system

Abstract

The system according to this embodiment aims to support home care for the elderly by monitoring their health status, detecting abnormalities, and sending alerts. [Solution] The system according to the embodiment comprises a collection unit, an alert unit, a management unit, and a suggestion unit. The collection unit collects health status data. The alert unit detects abnormalities based on the data collected by the collection unit and sends an alert. The management unit generates a health report and sets a reminder based on the data collected by the collection unit. The suggestion unit proposes a care protocol based on the data collected by the collection unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

[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 conventional technology, in-home care for the elderly does not sufficiently monitor the health status, detect abnormalities, and propose care protocols, etc., and there is room for improvement.

[0005] The system according to the embodiment aims to monitor the health status, detect abnormalities, and send alerts to support in-home care for the elderly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an alert unit, a management unit, and a proposal unit. The data collection unit collects health status data. The alert unit detects abnormalities based on the data collected by the data collection unit and sends an alert. The management unit generates a health report and sets reminders based on the data collected by the data collection unit. The proposal unit proposes a care protocol based on the data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the health status of elderly people to support home care, detect abnormalities, and send alerts. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple 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) An autonomous home assistant system according to an embodiment of the present invention is a system that supports home care for the elderly using generative AI and communication technology. This system utilizes a generative AI agent to monitor the health status of the person receiving care and provides comprehensive care support by interconnecting caregivers with medical and care-related institutions. For example, as a health status monitoring and alert function, it detects abnormalities based on data obtained from sensors and immediately sends alerts to family members and medical professionals in emergencies. It monitors blood pressure, heart rate, body temperature, activity level, etc. in real time. Next, as daily health management, it generates regular health reports to track the health status of the person receiving care and share them with family members. It also sets reminders for medication times and medical appointments to help ensure that the person receives the necessary care. Furthermore, as automated care support, it proposes care protocols adapted based on the health status of the person receiving care and supports the optimal care method. It manages a list of care-related tasks and tracks their progress. As communication support, it provides video call and chat functions with medical professionals, enabling mutual communication between caregivers and medical providers in the event of an emergency. As part of mental health support, it checks the stress levels of care recipients and their families and provides resources for relaxation and counseling. It also features a voice interface that allows for easy instruction and questioning. Finally, as part of personalized care planning, it provides individualized care plans based on the health status and needs of care recipients. The generated AI learns from user feedback and data to provide more accurate support. These functions support a safe and comfortable life for the elderly, enabling them to continue living at home with peace of mind. It also reduces the burden on caregivers and enables 24-hour monitoring and emergency response. In this way, the autonomous home assistant system can comprehensively support home care for the elderly and provide a safe and comfortable life.

[0029] The autonomous home assistant system according to this embodiment comprises a data collection unit, an alert unit, a management unit, and a suggestion unit. The data collection unit collects health status data. Health status data includes, but is not limited to, heart rate, blood pressure, and body temperature. The data collection unit can, for example, measure heart rate using a heart rate sensor. The data collection unit can also measure blood pressure using a blood pressure sensor. The data collection unit can also measure body temperature using a body temperature sensor. For example, the data collection unit measures heart rate in real time using a heart rate sensor and collects the data. The blood pressure sensor measures blood pressure periodically and collects the data. The body temperature sensor continuously measures body temperature and collects the data. The alert unit detects abnormalities based on the data collected by the data collection unit and sends an alert. Abnormalities include, but are not limited to, sudden changes in heart rate and abnormal blood pressure values. The alert unit can, for example, detect sudden changes in heart rate and send an alert. The alert unit can also detect abnormal blood pressure values ​​and send an alert. The alert unit can also detect abnormal body temperature and send alerts. For example, the alert unit sends an alert if the heart rate increases rapidly, if blood pressure is abnormally high, or if body temperature is abnormally high. The management unit generates health reports and sets reminders based on the data collected by the collection unit. Health reports include, but are not limited to, daily reports, weekly reports, and graphs. For example, the management unit generates daily reports to track the health status of care recipients. The management unit can also generate weekly reports to share health status with family members. The management unit can also visually display health status using graphs. For example, the management unit generates daily reports to record the health status of care recipients in detail. Weekly reports track changes in health status and share them with family members. Graphs visually display health data to make it easier to understand. The suggestion unit proposes care protocols based on the data collected by the collection unit. Care protocols include, but are not limited to, dietary management and exercise programs.The suggestion unit can, for example, propose a meal management protocol based on the health condition of the person requiring care. The suggestion unit can also propose an exercise program. Furthermore, the suggestion unit can propose a care protocol tailored to the person's health condition. For example, the suggestion unit can propose a balanced meal plan based on the person requiring care. The exercise program can propose appropriate exercises for maintaining health. The care protocol tailored to the health condition addresses individual needs. As a result, the autonomous home assistant system according to this embodiment can collect health data, detect abnormalities, send alerts, generate health reports, set reminders, and propose care protocols.

[0030] The data collection unit collects health status data. This health status data includes, but is not limited to, heart rate, blood pressure, and body temperature. For example, the data collection unit measures heart rate using a heart rate sensor. It can also measure blood pressure using a blood pressure sensor. It can also measure body temperature using a body temperature sensor. Specifically, the data collection unit measures heart rate in real time using a heart rate sensor and collects the data. The heart rate sensor is designed as a wearable device that is attached to the skin and can detect heart rate fluctuations with high accuracy. The blood pressure sensor measures blood pressure periodically and collects the data. Blood pressure sensors come in types that are attached to the upper arm or wrist and are designed with user comfort in mind. The body temperature sensor continuously measures body temperature and collects the data. The body temperature sensor is equipped with a highly sensitive temperature sensor in the part that contacts the skin and can accurately detect even minute temperature changes. As a result, the data collection unit collects health data such as heart rate, blood pressure, and body temperature in real time and transmits it to a central database. The collected data is stored on a cloud server, making it accessible to other departments. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, if changes in health are predicted, increasing the data collection frequency allows for a more detailed understanding of health status. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The alert unit detects anomalies based on data collected by the data collection unit and sends an alert. Anomalies include, but are not limited to, rapid changes in heart rate and abnormal blood pressure values. For example, the alert unit detects rapid changes in heart rate and sends an alert. Specifically, the alert unit analyzes the collected heart rate data in real time to detect rapid increases or decreases. If the heart rate exceeds a certain range, the alert unit immediately sends an alert. It can also detect abnormal blood pressure values ​​and send an alert. Blood pressure data is periodically transmitted from the data collection unit, and the alert unit analyzes it to detect abnormal values. For example, if blood pressure exceeds the normal range, the alert unit sends an alert. It can also detect abnormal body temperature and send an alert. Body temperature data is continuously transmitted from the data collection unit, and the alert unit analyzes it to detect abnormally high or low temperatures. For example, if body temperature is abnormally high, the alert unit sends an alert. Alerts are sent to the user's smartphone or home device and communicated to the user through voice, vibration, and visual warnings. This allows the alert unit to quickly detect abnormal health conditions and prompt users to take appropriate action. Furthermore, the alert unit can prioritize alerts according to the type and urgency of the abnormality, and notify important alerts first. In this way, the alert unit can support users' health management and encourage quick and appropriate action.

[0032] The management department generates health reports and sets reminders based on data collected by the collection department. Health reports include, but are not limited to, daily reports, weekly reports, and graphs. For example, the management department generates daily reports to track the health status of care recipients. Specifically, the management department analyzes the collected data and compiles it into daily reports. Daily reports include fluctuations and abnormalities in heart rate, blood pressure, and body temperature, recording the care recipient's health status in detail. Weekly reports track changes in health status and can also be shared with family members. Weekly reports aggregate the data from daily reports to show weekly changes and trends in health status. This allows family members to understand the care recipient's health status and consider necessary actions. Graphs visually display health data, making it easier to understand. The management department graphs the collected data so that fluctuations in heart rate, blood pressure, and body temperature can be seen at a glance. This allows care recipients and their families to intuitively understand changes in health status. Furthermore, the management department can set reminders to encourage regular health checks and medication adherence. These reminders are sent to smartphones and home devices, and communicated to the user through voice, vibration, and visual alerts. This allows the management department to support the health management of those requiring care, ensuring they don't forget regular health checks and medication.

[0033] The proposal department proposes care protocols based on data collected by the data collection department. These protocols may include, but are not limited to, dietary management and exercise programs. For example, the proposal department might propose a dietary management protocol based on the health status of the person requiring care. Specifically, it analyzes the collected data and creates a meal plan optimized for the person's health. This meal plan includes nutritional balance, calorie intake, and meal timing to support the person's health maintenance. It can also propose exercise programs. The proposal department creates exercise programs tailored to the person's physical strength and health status and proposes appropriate exercises. These programs may include light stretching, walking, and strength training to support the person's physical fitness and health promotion. It can also propose care protocols tailored to health status. The proposal department creates care protocols that address the individual needs of the person requiring care and proposes appropriate care methods. For example, it can propose special care or rehabilitation programs tailored to specific health conditions or symptoms. This allows the proposal department to optimally manage the health status of the person requiring care and provide care protocols that meet individual needs. Furthermore, the proposal department can evaluate the effectiveness of care protocols based on the collected data and modify them as needed. This allows the proposal department to always provide optimal care protocols based on the latest information, supporting the health maintenance of those requiring care.

[0034] The alert unit can detect abnormalities based on data obtained from sensors and send alerts to family members or medical professionals in emergencies. For example, the alert unit can detect abnormal heart rates based on data obtained from a heart rate sensor. For example, it can send an alert if the heart rate increases rapidly. The alert unit can also detect abnormal blood pressure based on data obtained from a blood pressure sensor. For example, it can send an alert if the blood pressure shows an abnormal value. The alert unit can also detect abnormal body temperature based on data obtained from a body temperature sensor. For example, it can send an alert if the body temperature is abnormally high. This allows for a rapid response by sending an alert immediately in an emergency. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input data obtained from sensors into a generating AI and have the generating AI perform abnormality detection and alert sending.

