system

Abstract

The system according to this embodiment aims to constantly monitor the physical condition of the person receiving care and to respond quickly in the event of any abnormalities. [Solution] The system according to the embodiment comprises a monitoring unit, a detection unit, a suggestion unit, a provision unit, an alert unit, and an action unit. The monitoring unit monitors the physical condition of the person receiving care. The detection unit detects abnormalities based on the physical condition data monitored by the monitoring unit. The suggestion unit proposes notifications and countermeasures based on the abnormalities detected by the detection unit. The provision unit provides counseling information based on the countermeasures proposed by the suggestion unit. The alert unit issues autonomous alerts based on the counseling information provided by the provision unit. The action unit automatically takes action in emergencies based on the alerts issued by the alert unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to constantly monitor the physical condition of a person to be cared for and respond promptly when an abnormality occurs.

[0005] The system according to the embodiment aims to constantly monitor the physical condition of a person to be cared for and respond promptly when an abnormality occurs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a detection unit, a suggestion unit, a provision unit, an alert unit, and an action unit. The monitoring unit monitors the physical condition of the person receiving care. The detection unit detects abnormalities based on the physical condition data monitored by the monitoring unit. The suggestion unit proposes notifications and countermeasures based on the abnormalities detected by the detection unit. The provision unit provides counseling information based on the countermeasures proposed by the suggestion unit. The alert unit issues autonomous alerts based on the counseling information provided by the provision unit. The action unit automatically takes action in emergencies based on the alerts issued by the alert unit. [Effects of the Invention]

[0007] The system according to this embodiment can constantly monitor the physical condition of the person receiving care and respond quickly if any abnormalities occur. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The agent system according to an embodiment of the present invention is a system that constantly monitors the physical condition of a person receiving care and promptly notifies them or proposes countermeasures if any abnormalities are detected. This agent system utilizes generative AI to monitor the physical and mental state of the person receiving care in real time, and autonomously analyzes and issues alerts when abnormalities are detected. Furthermore, it provides counseling information and advice from depression specialists. Through data integration, it integrates with other health data to perform comprehensive analysis and provide highly accurate health management and proactive support. In addition, an autonomous alert system and a function that automatically takes action in emergencies are also added. This reduces the burden on caregivers and improves the quality of life of the person receiving care. For example, generative AI is used to monitor the physical and mental state of the person receiving care in real time. In this case, vital signs such as body temperature, heart rate, and blood pressure are monitored, and if abnormalities are detected, it autonomously analyzes and issues alerts. For example, if the heart rate suddenly increases, the generative AI detects the abnormality and issues an alert. Next, it provides counseling information and advice from depression specialists. For example, the generative AI analyzes the mental state of the person receiving care and provides counseling information and advice from depression specialists. This can support the mental health of the person receiving care. Furthermore, data integration allows for comprehensive analysis by combining data with other health data. For example, the generating AI integrates the vital sign data of the person being cared for with past medical records for comprehensive analysis. This enables highly accurate health management and proactive support. In addition, an autonomous alert system and a function that automatically takes action in emergencies will be added. For example, when the generating AI detects an abnormality, the autonomous alert system will issue an alert and, in the case of an emergency, will automatically take action such as calling an ambulance. This enables a rapid response and ensures the safety of the person being cared for. In this way, the agent system utilizing the generating AI can reduce the burden on caregivers and improve the quality of life for the person being cared for by constantly monitoring the health of the person being cared for and promptly notifying them and suggesting countermeasures if abnormalities are detected.This allows the agent system to constantly monitor the health of the person being cared for and quickly notify them or suggest countermeasures if any abnormalities are detected.

