifically, the AI ​​monitors the health status and environmental changes of elderly individuals in real time, and immediately makes an emergency call if an abnormality is detected. For example, if an elderly person falls or suddenly becomes ill, the AI ​​will take emergency action such as "ALERT! Contact counseling or care manager" and notify ambulance and the police. It will also contact relatives and friends at the same time to encourage a quick response. In this way, the reporting unit ensures that elderly individuals can live with peace of mind even in emergencies. Furthermore, the reporting unit can also conduct regular welfare checks even outside of emergencies. For example, by asking elderly individuals "How are you?" at a set time every day, it checks on their well-being and checks for any abnormalities. In this way, the reporting unit can always ensure the safety of elderly individuals and respond quickly and appropriately in emergencies. In addition to emergency response, the reporting unit also provides daily support, enabling elderly individuals to live with peace of mind.

[0034] The monitoring unit can check the health status through conversations with AI and monitoring of physical and mental health. For example, the monitoring unit's AI might ask the elderly person, "Have you been sleeping well lately?" or suggest exercises such as, "You can just sit up, but try lifting your legs." This allows the monitoring unit to constantly understand the health status of the elderly person and take necessary actions. Some or all of the above-mentioned processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to analyze the content of conversations using speech recognition technology, taking the elderly person's voice data as input, and check their health status.

[0035] The reminder function can remind users about vaccination appointments and information about local events. For example, the AI ​​can send reminders such as, "Vaccination appointments are now open. I'll make a reservation for you," or provide information such as, "There's an autumn course at the XX Senior Support Center." This ensures that elderly individuals remember to take necessary steps. Some or all of the above-mentioned processes in the reminder function may be performed using AI or not. For example, the reminder function can use AI to take the elderly person's schedule data as input, generate reminder content, and send reminders via voice or text.

[0036] The bridging unit can communicate with government agencies and support necessary procedures. For example, the bridging unit can use AI to communicate with government agencies and provide information such as, "There are autumn courses at the XX Senior Support Center." This allows the bridging unit to smoothly carry out necessary procedures for the elderly. Some or all of the above-mentioned processes in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take the elderly person's schedule data as input, generate communication content for government agencies, and make contact via email or telephone.

[0037] The emergency department can make emergency calls to ambulance services and the police, and contact relatives and friends in times of emergency. For example, the emergency department's AI can perform emergency responses such as "ALART! Contact counseling or care managers." This allows the emergency department to ensure that elderly people can live with peace of mind even in emergencies. Some or all of the above processes in the emergency department may be performed using AI or not. For example, the emergency department can use AI to take emergency data of elderly people as input, generate emergency call content, and make calls to ambulance services and the police.

[0038] The monitoring unit can analyze the elderly person's past health data and select the optimal monitoring method. For example, the monitoring unit can analyze the elderly person's past blood pressure data to determine the frequency of blood pressure measurement. It can also analyze the elderly person's past exercise data to select an exercise monitoring method. Furthermore, it can analyze the elderly person's past sleep data to select a sleep monitoring method. This allows the monitoring unit to select the optimal monitoring method by analyzing the elderly person's past health data, enabling more effective health management. Some or all of the above processing in the monitoring unit may be performed using AI, or without AI. For example, the monitoring unit can use AI to select the optimal monitoring method using data analysis techniques, with the AI ​​taking the elderly person's past health data as input.

[0039] The monitoring unit can assess the health status of elderly individuals based on their lifestyle and dietary habits during monitoring. For example, the monitoring unit can record the elderly individual's diet and evaluate its nutritional balance. It can also record the elderly individual's lifestyle and evaluate their exercise level and rest time. Furthermore, it can record the elderly individual's drinking and smoking habits and assess their health risks. This allows the monitoring unit to provide more accurate health management by assessing the health status of elderly individuals based on their lifestyle and dietary habits. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to evaluate the nutritional balance of an elderly individual's dietary data as input.

[0040] The monitoring unit can assess region-specific health risks based on the geographical location information of elderly individuals during monitoring. For example, the monitoring unit can assess health risks based on climate information of the area where the elderly person lives. It can also assess health risks based on access information to medical facilities in the area where the elderly person lives. Furthermore, the monitoring unit can assess health risks based on environmental pollution information of the area where the elderly person lives. As a result, the monitoring unit can provide more appropriate health management by assessing region-specific health risks based on the geographical location information of elderly individuals. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to evaluate region-specific health risks using the geographical location data of elderly individuals as input.

[0041] The monitoring unit can analyze the social media activity of elderly individuals during monitoring and obtain information related to their health status. For example, the monitoring unit can analyze elderly individuals' social media posts and assess their stress levels. It can also analyze elderly individuals' social media friendships and assess the risk of social isolation. Furthermore, the monitoring unit can analyze the frequency of elderly individuals' social media activity and assess their mental health status. As a result, by analyzing the social media activity of elderly individuals, the monitoring unit can obtain information related to their health status and enable more appropriate health management. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to take elderly individuals' social media data as input and obtain information related to their health status using data analysis techniques.

[0042] The reminder unit can adjust the level of detail in reminders based on the importance of the schedule. For example, it will provide a detailed reminder for important medical appointments. It can also provide a concise reminder for routine appointments. Furthermore, it can repeatedly remind users of appointments that elderly individuals tend to forget. This allows the reminder unit to provide more appropriate reminders by adjusting the level of detail based on the importance of the schedule. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can use AI to take the elderly person's schedule data as input, evaluate the importance of the schedule, and adjust the level of detail in the reminder.

[0043] The reminder function can apply different reminder methods depending on the schedule category. For example, for medical appointments, the reminder function can provide voice reminders. For social events, it can also provide text reminders. Furthermore, for daily tasks, it can provide visual reminders. This allows the reminder function to provide more appropriate reminders by applying different reminder methods depending on the schedule category. Some or all of the above processing in the reminder function may be performed using AI or not. For example, the reminder function can use AI to take elderly people's schedule data as input, classify the schedule categories, and select an appropriate reminder method.

[0044] The reminder unit can adjust the frequency of reminders based on the submission date of the schedule. For example, the reminder unit will send frequent reminders when the submission deadline is approaching. It can also reduce the frequency of reminders when the submission deadline is far away. Furthermore, it can send reminders at a moderate frequency when the submission deadline is moderately far away. In this way, the reminder unit can provide more appropriate reminders by adjusting the frequency of reminders based on the submission date of the schedule. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can use AI to take the schedule data of an elderly person as input, evaluate the submission date, and adjust the frequency of reminders.