[0035] The management department can generate health reports, track the health status of care recipients, and share them with their families. For example, the management department can generate daily reports and record the health status of care recipients in detail. The management department can also generate weekly reports and share the health status with their families. The management department can also generate weekly reports, track changes in health status, and share them with their families. The management department can also visually display health status using graphs. The management department can visually display health data using graphs to make it easier to understand. This makes health management easier by generating regular health reports, tracking health status, and sharing it with families. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input collected health data into a generating AI and have the generating AI perform the generation and sharing of health reports.

[0036] The management department can set reminders for medication times and medical appointments. For example, the management department can set up smartphone notifications to remind users of medication times. The management department can also set up email notifications to remind users of medical appointments. The management department can also customize the methods for setting reminders and notifications to meet individual needs. This allows users to receive the necessary care by setting reminders for medication times and medical appointments. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can have a generation AI perform the setting of reminders and notifications.

[0037] The suggestion unit can propose care protocols based on the health condition of the person requiring care. For example, the suggestion unit can propose a meal management protocol based on the health condition of the person requiring care. The suggestion unit can also propose an exercise program, for example, suggesting an exercise program and appropriate exercises for maintaining health. The suggestion unit can also propose care protocols tailored to the health condition, for example, suggesting care protocols tailored to the health condition and addressing individual needs. This supports the optimal care method by proposing care protocols adapted based on the health condition of the person requiring care. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input collected health data into a generating AI and have the generating AI execute the proposal of care protocols.

[0038] The proposal unit can manage a list of caregiving tasks and track their progress. For example, the proposal unit can create a list of caregiving tasks and track the progress of each task. The proposal unit can also update the contents of the task list and display the progress in real time. The proposal unit can also analyze the progress of the task list and make necessary adjustments. This improves the efficiency of caregiving by managing the caregiving task list and tracking its progress. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can have a generating AI perform the task list management and progress tracking.

[0039] The system includes a communication support unit that provides video call and chat functions for medical professionals. The communication support unit can provide video call and chat functions for medical professionals. For example, the communication support unit can provide video call functionality, enabling real-time communication with medical professionals. The communication support unit can also provide chat functionality, supporting text-based communication. Furthermore, the communication support unit can combine video call and chat functions to support rapid response in the event of an emergency. This enables mutual communication between caregivers and medical providers in the event of an emergency by providing video call and chat functions for medical professionals. Some or all of the above-described processes in the communication support unit may be performed using, for example, AI, or not. For example, the communication support unit can have a generating AI perform the provision of video call and chat functions.

[0040] The facility includes a mental health support department that checks the stress levels of care recipients and their families and provides relaxation and counseling resources. The mental health support department can, for example, conduct questionnaires to check stress levels. The mental health support department can also measure stress levels using biometric data. Furthermore, the mental health support department can provide relaxation resources to support stress reduction. The mental health support department can also provide counseling resources to support the maintenance of mental health. This allows the facility to support mental health by checking the stress levels of care recipients and their families and providing relaxation and counseling resources. Some or all of the processes described above in the Mental Health Support Department may be performed using AI, for example, or without AI. For example, the Mental Health Support Department may have a generating AI perform stress level checks and resource provision.

[0041] The system includes a personalized care plan unit that provides care plans based on the health status and needs of the person requiring care. The personalized care plan unit can provide care plans based on the health status and needs of the person requiring care. For example, the personalized care plan unit can analyze the health status of the person requiring care and create an individualized care plan. The personalized care plan unit can also adjust the care plan based on the needs of the person requiring care. The personalized care plan unit can also improve the care plan based on feedback from the user. This provides more accurate support by providing individualized care plans based on the health status and needs of the person requiring care. Some or all of the above-described processes in the personalized care plan unit may be performed using AI, for example, or without AI. For example, the personalized care plan unit can have a generating AI perform the health status analysis and care plan creation.

[0042] The data collection unit can analyze the care recipient's past health data and select a collection method. For example, the data collection unit can collect data from the care recipient's past health data at specific time periods. The data collection unit can also determine the optimal sensor placement based on the care recipient's past health data. Furthermore, the data collection unit can analyze the care recipient's past health data and focus data collection on specific health indicators. This allows for efficient data collection by analyzing the care recipient's past health data and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the collection method.

[0043] The data collection unit can filter data based on the care recipient's lifestyle patterns during data collection. For example, the data collection unit can analyze the care recipient's lifestyle patterns and concentrate data collection during daytime activity hours. The data collection unit can also minimize nighttime data collection based on the care recipient's lifestyle patterns. The data collection unit can also take the care recipient's lifestyle patterns into consideration and collect data during specific activities. This allows for efficient data collection by filtering data collection based on the care recipient's lifestyle patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of lifestyle patterns and the filtering of data collection.

[0044] The data collection unit can prioritize the collection of highly relevant data based on the geographical location information of the person receiving care. For example, if the person receiving care is out, the data collection unit can prioritize the collection of health data during travel. The data collection unit can also prioritize the collection of data related to the indoor environment if the person receiving care is at home. The data collection unit can also prioritize the collection of data related to the environment of a specific facility if the person receiving care is at that facility. This enables efficient data collection by prioritizing the collection of highly relevant data while considering the geographical location information of the person receiving care. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0045] The data collection unit can analyze the social media activities of the person being cared for and collect relevant data during data collection. For example, the data collection unit can estimate the stress level from the person being cared for and collect relevant data. The data collection unit can also identify health concerns from the person being cared for and collect relevant data. The data collection unit can also analyze lifestyle habits from the person being cared for and collect relevant data. By analyzing the social media activities of the person being cared for and collecting relevant data, a more detailed understanding of their health status can be obtained. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of social media activities and data collection.

[0046] The alert unit can adjust the level of detail of an alert based on the severity of the anomaly when sending an alert. For example, the alert unit can send a brief alert in the case of a minor anomaly. The alert unit can also send a detailed alert in the case of a moderate anomaly. The alert unit can also send an emergency alert in the case of a severe anomaly. This allows the system to send an appropriate alert by adjusting the level of detail of the alert based on the severity of the anomaly. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the severity of the anomaly into a generating AI and have the generating AI adjust the level of detail of the alert.

[0047] The alert unit can apply different alert algorithms based on the category of the anomaly when sending an alert. For example, in the case of a health anomaly, the alert unit can apply a health-related alert algorithm. The alert unit can also apply an environment-related alert algorithm in the case of an environmental anomaly. The alert unit can also apply a behavior-related alert algorithm in the case of a behavioral anomaly. By applying different alert algorithms according to the category of the anomaly, the alert unit can send appropriate alerts. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the category of the anomaly into a generating AI and have the generating AI execute the application of the alert algorithm.

[0048] The alert unit can determine the priority of alerts based on when the anomaly occurred when sending an alert. For example, the alert unit can send an alert immediately after the anomaly occurs. The alert unit can also increase the priority of alerts if the anomaly is ongoing. The alert unit can also decrease the priority of alerts if the anomaly is resolved. By determining the priority of alerts based on when the anomaly occurred, alerts can be sent at the appropriate time. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the timing of the anomaly occurrence into a generating AI and have the generating AI determine the priority of alerts.

[0049] The alert unit can adjust the order of alerts based on the relevance of the anomalies when sending alerts. For example, the alert unit can send an alert with the highest priority if the anomaly is directly related to health. The alert unit can also send an alert with the next highest priority if the anomaly is related to the environment. The alert unit can also send an alert last if the anomaly is related to behavior. This allows important alerts to be sent preferentially by adjusting the order of alerts based on the relevance of the anomalies. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, the alert unit can input the relevance of the anomalies into a generating AI and have the generating AI perform the adjustment of the order of alerts.

[0050] The management department can adjust the level of detail in health reports based on the importance of the health data when generating them. For example, the management department can generate a detailed report for important health data. The management department can also generate a report with moderate detail for moderate health data. The management department can also generate a concise report for minor health data. This allows the management department to provide appropriate reports by adjusting the level of detail based on the importance of the health data. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can input the importance of the health data into a generation AI and have the generation AI adjust the level of detail in the report.

[0051] The management unit can apply different reporting algorithms based on the category of health data when generating health reports. For example, if the health data is related to heart rate, the management unit can apply a heart rate-related reporting algorithm. The management unit can also apply a blood pressure-related reporting algorithm if the health data is related to blood pressure. The management unit can also apply a body temperature-related reporting algorithm if the health data is related to body temperature. By applying different reporting algorithms according to the category of health data, the management unit can provide appropriate reports. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the categories of health data into a generation AI and have the generation AI perform the application of the reporting algorithm.

[0052] The management department can determine the priority of health reports based on the timing of health data submission when generating them. For example, the management department can prioritize the inclusion of the most recent health data in the report. The management department can also prioritize the inclusion of periodic health data in the report in a balanced manner. The management department can also prioritize the inclusion of important data in the report by referring to past health data. This allows the management department to provide appropriate reports by determining the priority of reports based on the timing of health data submission. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the timing of health data submission into a generation AI and have the generation AI determine the report priorities.

[0053] The management department can adjust the order of health reports based on the relevance of the health data when generating them. For example, the management department can prioritize the inclusion of important health data in the report. The management department can also prioritize the inclusion of moderate health data in the report. The management department can also prioritize the inclusion of minor health data last. This allows for the provision of appropriate reports by adjusting the order of reports based on the relevance of the health data. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can input the relevance of the health data into a generating AI and have the generating AI adjust the order of the reports.

[0054] The proposal unit can adjust the level of detail of the proposed care protocol based on the importance of the health data. For example, if the health data is important, the proposal unit will propose a detailed care protocol. The proposal unit can also propose a care protocol with a moderate level of detail if the health data is of moderate importance. The proposal unit can also propose a concise care protocol if the health data is of mild importance. By adjusting the level of detail of the proposal based on the importance of the health data, an appropriate care protocol can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0055] The proposal unit can apply different proposal algorithms based on the category of health data when proposing care protocols. For example, if the health data is related to heart rate, the proposal unit will apply a heart rate-related proposal algorithm. The proposal unit can also apply a blood pressure-related proposal algorithm if the health data is related to blood pressure. The proposal unit can also apply a body temperature-related proposal algorithm if the health data is related to body temperature. By applying different proposal algorithms according to the category of health data, an appropriate care protocol can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of health data into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0056] The proposal unit can determine the priority of proposals based on the timing of health data submission when proposing care protocols. For example, the proposal unit can prioritize the incorporation of the most recent health data into the care protocol. The proposal unit can also incorporate periodic health data into the care protocol in a balanced manner. The proposal unit can also refer to past health data and prioritize the incorporation of important data into the care protocol. This allows for the provision of appropriate care protocols by determining the priority of proposals based on the timing of health data submission. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of health data submission into a generating AI and have the generating AI determine the priority of proposals.