[0029] The agent system according to this embodiment comprises a monitoring unit, a detection unit, a suggestion unit, a provision unit, an alert unit, and an action unit. The monitoring unit monitors the physical condition of the person being cared for. The monitoring unit monitors vital signs such as body temperature, heart rate, and blood pressure. The monitoring unit can collect and analyze the physical condition data of the person being cared for in real time using a generating AI. The detection unit detects abnormalities based on the physical condition data monitored by the monitoring unit. The detection unit detects abnormalities, for example, when the heart rate suddenly increases. The detection unit can automatically detect abnormalities in physical condition data using a generating AI. The suggestion unit proposes notifications and countermeasures based on the abnormalities detected by the detection unit. The suggestion unit proposes notifications and countermeasures quickly when an abnormality is detected, for example. The suggestion unit can propose the optimal countermeasures for abnormalities using a generating AI. The provision unit provides counseling information based on the countermeasures proposed by the suggestion unit. The provision unit provides counseling information and advice from a depression specialist, for example. The provision unit can provide information to support the mental health of the person being cared for using a generating AI. The alert unit issues autonomous alerts based on counseling information provided by the service provider. For example, the alert unit issues an autonomous alert when an abnormality is detected. The alert unit can issue rapid alerts for abnormalities using a generation AI. The action unit automatically takes action in emergencies based on the alerts issued by the alert unit. For example, the action unit automatically calls an ambulance in an emergency. The action unit can automatically execute the most appropriate action in an emergency using a generation AI. As a result, the agent system according to this embodiment can constantly monitor the physical condition of the person being cared for and quickly notify or suggest countermeasures if an abnormality is detected. As a result, the agent system can reduce the burden on caregivers and improve the quality of life for the person being cared for.

[0030] The monitoring unit monitors the health of the person being cared for. For example, it monitors vital signs such as body temperature, heart rate, and blood pressure. Specifically, the monitoring unit collects these vital signs in real time using wearable devices and sensors attached to the person being cared for. Examples of wearable devices include wristband-type heart rate monitors and skin-attached thermometers. These devices transmit data to the monitoring unit via Bluetooth or Wi-Fi, which centrally manages this data. By using generative AI, the collected health data can be analyzed in real time, enabling early detection of abnormalities. For example, the generative AI can identify abnormal patterns by comparing them with past data and predict changes in health. This allows the monitoring unit to constantly monitor the health of the person being cared for and take preventative measures before abnormalities occur. Furthermore, the monitoring unit stores the collected data in the cloud, making it accessible to caregivers and medical professionals, allowing them to check the health of the person being cared for remotely. This enables the monitoring unit to efficiently and effectively manage the health of the person being cared for.

[0031] The detection unit detects abnormalities based on health data monitored by the monitoring unit. For example, the detection unit detects an abnormality when the heart rate suddenly increases. Specifically, the detection unit uses a generative AI to analyze the collected health data and automatically detect abnormal patterns and values. The generative AI uses a machine learning algorithm to learn the range of normal health data and identify abnormalities based on that. For example, when abnormal data is detected, such as when the heart rate exceeds the normal range or when the body temperature rises suddenly, the detection unit immediately issues an alert. Furthermore, the detection unit can issue different levels of alerts depending on the type and severity of the abnormality. For example, it notifies the caregiver in the case of a minor abnormality and issues an alert indicating that emergency action is required in the case of a severe abnormality. This allows the detection unit to quickly detect changes in the health of the person being cared for and prompt appropriate action. In addition, the detection unit can accumulate past abnormality data and analyze the frequency and patterns of abnormalities to predict future risks and take preventive measures. This allows the detection unit to more effectively manage the health of the person being cared for.

[0032] The suggestion unit proposes notifications and countermeasures based on abnormalities detected by the detection unit. For example, when an abnormality is detected, the suggestion unit quickly proposes notifications and countermeasures. Specifically, the suggestion unit can propose the optimal countermeasures for abnormalities using generative AI. The generative AI automatically generates countermeasures according to the type and severity of the abnormality, based on past data and medical knowledge. For example, if the heart rate rises sharply, it will suggest resting and hydration, and if the body temperature rises sharply, it will suggest taking cooling measures. The suggestion unit can also identify the cause of the abnormality and propose countermeasures based on that. For example, if the heart rate rises due to stress, it will suggest relaxation methods, and if the body temperature rises due to an infection, it will suggest seeking medical attention. In this way, the suggestion unit can propose quick and appropriate countermeasures for abnormalities in the health of the person being cared for, reducing the burden on caregivers. Furthermore, the suggestion unit notifies caregivers and medical professionals of the proposed content, enabling them to receive expert advice as needed. In this way, the suggestion unit can comprehensively support the health management of the person being cared for.