[0045] The reminder unit can adjust the order of reminders based on the relevance of the schedule. For example, it can remind users of important appointments first. It can also postpone less relevant appointments. Furthermore, it can prioritize reminders of appointments that are of high interest to the elderly. This allows the reminder unit to provide more appropriate reminders by adjusting the order of reminders based on the relevance of the schedule. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can use AI to take the elderly person's schedule data as input, evaluate the relevance of the schedules, and adjust the order of reminders.

[0046] The bridging unit can adjust the level of detail in communication based on the importance of the administrative procedure during the bridging process. For example, the bridging unit will provide detailed communication for important administrative procedures. It can also provide concise communication for routine administrative procedures. Furthermore, the bridging unit can repeatedly contact elderly individuals for procedures they tend to forget. This allows the bridging unit to provide more appropriate communication by adjusting the level of detail based on the importance of the administrative procedure. Some or all of the above processing in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take the elderly person's procedure data as input, evaluate the importance of the procedure, and adjust the level of detail in communication.

[0047] The bridging unit can apply different communication methods depending on the category of administrative procedure during the bridging process. For example, in the case of medical-related procedures, the bridging unit can use voice communication. In the case of social security-related procedures, the bridging unit can also use text communication. Furthermore, in the case of routine procedures, the bridging unit can use visual communication. This allows the bridging unit to provide more appropriate communication by applying different communication methods depending on the category of administrative procedure. Some or all of the above processing in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take the procedure data of elderly people as input, classify the procedure category, and select the appropriate communication method.

[0048] The bridging unit can adjust the frequency of contact based on the timing of the submission of administrative procedures during the bridging process. For example, the bridging unit will contact more frequently when the submission deadline is approaching. Conversely, the bridging unit can also reduce the frequency of contact when the submission deadline is far away. Furthermore, the bridging unit can contact at a moderate frequency when the submission deadline is moderate. This allows the bridging unit to provide more appropriate communication by adjusting the frequency of contact based on the timing of the submission of administrative procedures. Some or all of the above processing in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take the procedure data of elderly people as input, evaluate the submission timing, and adjust the frequency of contact.

[0049] The bridging unit can adjust the order of contact based on the relevance of administrative procedures during the bridging process. For example, the bridging unit may contact important procedures first. It can also postpone less relevant procedures. Furthermore, the bridging unit may prioritize contacting procedures of high interest to the elderly. This allows the bridging unit to provide more appropriate contact by adjusting the order of contact based on the relevance of administrative procedures. Some or all of the above processing in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take the elderly person's procedure data as input, evaluate the relevance of the procedures, and adjust the order of contact.

[0050] The reporting unit can adjust the level of detail in a report based on the severity of the emergency. For example, if the emergency is highly urgent, the reporting unit will provide a detailed report. If the emergency is less urgent, the reporting unit can provide a concise report. Furthermore, if the emergency is of moderate urgency, the reporting unit can provide a report with an appropriate level of detail. This allows the reporting unit to provide more appropriate reports by adjusting the level of detail based on the severity of the emergency. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to take emergency data of elderly people as input, evaluate the urgency, and adjust the level of detail in the report.

[0051] The reporting unit can apply different reporting methods depending on the category of the emergency. For example, in the case of a medical emergency, the reporting unit will call for an ambulance. In the case of a fire, the reporting unit can also call the fire department. Furthermore, in the case of a crime, the reporting unit can also call the police. This allows the reporting unit to make more appropriate reports by applying different reporting methods depending on the category of the emergency. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to take emergency data of elderly people as input, classify the category of the emergency, and select the appropriate reporting method.

[0052] The reporting unit can adjust the frequency of reporting based on when the emergency occurred. For example, the reporting unit may report frequently immediately after an emergency occurs. It can also reduce the frequency of reporting as time has passed since the emergency occurred. Furthermore, if the emergency continues, it can report at an appropriate frequency. This allows the reporting unit to provide more appropriate reports by adjusting the frequency of reporting based on when the emergency occurred. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit may use AI to take emergency data of the elderly as input, evaluate the timing of the occurrence, and adjust the frequency of reporting.

[0053] The reporting system can adjust the order of notifications based on the relevance of the emergencies. For example, the reporting system will report important emergencies first. It can also postpone reporting less relevant emergencies. Furthermore, the reporting system can prioritize reporting emergencies of high interest to the elderly. This allows the reporting system to make more appropriate notifications by adjusting the order of notifications based on the relevance of the emergencies. Some or all of the above processing in the reporting system may be performed using AI or not. For example, the reporting system can use AI to take emergency data of the elderly as input, evaluate the relevance of the emergencies, and adjust the order of notifications.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The elderly support system may also include a "hobby support section." This section can provide relevant information and activities based on the elderly person's hobbies and interests. For example, if an elderly person is interested in gardening, the hobby support section can provide seasonal gardening advice and information about local gardening clubs. If an elderly person enjoys reading, the hobby support section can also provide recommendations for new books and information about events at local libraries. Furthermore, if an elderly person is interested in handicrafts, the hobby support section can provide information about handicraft workshops and online lessons. In this way, the hobby support section can help elderly people lead fulfilling lives through their hobbies. Some or all of the above processing in the hobby support section may be performed using AI, or not. For example, the hobby support section could use AI to take the elderly person's hobby data as input, generate relevant information, and provide it in voice or text format.

[0056] The elderly support system may also include a "communication promotion unit." This unit can provide support to increase opportunities for elderly people to interact with others. For example, it could provide event information for elderly people to interact with their neighbors. It could also provide a platform for elderly people to interact with other elderly people online. Furthermore, it could set reminders for elderly people to regularly contact family and friends. In this way, the communication promotion unit can help elderly people avoid isolation and maintain social connections. Some or all of the above processes in the communication promotion unit may be performed using AI or not. For example, the communication promotion unit could use AI to take elderly people's interaction data as input and suggest appropriate interaction opportunities.

[0057] The elderly support system may also include an "exercise support unit." This unit can provide information and advice to support the exercise habits of the elderly. For example, it can introduce simple exercises that the elderly can do at home. It can also provide information on local exercise events and classes that the elderly can participate in. Furthermore, it can record the elderly's exercise data and monitor their progress. This allows the exercise support unit to support the elderly in maintaining their exercise habits for good health. Some or all of the above-described processes in the exercise support unit may be performed using AI or not. For example, the exercise support unit could use AI to input the elderly's exercise data and propose an appropriate exercise plan.