[0057] The proposal unit can adjust the order of proposals based on the relevance of health data when proposing care protocols. For example, the proposal unit can prioritize the inclusion of important health data in the care protocols. The proposal unit can also prioritize the inclusion of moderate health data. The proposal unit can also prioritize the inclusion of minor health data last. By adjusting the order of proposals based on the relevance of health data, an appropriate care protocol can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0058] The communication support unit can select a method based on past communication history when providing communication support. For example, the communication support unit can prioritize suggesting communication methods that the care recipient has preferred to use in the past. The communication support unit can also communicate at the optimal timing based on the care recipient's past communication history. The communication support unit can also analyze the care recipient's past communication history and select the most effective communication method. By selecting the optimal method by referring to past communication history, more effective communication can be provided. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support unit can input past communication history into a generating AI and have the generating AI select the method.

[0059] The communication support unit can select a method based on the care recipient's device information when providing communication support. For example, if the care recipient is using a smartphone, the communication support unit can provide a communication method optimized for the smartphone. The communication support unit can also provide a communication method optimized for the tablet if the care recipient is using a tablet. The communication support unit can also provide a communication method optimized for the smartwatch if the care recipient is using a smartwatch. By selecting the optimal method while considering the care recipient's device information, more effective communication can be provided. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support unit can input device information into a generating AI and have the generating AI select a method.

[0060] The communication support unit can select a method based on the care recipient's device information when providing communication support. For example, if the care recipient is using a smartphone, the communication support unit can provide a communication method optimized for the smartphone. The communication support unit can also provide a communication method optimized for the tablet if the care recipient is using a tablet. The communication support unit can also provide a communication method optimized for the smartwatch if the care recipient is using a smartwatch. By selecting the optimal method while considering the care recipient's device information, more effective communication can be provided. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support unit can input device information into a generating AI and have the generating AI select a method.

[0061] The communication support unit can provide multilingual communication based on the care recipient's language settings during communication support. For example, the communication support unit can automatically set the communication language based on the care recipient's device language settings. The communication support unit can also provide a language switching function if the care recipient uses multiple languages. Furthermore, the communication support unit can provide communication in a specific language if the care recipient selects that language. This allows for more effective communication by providing multilingual communication according to the care recipient's language settings. Some or all of the above processing in the communication support unit may be performed using AI, or not. For example, the communication support unit can input language settings into a generating AI and have the generating AI execute multilingual communication.

[0062] The Mental Health Support Department can select methods based on past mental health data when providing mental health support. For example, the Mental Health Support Department can propose the most suitable relaxation method based on the care recipient's past mental health data. The Mental Health Support Department can also propose the most suitable counseling method based on the care recipient's past mental health data. The Mental Health Support Department can also analyze the care recipient's past mental health data and select the most effective mental health support method. By selecting the most suitable method by referring to past mental health data, the Mental Health Support Department can provide more effective mental health support. Some or all of the above processes in the Mental Health Support Department may be performed using AI, for example, or without AI. For example, the Mental Health Support Department can input past mental health data into a generating AI and have the generating AI perform the method selection.

[0063] The Mental Health Support Department can select methods for providing mental health support based on the current health status of the person receiving care. For example, if the person receiving care is tired, the Mental Health Support Department will prioritize providing relaxation resources. The Mental Health Support Department can also provide counseling resources if the person receiving care is in good health. The Mental Health Support Department can also provide concise and effective mental health support if the person receiving care is unwell. This allows for more effective mental health support by selecting the optimal method considering the current health status of the person receiving care. Some or all of the above-described processes in the Mental Health Support Department may be performed using AI, for example, or without AI. For example, the Mental Health Support Department can input the current health status into a generating AI and have the generating AI select the method.

[0064] The Mental Health Support Department can select a method of mental health support based on the geographical location of the person receiving care. For example, if the person receiving care is at home, the Mental Health Support Department can provide relaxation methods that can be performed at home. The Mental Health Support Department can also provide simple mental health support that can be performed while the person receiving care is out. The Mental Health Support Department can also provide mental health resources available at a specific facility if the person receiving care is in that facility. By selecting the optimal method considering the geographical location of the person receiving care, more effective mental health support can be provided. Some or all of the above processing in the Mental Health Support Department may be performed using AI, for example, or without AI. For example, the mental health support department can input geographical location information into a generating AI and have the AI ​​select a method.

[0065] The Mental Health Support Department can analyze the social media activities of the person receiving care and provide relevant support during mental health support sessions. For example, the Mental Health Support Department can estimate the stress level from the person receiving care's social media activities and provide relevant mental health support. Furthermore, the Mental Health Support Department can identify the person's health concerns from their social media activities and provide relevant mental health support. Additionally, the Mental Health Support Department can analyze the lifestyle habits from the person receiving care's social media activities and provide relevant mental health support. By analyzing the person receiving care's social media activities and providing relevant support, more effective mental health support can be provided. Some or all of the above-described processes in the Mental Health Support Department may be performed using AI, for example, or without AI. For example, the mental health support department can have a generating AI perform the analysis of social media activity and provide support.

[0066] The personalized care plan unit can select a method based on past care plan data when providing a care plan. For example, the personalized care plan unit can propose the optimal care plan from the care recipient's past care plan data. The personalized care plan unit can also select the optimal care method based on the care recipient's past care plan data. The personalized care plan unit can also analyze the care recipient's past care plan data to provide the most effective care plan. The personalized care plan unit can also analyze the care recipient's past care plan data to provide the most effective care plan. By selecting the optimal method by referring to past care plan data, a more effective care plan can be provided. Some or all of the above processing in the personalized care plan unit may be performed using AI, for example, or without AI. For example, the personalized care plan unit can input past care plan data into a generating AI and have the generating AI perform the method selection.

[0067] The personalized care plan unit can select a method based on the care recipient's current health condition when providing a care plan. For example, if the care recipient is tired, the personalized care plan unit can provide a concise and effective care plan. The personalized care plan unit can also provide a detailed care plan if the care recipient is in good health. The personalized care plan unit can also provide a quick and concise care plan if the care recipient is unwell. This allows for the provision of a more effective care plan by selecting the optimal method considering the care recipient's current health condition. Some or all of the above processing in the personalized care plan unit may be performed using AI, for example, or without AI. For example, the personalized care plan unit can input the current health condition into a generating AI and have the generating AI select the method.

[0068] The Personalized Care Plan Unit can select a method based on the geographical location information of the person receiving care when providing a care plan. For example, if the person receiving care is at home, the Personalized Care Plan Unit can provide a care plan that can be carried out at home. The Personalized Care Plan Unit can also provide a simple care plan that can be carried out at the location where the person receiving care is out. The Personalized Care Plan Unit can also provide a care plan that can be used at a specific facility if the person receiving care is at that facility. By selecting the optimal method considering the geographical location information of the person receiving care, a more effective care plan can be provided. Some or all of the above processing in the Personalized Care Plan Unit may be performed using AI, for example, or without AI. For example, the personalized care plan department can input geographical location information into a generating AI and have the AI ​​select a method.

[0069] The Personalized Care Plan Unit can analyze the social media activity of the person receiving care and provide a relevant care plan when providing a care plan. For example, the Personalized Care Plan Unit can estimate the stress level from the person receiving care's social media activity and provide a relevant care plan. The Personalized Care Plan Unit can also understand the person receiving care's health concerns from their social media activity and provide a relevant care plan. The Personalized Care Plan Unit can also analyze the lifestyle habits from the person receiving care's social media activity and provide a relevant care plan. By analyzing the person receiving care's social media activity and providing a relevant care plan, a more effective care plan can be provided. Some or all of the above processing in the Personalized Care Plan Unit may be performed using AI, for example, or without AI. For example, the personalized care plan department can have AI generate data to analyze social media activity and provide care plans.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The data collection unit can monitor the sleep patterns of the person being cared for and evaluate the quality of their sleep. For example, it can detect the person's movements during sleep and evaluate the depth of their sleep. It can also monitor the person's heart rate and respiratory rate to detect abnormalities during sleep. Furthermore, it can monitor the person's sleep environment (e.g., room temperature and humidity) and collect data to provide a comfortable sleep environment. This can improve the quality of sleep for the person being cared for and contribute to improving their health.

[0072] The management department can record the meals of those receiving care and evaluate their nutritional balance. For example, the management department can record the calories and nutrients of the meals consumed by those receiving care and evaluate their nutritional balance. Furthermore, the management department can suggest improvements to the nutritional balance based on the meals of those receiving care. In addition, the management department can share the meals of those receiving care with their families and provide support for meal management. This can improve the nutritional balance of those receiving care and contribute to maintaining their health.

[0073] The suggestion unit can monitor the exercise patterns of the person receiving care and propose an appropriate exercise program. For example, the suggestion unit can record the number of steps and exercise time of the person receiving care and evaluate the amount of exercise. Furthermore, the suggestion unit can propose an appropriate exercise program based on the person receiving care's health condition. In addition, the suggestion unit can share the person receiving care's exercise patterns with the family and provide support for exercise management. This allows for proper management of the person receiving care's exercise level and contributes to maintaining their health.