[0033] The service provider will provide counseling information based on the measures proposed by the proposal provider. For example, the service provider will provide counseling information and advice from specialists in depression. Specifically, the service provider can use a generative AI to provide information to support the mental health of the person being cared for. The generative AI will generate counseling information tailored to individual needs based on the person being cared for's physical condition data and past counseling history. For example, if a person's physical condition is deteriorating due to stress, the service provider will provide advice on relaxation methods and stress management, and if symptoms of depression are present, it will recommend that the person receive counseling from a specialist. The service provider can also propose regular counseling sessions to maintain the mental health of the person being cared for and can collaborate with specialists as needed. In this way, the service provider can comprehensively support the mental health of the person being cared for and improve their quality of life. Furthermore, the service provider can also provide counseling information to caregivers and family members to support the mental health of the person being cared for. In this way, the service provider can maintain the mental health of the person being cared for and reduce the burden on caregivers and family members.

[0034] The alert unit issues autonomous alerts based on counseling information provided by the service provider. For example, the alert unit issues an autonomous alert when an abnormality is detected. Specifically, the alert unit can issue rapid alerts for abnormalities using a generation AI. The generation AI automatically generates appropriate alert content according to the type and severity of the abnormality and notifies caregivers and medical professionals. For example, if the heart rate rises sharply, it issues an alert indicating that emergency response is necessary, and if the body temperature rises sharply, it issues an alert prompting the use of cooling measures. Furthermore, the alert unit can use different alert methods depending on the location and time of the abnormality. For example, if an abnormality occurs at night, it will use voice or vibration alerts to wake the caregiver, and if an abnormality occurs during the day, it will send an alert via smartphone notification or email. This allows the alert unit to encourage a quick and appropriate response to abnormalities and ensure the safety of the person being cared for. In addition, the alert unit can analyze past alert history and continuously improve the accuracy and effectiveness of alerts. This allows the alert unit to support a quick and appropriate response to abnormalities in the health of the person being cared for and improve the reliability and safety of the entire system.

[0035] The Action Unit automatically takes action in emergencies based on alerts issued by the Alert Unit. For example, the Action Unit can automatically call an ambulance in an emergency. Specifically, the Action Unit can use generative AI to automatically execute the most appropriate action in an emergency. The generative AI automatically determines and executes the appropriate action according to the type and severity of the abnormality. For example, if the heart rate rises sharply, it will call an ambulance and make an emergency contact with caregivers and family members. If the body temperature rises sharply, it will issue instructions to take cooling measures and, if necessary, encourage the person to seek medical attention. Furthermore, the Action Unit can pre-set an emergency action plan and respond quickly and appropriately based on it. For example, it can calculate the optimal emergency route based on the location of the person being cared for and the location of medical facilities, ensuring a smooth arrival of the ambulance. In addition, the Action Unit can record the history of emergency actions and analyze it later to continuously improve the accuracy and effectiveness of emergency responses. In this way, the Action Unit can support a quick and appropriate response to abnormal health conditions in the person being cared for, and reduce the burden on caregivers and family members.

[0036] The monitoring unit can monitor vital signs such as body temperature, heart rate, and blood pressure. For example, the monitoring unit may be equipped with a temperature sensor for measuring body temperature. The monitoring unit may also be equipped with a heart rate sensor for measuring heart rate. The monitoring unit may also be equipped with a blood pressure monitor for measuring blood pressure. This allows for a detailed understanding of the health condition of the person being cared for by monitoring vital signs. Some or all of the above-described processes in the monitoring unit may be performed using or without a generation AI. For example, the monitoring unit can input data acquired from the body temperature sensor into a generation AI to detect abnormalities in body temperature.

[0037] The detection unit can detect abnormalities when the heart rate increases rapidly. For example, the detection unit monitors changes in heart rate in real time and detects sudden increases. The detection unit can also determine abnormalities based on the rate of increase in heart rate. The detection unit can also analyze changes in heart rate over time and detect abnormalities. This allows for a rapid response by detecting sudden increases in heart rate. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input data acquired from a heart rate sensor into a generation AI to detect abnormalities in heart rate.

[0038] The suggestion unit can quickly provide notifications and propose countermeasures when an anomaly is detected. For example, the suggestion unit can issue an alert and propose countermeasures when an anomaly is detected. The suggestion unit can also propose the most appropriate countermeasures depending on the type of anomaly. The suggestion unit can also set notification priorities according to the urgency of the anomaly. This ensures the safety of the person being cared for by quickly providing notifications and proposing countermeasures when an anomaly is detected. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or not. For example, the suggestion unit can input anomaly data from the detection unit into a generation AI and propose the most appropriate countermeasures.