[0058] The elderly support system may also include a "meal support section." This section can provide information and advice to support the eating habits of the elderly. For example, it can provide recipes for balanced meals. It can also provide information on local meal services and meal delivery services available to the elderly. Furthermore, it can record the elderly's meal data and monitor their nutritional balance. This allows the meal support section to support the elderly in maintaining healthy eating habits. Some or all of the above processes in the meal support section may be performed using AI or not. For example, the meal support section could use AI to input the elderly's meal data and propose an appropriate meal plan.

[0059] The elderly support system may also include a "safety verification unit." The safety verification unit can verify the safety of the elderly person's living environment and take necessary actions. For example, the safety verification unit can detect dangerous areas in the elderly person's home and provide advice for improvement. It can also suggest safe routes for the elderly person when they go out. Furthermore, the safety verification unit can monitor the security system of the elderly person's home and notify if an anomaly is detected. In this way, the safety verification unit can help the elderly person live safely. Some or all of the above processes in the safety verification unit may be performed using AI or not. For example, the safety verification unit can use AI to take data on the elderly person's living environment as input, detect dangerous areas, and provide advice for improvement.

[0060] The elderly support system may also include a "Medical Collaboration Department." This department can collaborate with medical institutions to provide appropriate medical services by linking the health data of elderly individuals. For example, the Medical Collaboration Department can share the health data of elderly individuals with doctors and conduct regular health checkups. It can also support elderly individuals in making appointments for necessary medical services. Furthermore, the Medical Collaboration Department can provide advice from medical institutions based on the health data of elderly individuals. This allows the Medical Collaboration Department to support elderly individuals in receiving appropriate medical services. Some or all of the above-described processes in the Medical Collaboration Department may be performed using AI, or not. For example, the Medical Collaboration Department could use AI as input for elderly individuals' health data and collaborate with medical institutions to provide appropriate medical services.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The monitoring unit checks the health status. For example, it checks the health status through conversations with AI and monitoring of physical and mental condition. The AI ​​may ask the elderly, "Have you been sleeping well lately?" or suggest exercises such as, "You can just sit up, but try lifting your legs." This allows the monitoring unit to constantly understand the health status of the elderly and take necessary actions. Step 2: The reminder unit reminds users of their schedules based on the health status checked by the monitoring unit. For example, it reminds them of vaccination appointments and information about local events. The AI ​​might send reminders such as, "Vaccination appointments are now open. I'll make a reservation for you," or provide information such as, "There's an autumn course at the XX Longevity Support Center." This allows the reminder unit to ensure that elderly individuals don't forget to take necessary steps. Step 3: The bridging unit acts as a bridge to the government based on the schedule reminded by the reminder unit. For example, it contacts the government and supports necessary procedures. The AI ​​contacts the government and provides information such as, "There is an autumn course at the XX Longevity Support Center." This allows the bridging unit to smoothly carry out the necessary procedures for the elderly. Step 4: The reporting unit makes emergency calls and contacts based on the information relayed by the bridging unit. For example, it will call emergency services and the police in an emergency, and contact relatives and friends. The AI ​​will perform emergency responses such as "ALART! Contact counseling or care managers." This allows the reporting unit to ensure that elderly people can live with peace of mind even in emergencies.

[0063] (Example of form 2) The elderly support system according to an embodiment of the present invention is a service for elderly people living alone, and is a system that supports the lives of elderly people using AI. This elderly support system checks the health status through conversation with the AI ​​and monitoring of physical and mental condition. Next, it provides schedule reminders and acts as a liaison with local government in the residential area. Furthermore, in emergencies, it makes announcements and contacts relatives and friends. In this way, it helps elderly people to live without anxiety. For example, the AI ​​may ask the elderly person, "Have you been sleeping well lately?" or suggest exercises such as, "You can just sit up, but try lifting your legs." This allows the system to constantly monitor the health status of the elderly person and take necessary actions. Next, the AI ​​may give reminders such as, "Reservations for vaccinations have started. I will make a reservation for you," or provide information such as, "There are autumn courses at the XX Longevity Support Center." This helps elderly people not forget necessary procedures. Furthermore, the AI ​​takes emergency action such as, "ALART! Contact counseling or a care manager." This allows elderly people to live with peace of mind even in emergencies. Through this mechanism, it is possible to support elderly people to live without anxiety. For example, AI can maintain the health of elderly people by constantly monitoring their health and taking necessary actions. It can also ensure that elderly people don't forget necessary procedures by providing schedule reminders and acting as a liaison with government agencies. Furthermore, in emergencies, it can make notifications and contact relatives and friends, allowing elderly people to live with peace of mind. Thus, elderly support systems can help elderly people live without anxiety by checking their health, providing schedule reminders, acting as a liaison with government agencies, and making notifications and contacts in emergencies.

[0064] The elderly support system according to this embodiment comprises a monitoring unit, a reminder unit, a bridging unit, and a notification unit. The monitoring unit checks the health status. The monitoring unit checks the health status, for example, through conversation with AI and monitoring of physical and mental state. The AI ​​may ask the elderly person, "Have you been sleeping well lately?" or suggest exercises, such as, "You can just sit up, but try lifting your legs." This allows the monitoring unit to constantly understand the health status of the elderly person and take necessary actions. The reminder unit reminds the elderly person of schedules based on the health status checked by the monitoring unit. The reminder unit may remind the elderly person, for example, about vaccination appointment bookings or information about local events. The AI ​​may make reminders such as, "Vaccination appointment bookings have started. I'll make a reservation for you," or provide information such as, "There's an autumn course at the XX Longevity Support Center." This allows the reminder unit to ensure that the elderly person does not forget to complete necessary procedures. The bridging unit acts as a liaison with the government based on the schedule reminded by the reminder unit. The bridging unit may contact the government and support necessary procedures, for example. The AI ​​communicates with government agencies and provides information such as, "There are autumn courses at the XX Longevity Support Center." This allows the liaison department to smoothly handle necessary procedures for the elderly. The reporting department makes emergency reports and contacts based on the information relayed by the liaison department. For example, the reporting department will contact emergency services and the police in an emergency and contact relatives and friends. The AI ​​will perform emergency responses such as, "ALART! Contact counseling or care managers." This allows the reporting department to ensure that the elderly can live with peace of mind even in emergencies. In this way, the elderly support system according to this embodiment can support the elderly so that they can live without anxiety by checking their health status, reminding them of schedules, liaising with government agencies, and making emergency reports and contacts.