[0074] The data collection unit can analyze the care recipient's past health data and select the appropriate collection method. For example, it can collect data at specific time periods based on the care recipient's past health data. It can also determine the optimal sensor placement based on the care recipient's past health data. Furthermore, it can analyze the care recipient's past health data and focus data collection on specific health indicators. By analyzing the care recipient's past health data and selecting the optimal collection method, efficient data collection becomes possible.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The data collection unit collects health status data. This health status data includes heart rate, blood pressure, and body temperature. The data collection unit uses a heart rate sensor to measure heart rate in real time and collect data. A blood pressure sensor periodically measures blood pressure and collects data. A body temperature sensor continuously measures body temperature and collects data. Step 2: The alert unit detects anomalies based on the data collected by the collection unit and sends an alert. Anomalies include rapid changes in heart rate, abnormal blood pressure values, and abnormal body temperature. The alert unit detects rapid changes in heart rate and sends an alert. It detects abnormal blood pressure values ​​and sends an alert. It detects abnormal body temperature and sends an alert. Step 3: The management department generates health reports and sets reminders based on the data collected by the collection department. Health reports include daily reports, weekly reports, and graphs. The management department generates daily reports to track the health status of care recipients. It generates weekly reports to share health status with families. It uses graphs to visually represent health status. Step 4: The proposal unit proposes care protocols based on the data collected by the collection unit. These care protocols include dietary management and exercise programs. The proposal unit proposes dietary management protocols based on the health status of the person requiring care. It also proposes exercise programs and care protocols tailored to the person's health status.

[0077] (Example of form 2) An autonomous home assistant system according to an embodiment of the present invention is a system that supports home care for the elderly using generative AI and communication technology. This system utilizes a generative AI agent to monitor the health status of the person receiving care and provides comprehensive care support by interconnecting caregivers with medical and care-related institutions. For example, as a health status monitoring and alert function, it detects abnormalities based on data obtained from sensors and immediately sends alerts to family members and medical professionals in emergencies. It monitors blood pressure, heart rate, body temperature, activity level, etc. in real time. Next, as daily health management, it generates regular health reports to track the health status of the person receiving care and share them with family members. It also sets reminders for medication times and medical appointments to help ensure that the person receives the necessary care. Furthermore, as automated care support, it proposes care protocols adapted based on the health status of the person receiving care and supports the optimal care method. It manages a list of care-related tasks and tracks their progress. As communication support, it provides video call and chat functions with medical professionals, enabling mutual communication between caregivers and medical providers in the event of an emergency. As part of mental health support, it checks the stress levels of care recipients and their families and provides resources for relaxation and counseling. It also features a voice interface that allows for easy instruction and questioning. Finally, as part of personalized care planning, it provides individualized care plans based on the health status and needs of care recipients. The generated AI learns from user feedback and data to provide more accurate support. These functions support a safe and comfortable life for the elderly, enabling them to continue living at home with peace of mind. It also reduces the burden on caregivers and enables 24-hour monitoring and emergency response. In this way, the autonomous home assistant system can comprehensively support home care for the elderly and provide a safe and comfortable life.

[0078] The autonomous home assistant system according to this embodiment comprises a data collection unit, an alert unit, a management unit, and a suggestion unit. The data collection unit collects health status data. Health status data includes, but is not limited to, heart rate, blood pressure, and body temperature. The data collection unit can, for example, measure heart rate using a heart rate sensor. The data collection unit can also measure blood pressure using a blood pressure sensor. The data collection unit can also measure body temperature using a body temperature sensor. For example, the data collection unit measures heart rate in real time using a heart rate sensor and collects the data. The blood pressure sensor measures blood pressure periodically and collects the data. The body temperature sensor continuously measures body temperature and collects the data. The alert unit detects abnormalities based on the data collected by the data collection unit and sends an alert. Abnormalities include, but are not limited to, sudden changes in heart rate and abnormal blood pressure values. The alert unit can, for example, detect sudden changes in heart rate and send an alert. The alert unit can also detect abnormal blood pressure values ​​and send an alert. The alert unit can also detect abnormal body temperature and send alerts. For example, the alert unit sends an alert if the heart rate increases rapidly, if blood pressure is abnormally high, or if body temperature is abnormally high. The management unit generates health reports and sets reminders based on the data collected by the collection unit. Health reports include, but are not limited to, daily reports, weekly reports, and graphs. For example, the management unit generates daily reports to track the health status of care recipients. The management unit can also generate weekly reports to share health status with family members. The management unit can also visually display health status using graphs. For example, the management unit generates daily reports to record the health status of care recipients in detail. Weekly reports track changes in health status and share them with family members. Graphs visually display health data to make it easier to understand. The suggestion unit proposes care protocols based on the data collected by the collection unit. Care protocols include, but are not limited to, dietary management and exercise programs.The suggestion unit can, for example, propose a meal management protocol based on the health condition of the person requiring care. The suggestion unit can also propose an exercise program. Furthermore, the suggestion unit can propose a care protocol tailored to the person's health condition. For example, the suggestion unit can propose a balanced meal plan based on the person requiring care. The exercise program can propose appropriate exercises for maintaining health. The care protocol tailored to the health condition addresses individual needs. As a result, the autonomous home assistant system according to this embodiment can collect health data, detect abnormalities, send alerts, generate health reports, set reminders, and propose care protocols.

[0079] The data collection unit collects health status data. This health status data includes, but is not limited to, heart rate, blood pressure, and body temperature. For example, the data collection unit measures heart rate using a heart rate sensor. It can also measure blood pressure using a blood pressure sensor. It can also measure body temperature using a body temperature sensor. Specifically, the data collection unit measures heart rate in real time using a heart rate sensor and collects the data. The heart rate sensor is designed as a wearable device that is attached to the skin and can detect heart rate fluctuations with high accuracy. The blood pressure sensor measures blood pressure periodically and collects the data. Blood pressure sensors come in types that are attached to the upper arm or wrist and are designed with user comfort in mind. The body temperature sensor continuously measures body temperature and collects the data. The body temperature sensor is equipped with a highly sensitive temperature sensor in the part that contacts the skin and can accurately detect even minute temperature changes. As a result, the data collection unit collects health data such as heart rate, blood pressure, and body temperature in real time and transmits it to a central database. The collected data is stored on a cloud server, making it accessible to other departments. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, if changes in health are predicted, increasing the data collection frequency allows for a more detailed understanding of health status. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0080] The alert unit detects anomalies based on data collected by the data collection unit and sends an alert. Anomalies include, but are not limited to, rapid changes in heart rate and abnormal blood pressure values. For example, the alert unit detects rapid changes in heart rate and sends an alert. Specifically, the alert unit analyzes the collected heart rate data in real time to detect rapid increases or decreases. If the heart rate exceeds a certain range, the alert unit immediately sends an alert. It can also detect abnormal blood pressure values ​​and send an alert. Blood pressure data is periodically transmitted from the data collection unit, and the alert unit analyzes it to detect abnormal values. For example, if blood pressure exceeds the normal range, the alert unit sends an alert. It can also detect abnormal body temperature and send an alert. Body temperature data is continuously transmitted from the data collection unit, and the alert unit analyzes it to detect abnormally high or low temperatures. For example, if body temperature is abnormally high, the alert unit sends an alert. Alerts are sent to the user's smartphone or home device and communicated to the user through voice, vibration, and visual warnings. This allows the alert unit to quickly detect abnormal health conditions and prompt users to take appropriate action. Furthermore, the alert unit can prioritize alerts according to the type and urgency of the abnormality, and notify important alerts first. In this way, the alert unit can support users' health management and encourage quick and appropriate action.

[0081] The management department generates health reports and sets reminders based on data collected by the collection department. Health reports include, but are not limited to, daily reports, weekly reports, and graphs. For example, the management department generates daily reports to track the health status of care recipients. Specifically, the management department analyzes the collected data and compiles it into daily reports. Daily reports include fluctuations and abnormalities in heart rate, blood pressure, and body temperature, recording the care recipient's health status in detail. Weekly reports track changes in health status and can also be shared with family members. Weekly reports aggregate the data from daily reports to show weekly changes and trends in health status. This allows family members to understand the care recipient's health status and consider necessary actions. Graphs visually display health data, making it easier to understand. The management department graphs the collected data so that fluctuations in heart rate, blood pressure, and body temperature can be seen at a glance. This allows care recipients and their families to intuitively understand changes in health status. Furthermore, the management department can set reminders to encourage regular health checks and medication adherence. These reminders are sent to smartphones and home devices, and communicated to the user through voice, vibration, and visual alerts. This allows the management department to support the health management of those requiring care, ensuring they don't forget regular health checks and medication.

[0082] The proposal department proposes care protocols based on data collected by the data collection department. These protocols may include, but are not limited to, dietary management and exercise programs. For example, the proposal department might propose a dietary management protocol based on the health status of the person requiring care. Specifically, it analyzes the collected data and creates a meal plan optimized for the person's health. This meal plan includes nutritional balance, calorie intake, and meal timing to support the person's health maintenance. It can also propose exercise programs. The proposal department creates exercise programs tailored to the person's physical strength and health status and proposes appropriate exercises. These programs may include light stretching, walking, and strength training to support the person's physical fitness and health promotion. It can also propose care protocols tailored to health status. The proposal department creates care protocols that address the individual needs of the person requiring care and proposes appropriate care methods. For example, it can propose special care or rehabilitation programs tailored to specific health conditions or symptoms. This allows the proposal department to optimally manage the health status of the person requiring care and provide care protocols that meet individual needs. Furthermore, the proposal department can evaluate the effectiveness of care protocols based on the collected data and modify them as needed. This allows the proposal department to always provide optimal care protocols based on the latest information, supporting the health maintenance of those requiring care.

[0083] The alert unit can detect abnormalities based on data obtained from sensors and send alerts to family members or medical professionals in emergencies. For example, the alert unit can detect abnormal heart rates based on data obtained from a heart rate sensor. For example, it can send an alert if the heart rate increases rapidly. The alert unit can also detect abnormal blood pressure based on data obtained from a blood pressure sensor. For example, it can send an alert if the blood pressure shows an abnormal value. The alert unit can also detect abnormal body temperature based on data obtained from a body temperature sensor. For example, it can send an alert if the body temperature is abnormally high. This allows for a rapid response by sending an alert immediately in an emergency. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input data obtained from sensors into a generating AI and have the generating AI perform abnormality detection and alert sending.

[0084] The management department can generate health reports, track the health status of care recipients, and share them with their families. For example, the management department can generate daily reports and record the health status of care recipients in detail. The management department can also generate weekly reports and share the health status with their families. The management department can also generate weekly reports, track changes in health status, and share them with their families. The management department can also visually display health status using graphs. The management department can visually display health data using graphs to make it easier to understand. This makes health management easier by generating regular health reports, tracking health status, and sharing it with families. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input collected health data into a generating AI and have the generating AI perform the generation and sharing of health reports.