[0039] The service provider can provide counseling information and advice from depression specialists. For example, the service provider can collect counseling information from depression specialists and provide it to the person being cared for. The service provider can also support the mental health of the person being cared for based on the advice of depression specialists. The service provider can also provide counseling information in real time. This allows the service provider to support the mental health of the person being cared for by providing counseling information and advice from depression specialists. Some or all of the above processing in the service provider may be performed using generative AI, or it may not be performed using generative AI. For example, the service provider can use generative AI to analyze the mental state of the person being cared for and provide optimal counseling information.

[0040] The alert unit can issue autonomous alerts when an anomaly is detected. For example, the alert unit can issue an audio alert when an anomaly is detected. The alert unit can also issue message notifications. The alert unit can also change the content of the alert depending on the type of anomaly. This enables a rapid response by issuing autonomous alerts when an anomaly is detected. Some or all of the above processing in the alert unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the alert unit can input anomaly data from the detection unit into a generation AI and issue the most appropriate alert.

[0041] The action unit can take actions such as automatically calling an ambulance in an emergency. For example, the action unit can automatically call an ambulance in an emergency. The action unit can also send notifications to emergency contacts. The action unit can also automatically execute emergency response procedures. This ensures the safety of the person being cared for by automatically taking action in an emergency. Some or all of the above processing in the action unit may be performed using or without a generating AI. For example, the action unit can input abnormal data from the alert unit into a generating AI and execute the optimal action.

[0042] The monitoring unit can analyze the care recipient's past health data and select the optimal monitoring method. For example, the monitoring unit can analyze the care recipient's past vital sign data and focus monitoring on times when abnormalities are likely to occur. The monitoring unit can also refer to the care recipient's past medical records and select a monitoring method for a specific disease. The monitoring unit can also create an optimal monitoring schedule based on the care recipient's past lifestyle data. This allows for the selection of the optimal monitoring method by analyzing past health data. Some or all of the above processes in the monitoring unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the monitoring unit can input past health data into a generation AI and have the generation AI select the optimal monitoring method.

[0043] The monitoring unit can filter data based on the lifestyle and environment of the person being cared for during monitoring. For example, the monitoring unit can focus on monitoring vital sign data after meals based on the person being cared for's eating patterns. The monitoring unit can also filter vital sign data during sleep to detect abnormalities based on the person being cared for's sleep environment. The monitoring unit can also filter vital sign data after exercise to detect abnormalities based on the person being cared for's exercise habits. This allows for more accurate monitoring by filtering data based on lifestyle and environment. Some or all of the above processing in the monitoring unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the monitoring unit can input lifestyle data into a generation AI and have the generation AI perform data filtering.

[0044] The monitoring unit can prioritize acquiring highly relevant data during monitoring, taking into account the geographical location of the person being cared for. For example, if the person being cared for is out, the monitoring unit can prioritize acquiring vital sign data from that location based on their location. If the person being cared for is at home, the monitoring unit can also prioritize acquiring vital sign data from home based on their location. If the person being cared for is at a medical facility, the monitoring unit can also prioritize acquiring vital sign data from the medical facility based on their location. This allows for more accurate monitoring by prioritizing the acquisition of highly relevant data while considering geographical location. Some or all of the above processing in the monitoring unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the monitoring unit can input location data into a generation AI and have the generation AI acquire highly relevant data.

[0045] The monitoring unit can analyze the social media activity of the person being cared for during monitoring and acquire relevant data. For example, if the person being cared for posts on social media indicating they are stressed, the monitoring unit can prioritize acquiring heart rate and blood pressure data. If the person being cared for posts on social media indicating they are relaxed, the monitoring unit can also prioritize acquiring body temperature data. If the person being cared for posts on social media indicating anxiety, the monitoring unit can also prioritize acquiring heart rate and respiratory rate data. By analyzing social media activity, relevant data can be acquired, enabling more accurate monitoring. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the monitoring unit can input social media posting data into a generative AI and have the generative AI acquire the relevant data.

[0046] The detection unit can improve the accuracy of anomaly detection by considering the relationships of the person being cared for when detection occurs. For example, the detection unit can improve the accuracy of anomaly detection by analyzing the communication patterns of the person being cared for with family and friends. The detection unit can also improve the accuracy of anomaly detection by considering the relationships of the person being cared for with their caregivers. The detection unit can also improve the accuracy of anomaly detection by considering the relationships of the person being cared for with medical staff. In this way, the accuracy of anomaly detection is improved by considering the relationships. Some or all of the above processing in the detection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the detection unit can input the communication data of the person being cared for into a generative AI and have the generative AI perform an analysis of the relationships.