[0065] The monitoring department collects information to check the health status of elderly individuals through conversations with AI and monitoring of their physical and mental state. Specifically, the AI ​​asks elderly individuals everyday questions to understand their health status. For example, by asking, "Have you been sleeping well lately?", it checks the quality and quantity of their sleep, and by suggesting simple exercises such as, "You can just sit up, but try lifting your legs," it checks their physical movement and muscle strength. This allows the monitoring department to constantly monitor the health status of elderly individuals and take necessary actions. Furthermore, the AI ​​can also detect signs of stress and anxiety by analyzing the tone of voice, facial expressions, and movements of elderly individuals. For example, if the tone of voice is low and the facial expression is gloomy, the AI ​​can use this information to determine that the elderly person may be mentally unstable and suggest appropriate actions. In addition, by using sensors that monitor vital signs such as heart rate, blood pressure, and body temperature, it is possible to understand their health status in more detail. This allows the monitoring department to contribute not only to daily health checks but also to the early detection and prevention of abnormalities.

[0066] The reminder unit provides schedule reminders to seniors based on their health status, which is checked by the monitoring unit. Specifically, the AI ​​reminds seniors about vaccination appointments and local events. For example, by sending a reminder such as, "Vaccination appointments are now open. We'll make a reservation for you," seniors can remember to take necessary steps. Also, by providing information such as, "There's an autumn course at the XX Senior Support Center," seniors can increase their opportunities to participate in local events and activities. Furthermore, the reminder unit can also remind seniors about medication times and regular health check schedules. For example, by sending a notification such as, "It's time for your medication. Don't forget to take it," seniors can be supported in taking their medication correctly. In this way, the reminder unit can provide support to seniors in remembering necessary steps in their daily lives and maintaining their health.

[0067] The bridging department acts as a bridge between the elderly and the government, based on schedules reminded by the reminder department. Specifically, the AI ​​communicates with the government on behalf of the elderly and supports them with necessary procedures. For example, it can not only provide information such as, "There are autumn courses at the XX Longevity Support Center," but can also handle the course reservation process on their behalf. This eliminates the need for the elderly to handle complicated procedures themselves, allowing them to smoothly receive the services they need. The bridging department also plays a role in receiving notifications and communications from the government on behalf of the elderly and providing necessary information. For example, if there is an important notification from the government or a deadline for a procedure is approaching, the AI ​​will convey that information to the elderly and prompt them to take the necessary action. In this way, the bridging department supports the elderly in smoothly communicating with the government and completing all necessary procedures without fail. Furthermore, the bridging department can also collaborate with local volunteer groups and support services to ensure that the elderly receive the support they need.

[0068] The reporting unit makes emergency calls and contacts based on information relayed by the bridging unit. Specifically, the AI ​​monitors the health status and environmental changes of elderly individuals in real time, and immediately makes an emergency call if an abnormality is detected. For example, if an elderly person falls or suddenly becomes ill, the AI ​​will take emergency action such as "ALERT! Contact counseling or care manager" and notify ambulance and the police. It will also contact relatives and friends at the same time to encourage a quick response. In this way, the reporting unit ensures that elderly individuals can live with peace of mind even in emergencies. Furthermore, the reporting unit can also conduct regular welfare checks even outside of emergencies. For example, by asking elderly individuals "How are you?" at a set time every day, it checks on their well-being and checks for any abnormalities. In this way, the reporting unit can always ensure the safety of elderly individuals and respond quickly and appropriately in emergencies. In addition to emergency response, the reporting unit also provides daily support, enabling elderly individuals to live with peace of mind.

[0069] The monitoring unit can check the health status through conversations with AI and monitoring of physical and mental health. For example, the monitoring unit's AI might ask the elderly person, "Have you been sleeping well lately?" or suggest exercises such as, "You can just sit up, but try lifting your legs." This allows the monitoring unit to constantly understand the health status of the elderly person and take necessary actions. Some or all of the above-mentioned processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to analyze the content of conversations using speech recognition technology, taking the elderly person's voice data as input, and check their health status.

[0070] The reminder function can remind users about vaccination appointments and information about local events. For example, the AI ​​can send reminders such as, "Vaccination appointments are now open. I'll make a reservation for you," or provide information such as, "There's an autumn course at the XX Senior Support Center." This ensures that elderly individuals remember to take necessary steps. Some or all of the above-mentioned processes in the reminder function may be performed using AI or not. For example, the reminder function can use AI to take the elderly person's schedule data as input, generate reminder content, and send reminders via voice or text.

[0071] The bridging unit can communicate with government agencies and support necessary procedures. For example, the bridging unit can use AI to communicate with government agencies and provide information such as, "There are autumn courses at the XX Senior Support Center." This allows the bridging unit to smoothly carry out necessary procedures for the elderly. Some or all of the above-mentioned processes in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take the elderly person's schedule data as input, generate communication content for government agencies, and make contact via email or telephone.

[0072] The emergency department can make emergency calls to ambulance services and the police, and contact relatives and friends in times of emergency. For example, the emergency department's AI can perform emergency responses such as "ALART! Contact counseling or care managers." This allows the emergency department to ensure that elderly people can live with peace of mind even in emergencies. Some or all of the above processes in the emergency department may be performed using AI or not. For example, the emergency department can use AI to take emergency data of elderly people as input, generate emergency call content, and make calls to ambulance services and the police.

[0073] The monitoring unit can estimate the emotions of elderly individuals and adjust the frequency of health checks based on the estimated emotions. For example, if an elderly individual is stressed, the AI ​​in the monitoring unit estimates their emotions and increases the frequency of health checks. Conversely, if an elderly individual is relaxed, the AI ​​in the monitoring unit can estimate their emotions and decrease the frequency of health checks. Furthermore, if an elderly individual is anxious, the AI ​​in the monitoring unit can estimate their emotions and appropriately adjust the frequency of health checks. This allows the monitoring unit to provide more appropriate health management by adjusting the frequency of health checks according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 monitoring unit may be performed using AI or not. For example, the monitoring unit can use facial expression data of elderly individuals as input, estimate their emotions using facial expression recognition technology, and adjust the frequency of health checks.

[0074] The monitoring unit can analyze the elderly person's past health data and select the optimal monitoring method. For example, the monitoring unit can analyze the elderly person's past blood pressure data to determine the frequency of blood pressure measurement. It can also analyze the elderly person's past exercise data to select an exercise monitoring method. Furthermore, it can analyze the elderly person's past sleep data to select a sleep monitoring method. This allows the monitoring unit to select the optimal monitoring method by analyzing the elderly person's past health data, enabling more effective health management. Some or all of the above processing in the monitoring unit may be performed using AI, or without AI. For example, the monitoring unit can use AI to select the optimal monitoring method using data analysis techniques, with the AI ​​taking the elderly person's past health data as input.