[0085] The management department can set reminders for medication times and medical appointments. For example, the management department can set up smartphone notifications to remind users of medication times. The management department can also set up email notifications to remind users of medical appointments. The management department can also customize the methods for setting reminders and notifications to meet individual needs. This allows users to receive the necessary care by setting reminders for medication times and medical appointments. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can have a generation AI perform the setting of reminders and notifications.

[0086] The suggestion unit can propose care protocols based on the health condition of the person requiring care. For example, the suggestion unit can propose a meal management protocol based on the health condition of the person requiring care. The suggestion unit can also propose an exercise program, for example, suggesting an exercise program and appropriate exercises for maintaining health. The suggestion unit can also propose care protocols tailored to the health condition, for example, suggesting care protocols tailored to the health condition and addressing individual needs. This supports the optimal care method by proposing care protocols adapted based on the health condition of the person requiring care. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input collected health data into a generating AI and have the generating AI execute the proposal of care protocols.

[0087] The proposal unit can manage a list of caregiving tasks and track their progress. For example, the proposal unit can create a list of caregiving tasks and track the progress of each task. The proposal unit can also update the contents of the task list and display the progress in real time. The proposal unit can also analyze the progress of the task list and make necessary adjustments. This improves the efficiency of caregiving by managing the caregiving task list and tracking its progress. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can have a generating AI perform the task list management and progress tracking.

[0088] The system includes a communication support unit that provides video call and chat functions for medical professionals. The communication support unit can provide video call and chat functions for medical professionals. For example, the communication support unit can provide video call functionality, enabling real-time communication with medical professionals. The communication support unit can also provide chat functionality, supporting text-based communication. Furthermore, the communication support unit can combine video call and chat functions to support rapid response in the event of an emergency. This enables mutual communication between caregivers and medical providers in the event of an emergency by providing video call and chat functions for medical professionals. Some or all of the above-described processes in the communication support unit may be performed using, for example, AI, or not. For example, the communication support unit can have a generating AI perform the provision of video call and chat functions.

[0089] The facility includes a mental health support department that checks the stress levels of care recipients and their families and provides relaxation and counseling resources. The mental health support department can, for example, conduct questionnaires to check stress levels. The mental health support department can also measure stress levels using biometric data. Furthermore, the mental health support department can provide relaxation resources to support stress reduction. The mental health support department can also provide counseling resources to support the maintenance of mental health. This allows the facility to support mental health by checking the stress levels of care recipients and their families and providing relaxation and counseling resources. Some or all of the processes described above in the Mental Health Support Department may be performed using AI, for example, or without AI. For example, the Mental Health Support Department may have a generating AI perform stress level checks and resource provision.

[0090] The system includes a personalized care plan unit that provides care plans based on the health status and needs of the person requiring care. The personalized care plan unit can provide care plans based on the health status and needs of the person requiring care. For example, the personalized care plan unit can analyze the health status of the person requiring care and create an individualized care plan. The personalized care plan unit can also adjust the care plan based on the needs of the person requiring care. The personalized care plan unit can also improve the care plan based on feedback from the user. This provides more accurate support by providing individualized care plans based on the health status and needs of the person requiring care. Some or all of the above-described processes in the personalized care plan unit may be performed using AI, for example, or without AI. For example, the personalized care plan unit can have a generating AI perform the health status analysis and care plan creation.

[0091] The data collection unit can estimate the emotions of the person being cared for and adjust the frequency of data collection based on the estimated emotions. For example, if the person being cared for is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. The data collection unit can also increase the frequency of data collection when the person being cared for is relaxed to gain a more detailed understanding of their health status. The data collection unit can also minimize the frequency of data collection when the person being cared for is in a hurry to quickly obtain the necessary information. This reduces the burden and allows for a more detailed understanding of the health status by adjusting the frequency of data collection based on the emotions of the person being cared for. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit may input the emotional data of the person being cared for into the generating AI and have the generating AI adjust the frequency of data collection.

[0092] The data collection unit can analyze the care recipient's past health data and select a collection method. For example, the data collection unit can collect data from the care recipient's past health data at specific time periods. The data collection unit can also determine the optimal sensor placement based on the care recipient's past health data. Furthermore, the data collection unit can analyze the care recipient's past health data and focus data collection on specific health indicators. This allows for efficient data collection by analyzing the care recipient's past health data and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the collection method.

[0093] The data collection unit can filter data based on the care recipient's lifestyle patterns during data collection. For example, the data collection unit can analyze the care recipient's lifestyle patterns and concentrate data collection during daytime activity hours. The data collection unit can also minimize nighttime data collection based on the care recipient's lifestyle patterns. The data collection unit can also take the care recipient's lifestyle patterns into consideration and collect data during specific activities. This allows for efficient data collection by filtering data collection based on the care recipient's lifestyle patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of lifestyle patterns and the filtering of data collection.

[0094] The data collection unit can estimate the emotions of the person being cared for and determine the priority of data to collect based on the estimated emotions. For example, if the person being cared for is stressed, the data collection unit will prioritize collecting stress-related data. The data collection unit can also prioritize collecting overall health data in a balanced manner if the person being cared for is relaxed. The data collection unit can also prioritize collecting only important health indicators if the person being cared for is in a hurry. This allows for the priority collection of important data by determining the priority of data to collect based on the emotions of the person being cared for. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the emotional data of the person receiving care into a generating AI and have the generating AI determine the priority of the data.

[0095] The data collection unit can prioritize the collection of highly relevant data based on the geographical location information of the person receiving care. For example, if the person receiving care is out, the data collection unit can prioritize the collection of health data during travel. The data collection unit can also prioritize the collection of data related to the indoor environment if the person receiving care is at home. The data collection unit can also prioritize the collection of data related to the environment of a specific facility if the person receiving care is at that facility. This enables efficient data collection by prioritizing the collection of highly relevant data while considering the geographical location information of the person receiving care. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0096] The data collection unit can analyze the social media activities of the person being cared for and collect relevant data during data collection. For example, the data collection unit can estimate the stress level from the person being cared for and collect relevant data. The data collection unit can also identify health concerns from the person being cared for and collect relevant data. The data collection unit can also analyze lifestyle habits from the person being cared for and collect relevant data. By analyzing the social media activities of the person being cared for and collecting relevant data, a more detailed understanding of their health status can be obtained. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of social media activities and data collection.

[0097] The alert unit can estimate the emotions of the person being cared for and adjust the method of sending alerts based on the estimated emotions. For example, if the person being cared for is stressed, the alert unit can send an alert in a gentle tone. The alert unit can also send an alert in a normal tone if the person being cared for is relaxed. The alert unit can also send an alert quickly if the person being cared for is in a hurry. This allows for the sending of more appropriate alerts by adjusting the method of sending alerts based on the emotions of the person being cared for. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the care recipient's emotional data into a generating AI and have the AI ​​adjust how alerts are sent.

[0098] The alert unit can adjust the level of detail of an alert based on the severity of the anomaly when sending an alert. For example, the alert unit can send a brief alert in the case of a minor anomaly. The alert unit can also send a detailed alert in the case of a moderate anomaly. The alert unit can also send an emergency alert in the case of a severe anomaly. This allows the system to send an appropriate alert by adjusting the level of detail of the alert based on the severity of the anomaly. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the severity of the anomaly into a generating AI and have the generating AI adjust the level of detail of the alert.

[0099] The alert unit can apply different alert algorithms based on the category of the anomaly when sending an alert. For example, in the case of a health anomaly, the alert unit can apply a health-related alert algorithm. The alert unit can also apply an environment-related alert algorithm in the case of an environmental anomaly. The alert unit can also apply a behavior-related alert algorithm in the case of a behavioral anomaly. By applying different alert algorithms according to the category of the anomaly, the alert unit can send appropriate alerts. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the category of the anomaly into a generating AI and have the generating AI execute the application of the alert algorithm.

[0100] The alert unit can estimate the emotions of the person being cared for and prioritize alerts based on the estimated emotions. For example, if the person being cared for is stressed, the alert unit will prioritize sending stress-related alerts. The alert unit can also send a balanced mix of general health alerts if the person being cared for is relaxed. The alert unit can also prioritize sending only important health alerts if the person being cared for is in a hurry. This allows for the priority of important alerts by prioritizing them based on the emotions of the person being cared for. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the care recipient's emotional data into a generating AI and have the generating AI determine the priority of alerts.

[0101] The alert unit can determine the priority of alerts based on when the anomaly occurred when sending an alert. For example, the alert unit can send an alert immediately after the anomaly occurs. The alert unit can also increase the priority of alerts if the anomaly is ongoing. The alert unit can also decrease the priority of alerts if the anomaly is resolved. By determining the priority of alerts based on when the anomaly occurred, alerts can be sent at the appropriate time. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the timing of the anomaly occurrence into a generating AI and have the generating AI determine the priority of alerts.

[0102] The alert unit can adjust the order of alerts based on the relevance of the anomalies when sending alerts. For example, the alert unit can send an alert with the highest priority if the anomaly is directly related to health. The alert unit can also send an alert with the next highest priority if the anomaly is related to the environment. The alert unit can also send an alert last if the anomaly is related to behavior. This allows important alerts to be sent preferentially by adjusting the order of alerts based on the relevance of the anomalies. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, the alert unit can input the relevance of the anomalies into a generating AI and have the generating AI perform the adjustment of the order of alerts.

[0103] The management department can estimate the emotions of the person receiving care and adjust the presentation of the health report based on the estimated emotions. For example, if the person receiving care is stressed, the management department can provide a concise and easy-to-read report. The management department can also provide a detailed report if the person receiving care is relaxed. The management department can also provide a concise and easy-to-read report if the person receiving care is in a hurry. This allows for the provision of more appropriate reports by adjusting the presentation of the health report based on the emotions of the person receiving care. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-mentioned processes in the management department may be performed using AI, for example, or without AI. For example, the management department may input the emotional data of the care recipients into a generating AI and have the generating AI adjust the way health reports are presented.

[0104] The management department can adjust the level of detail in health reports based on the importance of the health data when generating them. For example, the management department can generate a detailed report for important health data. The management department can also generate a report with moderate detail for moderate health data. The management department can also generate a concise report for minor health data. This allows the management department to provide appropriate reports by adjusting the level of detail based on the importance of the health data. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can input the importance of the health data into a generation AI and have the generation AI adjust the level of detail in the report.

[0105] The management unit can apply different reporting algorithms based on the category of health data when generating health reports. For example, if the health data is related to heart rate, the management unit can apply a heart rate-related reporting algorithm. The management unit can also apply a blood pressure-related reporting algorithm if the health data is related to blood pressure. The management unit can also apply a body temperature-related reporting algorithm if the health data is related to body temperature. By applying different reporting algorithms according to the category of health data, the management unit can provide appropriate reports. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the categories of health data into a generation AI and have the generation AI perform the application of the reporting algorithm.

[0106] The management department can estimate the emotions of the person receiving care and adjust the length of the health report based on the estimated emotions. For example, if the person receiving care is stressed, the management department can provide a short, to-the-point report. The management department can also provide a longer report with more detailed explanations if the person receiving care is relaxed. The management department can also provide a longer report with more detailed explanations if the person receiving care is relaxed. The management department can also provide a concise, easy-to-read report if the person receiving care is in a hurry. This allows for the provision of more appropriate reports by adjusting the length of the health report based on the emotions of the person receiving care. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-mentioned processes in the management department may be performed using AI, for example, or without AI. For example, the management department may input the emotional data of the care recipient into a generating AI and have the generating AI adjust the length of the health report.

[0107] The management department can determine the priority of health reports based on the timing of health data submission when generating them. For example, the management department can prioritize the inclusion of the most recent health data in the report. The management department can also prioritize the inclusion of periodic health data in the report in a balanced manner. The management department can also prioritize the inclusion of important data in the report by referring to past health data. This allows the management department to provide appropriate reports by determining the priority of reports based on the timing of health data submission. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the timing of health data submission into a generation AI and have the generation AI determine the report priorities.

[0108] The management department can adjust the order of health reports based on the relevance of the health data when generating them. For example, the management department can prioritize the inclusion of important health data in the report. The management department can also prioritize the inclusion of moderate health data in the report. The management department can also prioritize the inclusion of minor health data last. This allows for the provision of appropriate reports by adjusting the order of reports based on the relevance of the health data. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can input the relevance of the health data into a generating AI and have the generating AI adjust the order of the reports.

[0109] The suggestion unit can estimate the emotions of the person receiving care and adjust the method of suggesting care protocols based on the estimated emotions. For example, if the person receiving care is stressed, the suggestion unit can suggest a concise and easy-to-understand care protocol. The suggestion unit can also suggest a care protocol that includes detailed information if the person receiving care is relaxed. The suggestion unit can also suggest a care protocol that gets straight to the point if the person receiving care is in a hurry. By adjusting the method of suggesting care protocols based on the emotions of the person receiving care, a more appropriate care protocol can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the emotional data of the person receiving care into a generating AI and have the generating AI adjust the method of proposing care protocols.

[0110] The proposal unit can adjust the level of detail of the proposed care protocol based on the importance of the health data. For example, if the health data is important, the proposal unit will propose a detailed care protocol. The proposal unit can also propose a care protocol with a moderate level of detail if the health data is of moderate importance. The proposal unit can also propose a concise care protocol if the health data is of mild importance. By adjusting the level of detail of the proposal based on the importance of the health data, an appropriate care protocol can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0111] The proposal unit can apply different proposal algorithms based on the category of health data when proposing care protocols. For example, if the health data is related to heart rate, the proposal unit will apply a heart rate-related proposal algorithm. The proposal unit can also apply a blood pressure-related proposal algorithm if the health data is related to blood pressure. The proposal unit can also apply a body temperature-related proposal algorithm if the health data is related to body temperature. By applying different proposal algorithms according to the category of health data, an appropriate care protocol can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of health data into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0112] The suggestion unit can estimate the emotions of the person receiving care and prioritize care protocols based on those estimated emotions. For example, if the person receiving care is stressed, the suggestion unit will prioritize suggesting care protocols related to stress reduction. The suggestion unit can also propose a balanced set of care protocols related to overall health maintenance if the person receiving care is relaxed. The suggestion unit can also prioritize suggesting care protocols related to important health indicators if the person receiving care is in a hurry. This allows for the priority provision of important care protocols by prioritizing them based on the emotions of the person receiving care. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text-generating AI (e.g., LLM) or a multimodal generative AI. Some or all of the processing described above in the proposed unit may be performed using AI, or not using AI. For example, the proposed unit may input emotional data of the person being cared for into the generative AI and have the generative AI determine the priorities of the care protocol.

[0113] The proposal unit can determine the priority of proposals based on the timing of health data submission when proposing care protocols. For example, the proposal unit can prioritize the incorporation of the most recent health data into the care protocol. The proposal unit can also incorporate periodic health data into the care protocol in a balanced manner. The proposal unit can also refer to past health data and prioritize the incorporation of important data into the care protocol. This allows for the provision of appropriate care protocols by determining the priority of proposals based on the timing of health data submission. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of health data submission into a generating AI and have the generating AI determine the priority of proposals.

[0114] The proposal unit can adjust the order of proposals based on the relevance of health data when proposing care protocols. For example, the proposal unit can prioritize the inclusion of important health data in the care protocols. The proposal unit can also prioritize the inclusion of moderate health data. The proposal unit can also prioritize the inclusion of minor health data last. By adjusting the order of proposals based on the relevance of health data, an appropriate care protocol can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0115] The communication support unit can estimate the emotions of the person being cared for and adjust its communication methods based on the estimated emotions. For example, if the person being cared for is stressed, the communication support unit will communicate in a calm tone. The communication support unit can also communicate in a cheerful tone if the person being cared for is relaxed. The communication support unit can also communicate in a cheerful tone if the person being cared for is relaxed. The communication support unit can also communicate quickly and concisely if the person being cared for is in a hurry. The communication support unit can also communicate quickly and concisely if the person being cared for is in a hurry. By adjusting the communication methods based on the emotions of the person being cared for, more appropriate communication can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support department can input emotional data of the person receiving care into a generating AI and have the AI ​​adjust the communication method accordingly.

[0116] The communication support unit can select a method based on past communication history when providing communication support. For example, the communication support unit can prioritize suggesting communication methods that the care recipient has preferred to use in the past. The communication support unit can also communicate at the optimal timing based on the care recipient's past communication history. The communication support unit can also analyze the care recipient's past communication history and select the most effective communication method. By selecting the optimal method by referring to past communication history, more effective communication can be provided. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support unit can input past communication history into a generating AI and have the generating AI select the method.

[0117] The communication support unit can select a method based on the care recipient's device information when providing communication support. For example, if the care recipient is using a smartphone, the communication support unit can provide a communication method optimized for the smartphone. The communication support unit can also provide a communication method optimized for the tablet if the care recipient is using a tablet. The communication support unit can also provide a communication method optimized for the smartwatch if the care recipient is using a smartwatch. By selecting the optimal method while considering the care recipient's device information, more effective communication can be provided. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support unit can input device information into a generating AI and have the generating AI select a method.

[0118] The communication support unit can estimate the emotions of the person receiving care and determine communication priorities based on those estimated emotions. For example, if the person receiving care is stressed, the communication support unit will prioritize communication related to stress reduction. The communication support unit can also balance communication related to overall health maintenance if the person receiving care is relaxed. The communication support unit can also balance communication related to overall health maintenance if the person receiving care is relaxed. The communication support unit can also prioritize communication related to important health indicators if the person receiving care is in a hurry. This allows important communication to be prioritized by determining communication priorities based on the emotions of the person receiving care. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the communication support department may be performed using AI, for example, or without AI. For example, the communication support department can input the emotional data of the person receiving care into a generating AI and have the generating AI determine the priority of communication.

[0119] The communication support unit can select a method based on the care recipient's device information when providing communication support. For example, if the care recipient is using a smartphone, the communication support unit can provide a communication method optimized for the smartphone. The communication support unit can also provide a communication method optimized for the tablet if the care recipient is using a tablet. The communication support unit can also provide a communication method optimized for the smartwatch if the care recipient is using a smartwatch. By selecting the optimal method while considering the care recipient's device information, more effective communication can be provided. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support unit can input device information into a generating AI and have the generating AI select a method.

[0120] The communication support unit can provide multilingual communication based on the care recipient's language settings during communication support. For example, the communication support unit can automatically set the communication language based on the care recipient's device language settings. The communication support unit can also provide a language switching function if the care recipient uses multiple languages. Furthermore, the communication support unit can provide communication in a specific language if the care recipient selects that language. This allows for more effective communication by providing multilingual communication according to the care recipient's language settings. Some or all of the above processing in the communication support unit may be performed using AI, or not. For example, the communication support unit can input language settings into a generating AI and have the generating AI execute multilingual communication.

[0121] The mental health support department can estimate the emotions of the person receiving care and adjust the mental health support methods based on the estimated emotions. For example, if the person receiving care is stressed, the mental health support department can provide relaxation resources. The mental health support department can also provide counseling resources if the person receiving care is relaxed. The mental health support department can also provide concise and effective mental health support if the person receiving care is in a hurry. This allows for the provision of more appropriate mental health support by adjusting the mental health support methods based on the emotions of the person receiving care. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the mental health support department may be performed using AI, for example, or without AI. For example, the mental health support department can input the emotional data of the person receiving care into a generating AI and have the generating AI adjust the methods of mental health support.

[0122] The Mental Health Support Department can select methods based on past mental health data when providing mental health support. For example, the Mental Health Support Department can propose the most suitable relaxation method based on the care recipient's past mental health data. The Mental Health Support Department can also propose the most suitable counseling method based on the care recipient's past mental health data. The Mental Health Support Department can also analyze the care recipient's past mental health data and select the most effective mental health support method. By selecting the most suitable method by referring to past mental health data, the Mental Health Support Department can provide more effective mental health support. Some or all of the above processes in the Mental Health Support Department may be performed using AI, for example, or without AI. For example, the Mental Health Support Department can input past mental health data into a generating AI and have the generating AI perform the method selection.