[0075] The monitoring unit can assess the health status of elderly individuals based on their lifestyle and dietary habits during monitoring. For example, the monitoring unit can record the elderly individual's diet and evaluate its nutritional balance. It can also record the elderly individual's lifestyle and evaluate their exercise level and rest time. Furthermore, it can record the elderly individual's drinking and smoking habits and assess their health risks. This allows the monitoring unit to provide more accurate health management by assessing the health status of elderly individuals based on their lifestyle and dietary habits. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to evaluate the nutritional balance of an elderly individual's dietary data as input.

[0076] The monitoring unit can estimate the emotions of elderly individuals and adjust the notification method of monitoring results based on the estimated emotions. For example, if an elderly individual is feeling stressed, the AI ​​can estimate their emotions and notify them of the monitoring results in gentle language. The monitoring unit can also estimate the emotions of elderly individuals when they are relaxed and notify them of detailed monitoring results. Furthermore, if an elderly individual is feeling anxious, the AI ​​can estimate their emotions and select a notification method that provides reassurance. This allows the monitoring unit to provide more appropriate notifications by adjusting the notification method of monitoring results according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to take the elderly individual's voice data as input, estimate their emotions using voice analysis technology, and adjust the notification method.

[0077] The monitoring unit can assess region-specific health risks based on the geographical location information of elderly individuals during monitoring. For example, the monitoring unit can assess health risks based on climate information of the area where the elderly person lives. It can also assess health risks based on access information to medical facilities in the area where the elderly person lives. Furthermore, the monitoring unit can assess health risks based on environmental pollution information of the area where the elderly person lives. As a result, the monitoring unit can provide more appropriate health management by assessing region-specific health risks based on the geographical location information of elderly individuals. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to evaluate region-specific health risks using the geographical location data of elderly individuals as input.

[0078] The monitoring unit can analyze the social media activity of elderly individuals during monitoring and obtain information related to their health status. For example, the monitoring unit can analyze elderly individuals' social media posts and assess their stress levels. It can also analyze elderly individuals' social media friendships and assess the risk of social isolation. Furthermore, the monitoring unit can analyze the frequency of elderly individuals' social media activity and assess their mental health status. As a result, by analyzing the social media activity of elderly individuals, the monitoring unit can obtain information related to their health status and enable more appropriate health management. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can use AI to take elderly individuals' social media data as input and obtain information related to their health status using data analysis techniques.

[0079] The reminder unit can estimate the emotions of elderly individuals and adjust the timing of reminders based on the estimated emotions. For example, if an elderly individual is feeling stressed, the AI ​​can estimate their emotions and delay the reminder. Conversely, if an elderly individual is relaxed, the AI ​​can estimate their emotions and advance the reminder. Furthermore, if an elderly individual is feeling anxious, the AI ​​can estimate their emotions and appropriately adjust the reminder timing. This allows the reminder unit to provide more appropriate reminders by adjusting the timing according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 reminder unit may be performed using AI or not. For example, the reminder unit can use AI to take the elderly individual's voice data as input, estimate their emotions using voice analysis technology, and adjust the reminder timing.

[0080] The reminder unit can adjust the level of detail in reminders based on the importance of the schedule. For example, it will provide a detailed reminder for important medical appointments. It can also provide a concise reminder for routine appointments. Furthermore, it can repeatedly remind users of appointments that elderly individuals tend to forget. This allows the reminder unit to provide more appropriate reminders by adjusting the level of detail based on the importance of the schedule. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can use AI to take the elderly person's schedule data as input, evaluate the importance of the schedule, and adjust the level of detail in the reminder.

[0081] The reminder function can apply different reminder methods depending on the schedule category. For example, for medical appointments, the reminder function can provide voice reminders. For social events, it can also provide text reminders. Furthermore, for daily tasks, it can provide visual reminders. This allows the reminder function to provide more appropriate reminders by applying different reminder methods depending on the schedule category. Some or all of the above processing in the reminder function may be performed using AI or not. For example, the reminder function can use AI to take elderly people's schedule data as input, classify the schedule categories, and select an appropriate reminder method.

[0082] The reminder unit can estimate the emotions of elderly individuals and determine the priority of reminders based on those estimated emotions. For example, if an elderly individual is feeling stressed, the AI ​​can estimate their emotions and prioritize important reminders. Conversely, if an elderly individual is relaxed, the AI ​​can estimate their emotions and distribute all reminders equally. Furthermore, if an elderly individual is feeling anxious, the AI ​​can estimate their emotions and prioritize reminders that provide reassurance. This allows the reminder unit to provide more appropriate reminders by prioritizing them according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reminder unit may be performed using AI or not. For example, the reminder unit can use AI to take an elderly individual's voice data as input, estimate their emotions using voice analysis technology, and determine the priority of reminders.

[0083] The reminder unit can adjust the frequency of reminders based on the submission date of the schedule. For example, the reminder unit will send frequent reminders when the submission deadline is approaching. It can also reduce the frequency of reminders when the submission deadline is far away. Furthermore, it can send reminders at a moderate frequency when the submission deadline is moderately far away. In this way, the reminder unit can provide more appropriate reminders by adjusting the frequency of reminders based on the submission date of the schedule. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can use AI to take the schedule data of an elderly person as input, evaluate the submission date, and adjust the frequency of reminders.

[0084] The reminder unit can adjust the order of reminders based on the relevance of the schedule. For example, it can remind users of important appointments first. It can also postpone less relevant appointments. Furthermore, it can prioritize reminders of appointments that are of high interest to the elderly. This allows the reminder unit to provide more appropriate reminders by adjusting the order of reminders based on the relevance of the schedule. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can use AI to take the elderly person's schedule data as input, evaluate the relevance of the schedules, and adjust the order of reminders.

[0085] The bridging unit can estimate the emotions of elderly individuals and adjust the method of communication with the government based on the estimated emotions. For example, if an elderly person is feeling stressed, the AI ​​in the bridging unit can estimate their emotions and select a concise method of communication. If the elderly person is relaxed, the AI ​​can also estimate their emotions and select a more detailed method of communication. Furthermore, if the elderly person is feeling anxious, the AI ​​can estimate their emotions and select a method of communication that provides reassurance. This allows the bridging unit to adjust the method of communication with the government according to the elderly person's emotions, enabling more appropriate communication. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 bridging unit may be performed using AI or not. For example, the bridging unit can use AI to estimate emotions using voice data from elderly individuals as input and adjust the method of communication using voice analysis technology.