[0123] The Mental Health Support Department can select methods for providing mental health support based on the current health status of the person receiving care. For example, if the person receiving care is tired, the Mental Health Support Department will prioritize providing relaxation resources. The Mental Health Support Department can also provide counseling resources if the person receiving care is in good health. The Mental Health Support Department can also provide concise and effective mental health support if the person receiving care is unwell. This allows for more effective mental health support by selecting the optimal method considering the current health status of the person receiving care. Some or all of the above-described processes in the Mental Health Support Department may be performed using AI, for example, or without AI. For example, the Mental Health Support Department can input the current health status into a generating AI and have the generating AI select the method.

[0124] The mental health support department can estimate the emotions of the person receiving care and prioritize mental health support based on those estimated emotions. For example, if the person receiving care is stressed, the mental health support department will prioritize mental health support related to stress reduction. The mental health support department can also prioritize mental health support related to overall mental health maintenance if the person receiving care is relaxed. The mental health support department can also prioritize support related to important mental health indicators if the person receiving care is in a hurry. This allows for the prioritization of important mental health support based on the emotions of the person receiving care. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the mental health support unit may be performed using AI, or not using AI. For example, the mental health support unit may input emotional data of the person receiving care into the generative AI and have the generative AI determine the priority of mental health support.

[0125] The Mental Health Support Department can select a method of mental health support based on the geographical location of the person receiving care. For example, if the person receiving care is at home, the Mental Health Support Department can provide relaxation methods that can be performed at home. The Mental Health Support Department can also provide simple mental health support that can be performed while the person receiving care is out. The Mental Health Support Department can also provide mental health resources available at a specific facility if the person receiving care is in that facility. By selecting the optimal method considering the geographical location of the person receiving care, more effective mental health support can be provided. Some or all of the above processing in the Mental Health Support Department may be performed using AI, for example, or without AI. For example, the mental health support department can input geographical location information into a generating AI and have the AI ​​select a method.

[0126] The Mental Health Support Department can analyze the social media activities of the person receiving care and provide relevant support during mental health support sessions. For example, the Mental Health Support Department can estimate the stress level from the person receiving care's social media activities and provide relevant mental health support. Furthermore, the Mental Health Support Department can identify the person's health concerns from their social media activities and provide relevant mental health support. Additionally, the Mental Health Support Department can analyze the lifestyle habits from the person receiving care's social media activities and provide relevant mental health support. By analyzing the person receiving care's social media activities and providing relevant support, more effective mental health support can be provided. Some or all of the above-described processes in the Mental Health Support Department may be performed using AI, for example, or without AI. For example, the mental health support department can have a generating AI perform the analysis of social media activity and provide support.

[0127] The personalized care plan unit can estimate the emotions of the person receiving care and adjust the way the care plan is delivered based on those estimated emotions. For example, if the person receiving care is stressed, the personalized care plan unit can provide a concise and easy-to-understand care plan. The personalized care plan unit can also provide a care plan with more detailed information if the person receiving care is relaxed. The personalized care plan unit can also provide a care plan with more concise information if the person receiving care is in a hurry. This allows for the delivery of a more appropriate care plan by adjusting the delivery method based on the emotions of the person receiving care. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generating AI (e.g., LLM) or multimodal generating AI. Some or all of the processing described above in the personalized care plan unit may be performed using AI, or not using AI. For example, the personalized care plan unit may input the emotional data of the person receiving care into the generating AI and have the generating AI adjust the method of providing the care plan.

[0128] The personalized care plan unit can select a method based on past care plan data when providing a care plan. For example, the personalized care plan unit can propose the optimal care plan from the care recipient's past care plan data. The personalized care plan unit can also select the optimal care method based on the care recipient's past care plan data. The personalized care plan unit can also analyze the care recipient's past care plan data to provide the most effective care plan. The personalized care plan unit can also analyze the care recipient's past care plan data to provide the most effective care plan. By selecting the optimal method by referring to past care plan data, a more effective care plan can be provided. Some or all of the above processing in the personalized care plan unit may be performed using AI, for example, or without AI. For example, the personalized care plan unit can input past care plan data into a generating AI and have the generating AI perform the method selection.

[0129] The personalized care plan unit can select a method based on the care recipient's current health condition when providing a care plan. For example, if the care recipient is tired, the personalized care plan unit can provide a concise and effective care plan. The personalized care plan unit can also provide a detailed care plan if the care recipient is in good health. The personalized care plan unit can also provide a quick and concise care plan if the care recipient is unwell. This allows for the provision of a more effective care plan by selecting the optimal method considering the care recipient's current health condition. Some or all of the above processing in the personalized care plan unit may be performed using AI, for example, or without AI. For example, the personalized care plan unit can input the current health condition into a generating AI and have the generating AI select the method.

[0130] The personalized care plan unit can estimate the emotions of the person receiving care and prioritize care plans based on those estimated emotions. For example, if the person receiving care is stressed, the personalized care plan unit will prioritize care plans related to stress reduction. The personalized care plan unit can also provide a balanced set of care plans related to overall health maintenance if the person receiving care is relaxed. The personalized care plan unit can also prioritize care plans related to important health indicators if the person receiving care is in a hurry. This allows for the priority of important care plans by prioritizing them based on the emotions of the person receiving care. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generating AI (e.g., LLM) or multimodal generating AI. Some or all of the processing described above in the personalized care plan unit may be performed using AI, for example, or not using AI. For example, the personalized care plan unit may input the care recipient's emotional data into the generating AI and have the generating AI determine the priorities of the care plan.

[0131] The Personalized Care Plan Unit can select a method based on the geographical location information of the person receiving care when providing a care plan. For example, if the person receiving care is at home, the Personalized Care Plan Unit can provide a care plan that can be carried out at home. The Personalized Care Plan Unit can also provide a simple care plan that can be carried out at the location where the person receiving care is out. The Personalized Care Plan Unit can also provide a care plan that can be used at a specific facility if the person receiving care is at that facility. By selecting the optimal method considering the geographical location information of the person receiving care, a more effective care plan can be provided. Some or all of the above processing in the Personalized Care Plan Unit may be performed using AI, for example, or without AI. For example, the personalized care plan department can input geographical location information into a generating AI and have the AI ​​select a method.

[0132] The Personalized Care Plan Unit can analyze the social media activity of the person receiving care and provide a relevant care plan when providing a care plan. For example, the Personalized Care Plan Unit can estimate the stress level from the person receiving care's social media activity and provide a relevant care plan. The Personalized Care Plan Unit can also understand the person receiving care's health concerns from their social media activity and provide a relevant care plan. The Personalized Care Plan Unit can also analyze the lifestyle habits from the person receiving care's social media activity and provide a relevant care plan. By analyzing the person receiving care's social media activity and providing a relevant care plan, a more effective care plan can be provided. Some or all of the above processing in the Personalized Care Plan Unit may be performed using AI, for example, or without AI. For example, the personalized care plan department can have AI generate data to analyze social media activity and provide care plans.

[0133] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0134] The data collection unit can monitor the sleep patterns of the person being cared for and evaluate the quality of their sleep. For example, it can detect the person's movements during sleep and evaluate the depth of their sleep. It can also monitor the person's heart rate and respiratory rate to detect abnormalities during sleep. Furthermore, it can monitor the person's sleep environment (e.g., room temperature and humidity) and collect data to provide a comfortable sleep environment. This can improve the quality of sleep for the person being cared for and contribute to improving their health.

[0135] The alert unit can estimate the care recipient's emotions and adjust the alert delivery method based on the estimated emotions. For example, if the care recipient is stressed, the alert can be sent with a gentle sound. If the care recipient is relaxed, the alert can be sent with a normal sound. Furthermore, if the care recipient is in a hurry, the alert can be sent quickly. In this way, by adjusting the alert delivery method based on the care recipient's emotions, more appropriate alerts can be sent.

[0136] The management department can record the meals of those receiving care and evaluate their nutritional balance. For example, the management department can record the calories and nutrients of the meals consumed by those receiving care and evaluate their nutritional balance. Furthermore, the management department can suggest improvements to the nutritional balance based on the meals of those receiving care. In addition, the management department can share the meals of those receiving care with their families and provide support for meal management. This can improve the nutritional balance of those receiving care and contribute to maintaining their health.

[0137] The management department can estimate the emotions of the person receiving care and adjust the presentation of the health report based on those estimates. For example, if the person receiving care is stressed, a concise and easy-to-read report can be provided. If the person receiving care is relaxed, a report with more detailed information can be provided. Furthermore, if the person receiving care is in a hurry, a report that gets straight to the point can be provided. In this way, by adjusting the presentation of the health report based on the person receiving care's emotions, a more appropriate report can be provided.

[0138] The suggestion unit can monitor the exercise patterns of the person receiving care and propose an appropriate exercise program. For example, the suggestion unit can record the number of steps and exercise time of the person receiving care and evaluate the amount of exercise. Furthermore, the suggestion unit can propose an appropriate exercise program based on the person receiving care's health condition. In addition, the suggestion unit can share the person receiving care's exercise patterns with the family and provide support for exercise management. This allows for proper management of the person receiving care's exercise level and contributes to maintaining their health.

[0139] The proposal function can estimate the emotions of the person receiving care and adjust the way care protocols are proposed based on those estimated emotions. For example, if the person receiving care is stressed, it can propose a concise and easy-to-understand care protocol. If the person receiving care is relaxed, it can propose a care protocol that includes detailed information. Furthermore, if the person receiving care is in a hurry, it can propose a care protocol that gets straight to the point. In this way, by adjusting the way care protocols are proposed based on the emotions of the person receiving care, more appropriate care protocols can be provided.

[0140] The Communication Support Department can estimate the emotions of the person receiving care and adjust its communication methods based on those estimates. For example, if the person receiving care is stressed, it can communicate in a calm tone. Conversely, if the person receiving care is relaxed, it can communicate in a cheerful tone. Furthermore, if the person receiving care is in a hurry, it can communicate quickly and concisely. In this way, by adjusting the communication method based on the person receiving care's emotions, it can provide more appropriate communication.

[0141] The mental health support department can estimate the emotions of those receiving care and adjust the methods of mental health support based on those estimates. For example, if the person receiving care is stressed, they can be provided with relaxation resources. If the person receiving care is relaxed, they can be provided with counseling resources. Furthermore, if the person receiving care is in a hurry, they can be provided with concise and effective mental health support. In this way, by adjusting the methods of mental health support based on the emotions of those receiving care, more appropriate mental health support can be provided.