[0086] The bridging unit can adjust the level of detail in communication based on the importance of the administrative procedure during the bridging process. For example, the bridging unit will provide detailed communication for important administrative procedures. It can also provide concise communication for routine administrative procedures. Furthermore, the bridging unit can repeatedly contact elderly individuals for procedures they tend to forget. This allows the bridging unit to provide more appropriate communication by adjusting the level of detail based on the importance of the administrative procedure. Some or all of the above processing in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take the elderly person's procedure data as input, evaluate the importance of the procedure, and adjust the level of detail in communication.

[0087] The bridging unit can apply different communication methods depending on the category of administrative procedure during the bridging process. For example, in the case of medical-related procedures, the bridging unit can use voice communication. In the case of social security-related procedures, the bridging unit can also use text communication. Furthermore, in the case of routine procedures, the bridging unit can use visual communication. This allows the bridging unit to provide more appropriate communication by applying different communication methods depending on the category of administrative procedure. Some or all of the above processing in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take the procedure data of elderly people as input, classify the procedure category, and select the appropriate communication method.

[0088] The bridging unit can estimate the emotions of elderly individuals and prioritize communications with government agencies based on these estimated emotions. For example, if an elderly individual is feeling stressed, the AI ​​can estimate their emotions and prioritize important communications. Conversely, if an elderly individual is relaxed, the AI ​​can estimate their emotions and distribute all communications equally. Furthermore, if an elderly individual is feeling anxious, the AI ​​can estimate their emotions and prioritize communications that provide reassurance. This allows the bridging unit to prioritize communications with government agencies according to the elderly individual's emotions, enabling more appropriate communication. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take an elderly individual's voice data as input, estimate their emotions using voice analysis technology, and determine the priority of communications.

[0089] The bridging unit can adjust the frequency of contact based on the timing of the submission of administrative procedures during the bridging process. For example, the bridging unit will contact more frequently when the submission deadline is approaching. Conversely, the bridging unit can also reduce the frequency of contact when the submission deadline is far away. Furthermore, the bridging unit can contact at a moderate frequency when the submission deadline is moderate. This allows the bridging unit to provide more appropriate communication by adjusting the frequency of contact based on the timing of the submission of administrative procedures. Some or all of the above processing in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take the procedure data of elderly people as input, evaluate the submission timing, and adjust the frequency of contact.

[0090] The bridging unit can adjust the order of contact based on the relevance of administrative procedures during the bridging process. For example, the bridging unit may contact important procedures first. It can also postpone less relevant procedures. Furthermore, the bridging unit may prioritize contacting procedures of high interest to the elderly. This allows the bridging unit to provide more appropriate contact by adjusting the order of contact based on the relevance of administrative procedures. Some or all of the above processing in the bridging unit may be performed using AI or not. For example, the bridging unit can use AI to take the elderly person's procedure data as input, evaluate the relevance of the procedures, and adjust the order of contact.

[0091] The reporting unit can estimate the emotions of elderly individuals and adjust the reporting method based on the estimated emotions. For example, if an elderly person is feeling stressed, the AI ​​can estimate their emotions and select a rapid reporting method. The reporting unit can also estimate the emotions of elderly individuals when they are relaxed and select a more detailed reporting method. Furthermore, if an elderly person is feeling anxious, the AI ​​can estimate their emotions and select a reassuring reporting method. This allows the reporting unit to provide more appropriate reporting by adjusting the reporting method according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to take elderly individuals' voice data as input, estimate their emotions using voice analysis technology, and adjust the reporting method.

[0092] The reporting unit can adjust the level of detail in a report based on the severity of the emergency. For example, if the emergency is highly urgent, the reporting unit will provide a detailed report. If the emergency is less urgent, the reporting unit can provide a concise report. Furthermore, if the emergency is of moderate urgency, the reporting unit can provide a report with an appropriate level of detail. This allows the reporting unit to provide more appropriate reports by adjusting the level of detail based on the severity of the emergency. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to take emergency data of elderly people as input, evaluate the urgency, and adjust the level of detail in the report.

[0093] The reporting unit can apply different reporting methods depending on the category of the emergency. For example, in the case of a medical emergency, the reporting unit will call for an ambulance. In the case of a fire, the reporting unit can also call the fire department. Furthermore, in the case of a crime, the reporting unit can also call the police. This allows the reporting unit to make more appropriate reports by applying different reporting methods depending on the category of the emergency. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to take emergency data of elderly people as input, classify the category of the emergency, and select the appropriate reporting method.

[0094] The reporting system can estimate the emotions of elderly individuals and prioritize reports based on those estimated emotions. For example, if an elderly person is stressed, the AI ​​can estimate their emotions and prioritize important reports. Alternatively, if an elderly person is relaxed, the AI ​​can estimate their emotions and distribute all reports equally. Furthermore, if an elderly person is anxious, the AI ​​can estimate their emotions and prioritize reports that provide reassurance. This allows the reporting system to prioritize reports according to the elderly person's emotions, enabling more appropriate reporting. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting system may be performed using AI or not. For example, the reporting system can use AI to take an elderly person's voice data as input, estimate their emotions using voice analysis technology, and determine the priority of reports.

[0095] The reporting unit can adjust the frequency of reporting based on when the emergency occurred. For example, the reporting unit may report frequently immediately after an emergency occurs. It can also reduce the frequency of reporting as time has passed since the emergency occurred. Furthermore, if the emergency continues, it can report at an appropriate frequency. This allows the reporting unit to provide more appropriate reports by adjusting the frequency of reporting based on when the emergency occurred. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit may use AI to take emergency data of the elderly as input, evaluate the timing of the occurrence, and adjust the frequency of reporting.

[0096] The reporting system can adjust the order of notifications based on the relevance of the emergencies. For example, the reporting system will report important emergencies first. It can also postpone reporting less relevant emergencies. Furthermore, the reporting system can prioritize reporting emergencies of high interest to the elderly. This allows the reporting system to make more appropriate notifications by adjusting the order of notifications based on the relevance of the emergencies. Some or all of the above processing in the reporting system may be performed using AI or not. For example, the reporting system can use AI to take emergency data of the elderly as input, evaluate the relevance of the emergencies, and adjust the order of notifications.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The elderly support system may also include a "hobby support section." This section can provide relevant information and activities based on the elderly person's hobbies and interests. For example, if an elderly person is interested in gardening, the hobby support section can provide seasonal gardening advice and information about local gardening clubs. If an elderly person enjoys reading, the hobby support section can also provide recommendations for new books and information about events at local libraries. Furthermore, if an elderly person is interested in handicrafts, the hobby support section can provide information about handicraft workshops and online lessons. In this way, the hobby support section can help elderly people lead fulfilling lives through their hobbies. Some or all of the above processing in the hobby support section may be performed using AI, or not. For example, the hobby support section could use AI to take the elderly person's hobby data as input, generate relevant information, and provide it in voice or text format.

[0099] The elderly support system may also include a "communication promotion unit." This unit can provide support to increase opportunities for elderly people to interact with others. For example, it could provide event information for elderly people to interact with their neighbors. It could also provide a platform for elderly people to interact with other elderly people online. Furthermore, it could set reminders for elderly people to regularly contact family and friends. In this way, the communication promotion unit can help elderly people avoid isolation and maintain social connections. Some or all of the above processes in the communication promotion unit may be performed using AI or not. For example, the communication promotion unit could use AI to take elderly people's interaction data as input and suggest appropriate interaction opportunities.

[0100] The elderly support system may also include an "emotional care unit." This unit can estimate the emotions of elderly individuals and provide appropriate care based on those estimates. For example, if an elderly person is feeling sad, the emotional care unit can use AI to estimate their emotions and send a message of encouragement. Similarly, if an elderly person is feeling joy, the emotional care unit can use AI to estimate their emotions and send a message of empathy. Furthermore, if an elderly person is feeling anxious, the emotional care unit can use AI to estimate their emotions and provide advice to help them relax. In this way, the emotional care unit can support the mental health of elderly individuals by providing appropriate care according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotional care unit may be performed using AI or not. For example, the emotional care unit can use AI to take an elderly person's voice data as input, use voice analysis technology to estimate their emotions, and provide appropriate care.

[0101] The elderly support system may also include an "exercise support unit." This unit can provide information and advice to support the exercise habits of the elderly. For example, it can introduce simple exercises that the elderly can do at home. It can also provide information on local exercise events and classes that the elderly can participate in. Furthermore, it can record the elderly's exercise data and monitor their progress. This allows the exercise support unit to support the elderly in maintaining their exercise habits for good health. Some or all of the above-described processes in the exercise support unit may be performed using AI or not. For example, the exercise support unit could use AI to input the elderly's exercise data and propose an appropriate exercise plan.

[0102] The elderly support system may also include a "meal support section." This section can provide information and advice to support the eating habits of the elderly. For example, it can provide recipes for balanced meals. It can also provide information on local meal services and meal delivery services available to the elderly. Furthermore, it can record the elderly's meal data and monitor their nutritional balance. This allows the meal support section to support the elderly in maintaining healthy eating habits. Some or all of the above processes in the meal support section may be performed using AI or not. For example, the meal support section could use AI to input the elderly's meal data and propose an appropriate meal plan.

[0103] The elderly support system may also include an "emotion monitoring unit." This unit can estimate the emotions of elderly individuals and take appropriate action based on the estimated emotions. For example, if an elderly person is feeling lonely, the AI ​​in the emotion monitoring unit can estimate their emotions and encourage them to contact friends and family. Similarly, if an elderly person is feeling joy, the AI ​​in the emotion monitoring unit can estimate their emotions and suggest ways to share those feelings. Furthermore, if an elderly person is feeling anxious, the AI ​​in the emotion monitoring unit can estimate their emotions and provide advice to help them relax. In this way, the emotion monitoring unit can support the mental health of elderly individuals by taking appropriate action according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotion monitoring unit may be performed using AI or not. For example, the emotion monitoring unit may use AI to estimate emotions using voice analysis technology, taking the elderly person's voice data as input, and take appropriate action.

[0104] The elderly support system may also include a "safety verification unit." The safety verification unit can verify the safety of the elderly person's living environment and take necessary actions. For example, the safety verification unit can detect dangerous areas in the elderly person's home and provide advice for improvement. It can also suggest safe routes for the elderly person when they go out. Furthermore, the safety verification unit can monitor the security system of the elderly person's home and notify if an anomaly is detected. In this way, the safety verification unit can help the elderly person live safely. Some or all of the above processes in the safety verification unit may be performed using AI or not. For example, the safety verification unit can use AI to take data on the elderly person's living environment as input, detect dangerous areas, and provide advice for improvement.

[0105] The elderly support system may also include an "emotional reminder unit." This unit can estimate the elderly person's emotions and adjust the content and timing of reminders based on the estimated emotions. For example, if the elderly person is stressed, the emotional reminder unit's AI can estimate their emotions and reduce the content of the reminder. If the elderly person is relaxed, the AI ​​can estimate their emotions and make the reminder more detailed. Furthermore, if the elderly person is anxious, the AI ​​can estimate their emotions and adjust the timing of the reminder. This allows the emotional reminder unit to provide more appropriate reminders by adjusting the content and timing according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotional reminder unit may be performed using AI or not. For example, the emotional reminder unit uses AI to take voice data from elderly people as input, estimates their emotions using voice analysis technology, and adjusts the content and timing of the reminder accordingly.

[0106] The elderly support system may also include a "Medical Collaboration Department." This department can collaborate with medical institutions to provide appropriate medical services by linking the health data of elderly individuals. For example, the Medical Collaboration Department can share the health data of elderly individuals with doctors and conduct regular health checkups. It can also support elderly individuals in making appointments for necessary medical services. Furthermore, the Medical Collaboration Department can provide advice from medical institutions based on the health data of elderly individuals. This allows the Medical Collaboration Department to support elderly individuals in receiving appropriate medical services. Some or all of the above-described processes in the Medical Collaboration Department may be performed using AI, or not. For example, the Medical Collaboration Department could use AI as input for elderly individuals' health data and collaborate with medical institutions to provide appropriate medical services.