[0142] The personalized care plan department can estimate the emotions of the person receiving care and adjust the way the care plan is delivered based on those estimates. For example, if the person receiving care is stressed, a concise and easy-to-understand care plan can be provided. If the person receiving care is relaxed, a care plan with more detailed information can be provided. Furthermore, if the person receiving care is in a hurry, a care plan that gets straight to the point can be provided. In this way, by adjusting the way the care plan is delivered based on the emotions of the person receiving care, a more appropriate care plan can be provided.

[0143] The data collection unit can analyze the care recipient's past health data and select the appropriate collection method. For example, it can collect data at specific time periods based on the care recipient's past health data. It can also determine the optimal sensor placement based on the care recipient's past health data. Furthermore, it can analyze the care recipient's past health data and focus data collection on specific health indicators. By analyzing the care recipient's past health data and selecting the optimal collection method, efficient data collection becomes possible.

[0144] The following briefly describes the processing flow for example form 2.

[0145] Step 1: The data collection unit collects health status data. This health status data includes heart rate, blood pressure, and body temperature. The data collection unit uses a heart rate sensor to measure heart rate in real time and collect data. A blood pressure sensor periodically measures blood pressure and collects data. A body temperature sensor continuously measures body temperature and collects data. Step 2: The alert unit detects anomalies based on the data collected by the collection unit and sends an alert. Anomalies include rapid changes in heart rate, abnormal blood pressure values, and abnormal body temperature. The alert unit detects rapid changes in heart rate and sends an alert. It detects abnormal blood pressure values ​​and sends an alert. It detects abnormal body temperature and sends an alert. Step 3: The management department generates health reports and sets reminders based on the data collected by the collection department. Health reports include daily reports, weekly reports, and graphs. The management department generates daily reports to track the health status of care recipients. It generates weekly reports to share health status with families. It uses graphs to visually represent health status. Step 4: The proposal unit proposes care protocols based on the data collected by the collection unit. These care protocols include dietary management and exercise programs. The proposal unit proposes dietary management protocols based on the health status of the person requiring care. It also proposes exercise programs and care protocols tailored to the person's health status.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the data collection unit, alert unit, management unit, proposal unit, communication support unit, mental health support unit, and personalized care plan unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects health status data using the sensors of the smart device 14. The alert unit detects abnormalities using the specific processing unit 290 of the data processing unit 12 and sends an alert. The management unit generates a health report and sets a reminder using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a care protocol using the specific processing unit 290 of the data processing unit 12. The communication support unit provides video call and chat functions using the control unit 46A of the smart device 14. The mental health support unit checks stress levels using the sensors of the smart device 14 and the specific processing unit 290 of the data processing unit 12 and provides relaxation and counseling resources. The personalized care plan unit provides a care plan using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the data collection unit, alert unit, management unit, proposal unit, communication support unit, mental health support unit, and personalized care plan unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects health status data using the sensors of the smart glasses 214. The alert unit detects abnormalities using the specific processing unit 290 of the data processing unit 12 and sends an alert. The management unit generates a health report and sets a reminder using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a care protocol using the specific processing unit 290 of the data processing unit 12. The communication support unit provides video call and chat functions using the control unit 46A of the smart glasses 214. The mental health support unit checks stress levels using the sensors of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12 and provides relaxation and counseling resources. The personalized care plan unit provides a care plan using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the data collection unit, alert unit, management unit, proposal unit, communication support unit, mental health support unit, and personalized care plan unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects health status data using the sensors of the headset terminal 314. The alert unit detects abnormalities using the specific processing unit 290 of the data processing unit 12 and sends an alert. The management unit generates a health report and sets a reminder using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a care protocol using the specific processing unit 290 of the data processing unit 12. The communication support unit provides video call and chat functions using the control unit 46A of the headset terminal 314. The mental health support unit checks stress levels using the sensors of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12 and provides relaxation and counseling resources. The personalized care plan unit provides a care plan using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] Each of the multiple elements described above, including the data collection unit, alert unit, management unit, proposal unit, communication support unit, mental health support unit, and personalized care plan unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects health status data using the sensors of the robot 414. The alert unit detects abnormalities using the specific processing unit 290 of the data processing unit 12 and sends an alert. The management unit generates a health report and sets a reminder using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a care protocol using the specific processing unit 290 of the data processing unit 12. The communication support unit provides video call and chat functions using the control unit 46A of the robot 414. The mental health support unit checks stress levels using the sensors of the robot 414 and the specific processing unit 290 of the data processing unit 12 and provides relaxation and counseling resources. The personalized care plan unit provides a care plan using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] (Note 1) A data collection unit that collects health status data, An alert unit detects anomalies based on the data collected by the aforementioned collection unit and sends an alert, A management unit generates a health report and sets reminders based on the data collected by the aforementioned collection unit, The system includes a proposal unit that proposes a care protocol based on the data collected by the collection unit. A system characterized by the following features. (Note 2) The alert unit is, Based on data obtained from sensors, the system detects anomalies and sends alerts to family members and medical professionals in case of emergency. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, Generate health reports to track the health status of care recipients and share them with their families. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Set reminders for medication times and medical appointments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose care protocols based on the health condition of the person requiring care. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Manage a list of caregiving tasks and track progress. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a communications support department that provides video call and chat functions for medical professionals. The system described in Appendix 1, characterized by the features described herein. (Note 8) The facility includes a mental health support department that checks the stress levels of care recipients and their families and provides relaxation and counseling resources. The system described in Appendix 1, characterized by the features described herein. (Note 9) It has a Personalized Care Planning Department that provides care plans based on the health condition and needs of those requiring care. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the emotions of the care recipient and adjusts the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze the past health data of the person receiving care and select a method for collecting that data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, filtering is performed based on the care recipient's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The system estimates the emotions of the person receiving care and prioritizes the data to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the geographical location information of the care recipient. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, analyze the social media activity of the care recipient and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The alert unit is, It estimates the emotions of the person receiving care and adjusts how alerts are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The alert unit is, When sending an alert, adjust the level of detail of the alert based on the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 18) The alert unit is, When sending an alert, apply a different alert algorithm based on the anomaly category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The alert unit is, The system estimates the emotions of the person receiving care and prioritizes alerts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alert unit is, When an alert is sent, the alert priority is determined based on when the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, When sending alerts, the order of alerts is adjusted based on the relevance of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, The system estimates the emotions of the person receiving care and adjusts the way health reports are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, When generating health reports, adjust the level of detail in the report based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When generating health reports, different reporting algorithms are applied based on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, The system estimates the care recipient's emotions and adjusts the length of the health report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When generating health reports, prioritize reports based on when the health data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, When generating health reports, the order of reports is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, The system estimates the emotions of the person receiving care and adjusts the proposed care protocol based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When proposing care protocols, adjust the level of detail in the proposal based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When proposing care protocols, different proposal algorithms are applied based on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, The system estimates the emotions of the person receiving care and determines the priority of care protocols based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When proposing care protocols, prioritize proposals based on the timing of health data submission. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When proposing care protocols, adjust the order of suggestions based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned Communication Support Department The system estimates the emotions of the person receiving care and adjusts communication methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned Communication Support Department When providing communication support, select a method based on past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned Communication Support Department When providing communication support, select a method based on the care recipient's device information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned Communication Support Department It estimates the emotions of the person receiving care and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned Communication Support Department When providing communication support, select a method based on the care recipient's device information. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned Communication Support Department When providing communication support, we offer multilingual communication based on the care recipient's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned Mental Health Support Department Estimate the emotions of the person receiving care and adjust the mental health support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned Mental Health Support Department When providing mental health support, select methods based on past mental health data. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned Mental Health Support Department When providing mental health support, select a method based on the care recipient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned Mental Health Support Department The system estimates the emotions of the person receiving care and prioritizes mental health support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned Mental Health Support Department When providing mental health support, select a method based on the geographical location of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned Mental Health Support Department When providing mental health support, analyze the care recipient's social media activity and provide relevant support. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned personalized care plan section is: The system estimates the emotions of the person receiving care and adjusts the method of providing care plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned personalized care plan section is: When providing a care plan, the method is selected based on past care plan data. The system according to appended claim 1, characterized in that... (Appended claim 48) The personalized care plan unit selects a method based on the current health status of the care recipient when providing the care plan The system according to appended claim 1, characterized in that... (Appended claim 49) The personalized care plan unit estimates the emotions of the care recipient and determines the priority of the care plan based on the estimated emotions The system according to appended claim 1, characterized in that... (Appended claim 50) The personalized care plan unit selects a method based on the geographical location information of the care recipient when providing the care plan The system according to appended claim 1, characterized in that... (Appended claim 51) The personalized care plan unit analyzes the social media activities of the care recipient and provides a relevant care plan when providing the care plan The system according to appended claim 1, characterized in that...

Explanation of reference numerals

[0218] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection unit that collects health status data, An alert unit detects anomalies based on the data collected by the aforementioned collection unit and sends an alert, A management unit generates a health report and sets reminders based on the data collected by the aforementioned collection unit, The system includes a proposal unit that proposes a care protocol based on the data collected by the collection unit. A system characterized by the following features.

2. The alert unit is, Based on data obtained from sensors, the system detects anomalies and sends alerts to family members and medical professionals in case of emergency. The system according to feature 1.

3. The aforementioned management department, Generate health reports to track the health status of care recipients and share them with their families. The system according to feature 1.

4. The aforementioned management department, Set reminders for medication times and medical appointments. The system according to feature 1.

5. The aforementioned proposal section is, We propose care protocols based on the health condition of the person requiring care. The system according to feature 1.

6. The aforementioned proposal section is, Manage a list of caregiving tasks and track progress. The system according to feature 1.

7. It includes a communications support department that provides video call and chat functions for medical professionals. The system according to feature 1.

8. The facility includes a mental health support department that checks the stress levels of care recipients and their families and provides relaxation and counseling resources. The system according to feature 1.

9. It has a Personalized Care Planning Department that provides care plans based on the health condition and needs of those requiring care. The system according to feature 1.

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