[0107] The elderly support system may also include an "emotion notification unit." This unit can estimate the emotions of elderly individuals and adjust the content and method of notification based on the estimated emotions. For example, if an elderly person is feeling stressed, the AI ​​can estimate their emotions and provide a prompt notification. If the elderly person is relaxed, the AI ​​can estimate their emotions and provide a more detailed notification. Furthermore, if the elderly person is feeling anxious, the AI ​​can estimate their emotions and provide a reassuring notification. This allows the emotion notification unit to provide more appropriate notifications by adjusting the content and method of notification according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotion notification unit may be performed using AI or not. For example, the emotion notification unit can use AI to take the elderly person's voice data as input, estimate their emotions using voice analysis technology, and adjust the content and method of notification.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The monitoring unit checks the health status. For example, it checks the health status through conversations with AI and monitoring of physical and mental condition. The AI ​​may ask the elderly, "Have you been sleeping well lately?" or suggest exercises such as, "You can just sit up, but try lifting your legs." This allows the monitoring unit to constantly understand the health status of the elderly and take necessary actions. Step 2: The reminder unit reminds users of their schedules based on the health status checked by the monitoring unit. For example, it reminds them of vaccination appointments and information about local events. The AI ​​might send reminders such as, "Vaccination appointments are now open. I'll make a reservation for you," or provide information such as, "There's an autumn course at the XX Longevity Support Center." This allows the reminder unit to ensure that elderly individuals don't forget to take necessary steps. Step 3: The bridging unit acts as a bridge to the government based on the schedule reminded by the reminder unit. For example, it contacts the government and supports necessary procedures. The AI ​​contacts the government and provides information such as, "There is an autumn course at the XX Longevity Support Center." This allows the bridging unit to smoothly carry out the necessary procedures for the elderly. Step 4: The reporting unit makes emergency calls and contacts based on the information relayed by the bridging unit. For example, it will call emergency services and the police in an emergency, and contact relatives and friends. The AI ​​will perform emergency responses such as "ALART! Contact counseling or care managers." This allows the reporting unit to ensure that elderly people can live with peace of mind even in emergencies.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the monitoring unit, reminder unit, bridging unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit checks the health status of the elderly person using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A constantly monitors their health status. The reminder unit is implemented in the specific processing unit 290 of the data processing unit 12 and reminds the person of their schedule based on their health status. The bridging unit is implemented in the specific processing unit 46A of the smart device 14 and communicates with the government. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes emergency notifications and communications. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the monitoring unit, reminder unit, bridging unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit checks the health status of the elderly person using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A constantly monitors their health status. The reminder unit is implemented in the specific processing unit 290 of the data processing unit 12 and reminds the person of their schedule based on their health status. The bridging unit is implemented in the specific processing unit 46A of the smart glasses 214 and communicates with the government. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes emergency notifications and communications. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the monitoring unit, reminder unit, bridging unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit checks the health status of the elderly person using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A constantly monitors their health status. The reminder unit is implemented in the specific processing unit 290 of the data processing unit 12 and reminds the person of their schedule based on their health status. The bridging unit is implemented in the specific processing unit 46A of the headset terminal 314 and communicates with the government. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes emergency notifications and communications. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the monitoring unit, reminder unit, bridging unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit checks the health status of the elderly person using the camera 42 and microphone 238 of the robot 414, and the control unit 46A constantly monitors their health status. The reminder unit is implemented in the specific processing unit 290 of the data processing unit 12 and reminds the person of their schedule based on their health status. The bridging unit is implemented in the specific processing unit 46A of the robot 414 and communicates with the government. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes emergency notifications and communications. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) The monitoring unit checks the health status, A reminder unit that reminds the schedule based on the health status checked by the monitoring unit, A bridging unit that acts as a liaison with the government based on the schedule reminded by the aforementioned reminder unit, The system includes a notification unit that makes emergency notifications and communications based on the information transmitted by the aforementioned bridging unit. A system characterized by the following features. (Note 2) The monitoring unit, Check your health status through conversations with AI and monitoring of your physical and mental state. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reminder unit, Reminds you of vaccination appointment scheduling and information about local events. The system described in Appendix 1, characterized by the features described herein. (Note 4) The bridging section is, We will contact the government and support you with the necessary procedures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting unit, In an emergency, contact emergency services and the police, and get in touch with relatives and friends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, The system estimates the emotions of older adults and adjusts the frequency of health checks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, Analyze past health data of elderly individuals to select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, During monitoring, the health status of elderly individuals is assessed based on their lifestyle and dietary habits. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, The system estimates the emotions of older adults and adjusts the notification method of monitoring results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, During monitoring, region-specific health risks are assessed based on the geographical location information of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, During monitoring, we analyze the social media activity of older adults and obtain information related to their health status. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reminder unit, The system estimates the emotions of elderly individuals and adjusts the timing of reminders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reminder unit, When sending reminders, adjust the level of detail based on the importance of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reminder unit, When sending reminders, apply different reminder methods depending on the schedule category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reminder unit, The system estimates the emotions of elderly individuals and prioritizes reminders based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reminder unit, When sending reminders, adjust the frequency of reminders based on the submission date of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reminder unit, When sending reminders, adjust the order of reminders based on their relevance to the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 18) The bridging section is, The system estimates the emotions of elderly people and adjusts communication methods with the government based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The bridging section is, During the transition process, the level of detail in communication will be adjusted based on the importance of the administrative procedures. The system described in Appendix 1, characterized by the features described herein. (Note 20) The bridging section is, During the bridging process, different contact methods will be applied depending on the category of administrative procedure. The system described in Appendix 1, characterized by the features described herein. (Note 21) The bridging section is, The system estimates the emotions of elderly individuals and prioritizes communication with government agencies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The bridging section is, During the transition process, the frequency of communication will be adjusted based on the timing of submission of administrative procedures. The system described in Appendix 1, characterized by the features described herein. (Note 23) The bridging section is, During the bridging process, the order of contacts will be adjusted based on the relevance of the administrative procedures. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, The system estimates the emotions of elderly individuals and adjusts the reporting method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, When reporting an emergency, adjust the level of detail in the report based on the severity of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, When making a report, different reporting methods will be applied depending on the category of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting unit, The system estimates the emotions of elderly individuals and prioritizes reporting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reporting unit, When reporting an emergency, adjust the frequency of reporting based on when the emergency occurred. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reporting unit, When a report is made, the order of reports will be adjusted based on the relevance of the emergency. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The monitoring unit checks the health status, A reminder unit that reminds the schedule based on the health status checked by the monitoring unit, A bridging unit that acts as a liaison with the government based on the schedule reminded by the aforementioned reminder unit, The system includes a notification unit that makes emergency notifications and communications based on the information transmitted by the aforementioned bridging unit. A system characterized by the following features.

2. The monitoring unit, Check your health status through conversations with AI and monitoring of your physical and mental state. The system according to feature 1.

3. The aforementioned reminder unit, Reminds you of vaccination appointment scheduling and information about local events. The system according to feature 1.

4. The bridging section is, We will contact the government and support you with the necessary procedures. The system according to feature 1.

5. The aforementioned reporting unit, In an emergency, contact emergency services and the police, and get in touch with relatives and friends. The system according to feature 1.

6. The monitoring unit, The system estimates the emotions of older adults and adjusts the frequency of health checks based on those estimated emotions. The system according to feature 1.

7. The monitoring unit, Analyze past health data of elderly individuals to select the optimal monitoring method. The system according to feature 1.

8. The monitoring unit, During monitoring, the health status of elderly individuals is assessed based on their lifestyle and dietary habits. The system according to feature 1.

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