[0047] The detection unit can detect abnormalities by considering the attribute information of the person being cared for at the time of detection. For example, the detection unit sets criteria for abnormality detection by considering the age of the person being cared for. The detection unit can also set criteria for abnormality detection by considering the gender of the person being cared for. The detection unit can also set criteria for abnormality detection by considering the medical history of the person being cared for. This improves the accuracy of abnormality detection by considering attribute information. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input attribute data of the person being cared for into a generation AI and have the generation AI perform the setting of criteria for abnormality detection.

[0048] The detection unit can detect anomalies by considering the geographical distribution of the person being cared for. For example, if the person being cared for is out, the detection unit can detect anomalies based on location information. The detection unit can also detect anomalies based on location information if the person being cared for is at home. The detection unit can also detect anomalies based on location information if the person being cared for is in a medical facility. This improves the accuracy of anomaly detection by considering geographical distribution. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input location information data into a generation AI and have the generation AI perform anomaly detection.

[0049] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature on the person being cared for when detection occurs. For example, the detection unit can improve the accuracy of anomaly detection by referring to literature related to the medical history of the person being cared for. The detection unit can also improve the accuracy of anomaly detection by referring to literature related to the age and gender of the person being cared for. The detection unit can also improve the accuracy of anomaly detection by referring to literature related to the lifestyle of the person being cared for. In this way, the accuracy of anomaly detection is improved by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the detection unit can input relevant literature data into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0050] The proposal unit can adjust the level of detail of its proposals based on the severity of the anomaly. For example, if a major anomaly is detected, the proposal unit will propose detailed countermeasures. If a minor anomaly is detected, the proposal unit may also propose concise countermeasures. If a moderate anomaly is detected, the proposal unit may also propose countermeasures with a moderate level of detail. By adjusting the level of detail of the proposals based on the severity of the anomaly, more appropriate countermeasures are proposed. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input anomaly data into a generation AI and have the generation AI perform the adjustment of the level of detail of the proposals.

[0051] The proposal unit can apply different proposal algorithms depending on the category of the anomaly during the proposal process. For example, if an abnormality in heart rate is detected, the proposal unit can apply a heart-related proposal algorithm. If an abnormality in blood pressure is detected, the proposal unit can also apply a blood pressure-related proposal algorithm. If an abnormality in body temperature is detected, the proposal unit can also apply a body temperature-related proposal algorithm. By applying the most appropriate proposal algorithm according to the category of the anomaly, more effective countermeasures are proposed. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input abnormal data into a generative AI and have the generative AI execute the application of different proposal algorithms.

[0052] The proposal unit can determine the priority of proposals based on when the anomaly occurred. For example, the proposal unit will prioritize proposals for the most recent anomaly. The proposal unit can also make appropriate proposals for anomalies that have occurred in the past. The proposal unit can also dynamically adjust the priority of proposals based on when the anomaly occurred. This allows for a faster response by determining the priority of proposals based on when the anomaly occurred. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input anomaly occurrence time data into a generation AI and have the generation AI perform the determination of proposal priority.

[0053] The suggestion unit can adjust the order of suggestions based on the correlation of the abnormalities. For example, if abnormalities in the care recipient's heart rate and blood pressure are correlated, the suggestion unit will prioritize suggestions with high correlation. Similarly, if abnormalities in the care recipient's body temperature and respiratory rate are correlated, the suggestion unit can also prioritize suggestions with high correlation. The suggestion unit can also comprehensively analyze the care recipient's abnormality data and prioritize suggestions with high correlation. By adjusting the order of suggestions based on the correlation of the abnormalities, more effective countermeasures can be proposed. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input abnormality data into a generative AI and have the generative AI adjust the order of suggestions.

[0054] The service provider can provide optimal information by referring to the care recipient's past counseling history when providing counseling information. For example, the service provider can provide optimal counseling information based on the care recipient's past counseling history. The service provider can also provide effective advice based on the care recipient's past counseling history. The service provider can also analyze the care recipient's past counseling history and provide the most appropriate counseling information. In this way, optimal counseling information can be provided by referring to past counseling history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input past counseling history data into a generation AI and have the generation AI perform the task of providing optimal information.

[0055] The service provider can provide optimal information by considering the geographical location of the person receiving care when providing counseling information. For example, if the person receiving care is at home, the service provider can provide counseling information that can be practiced at home. If the person receiving care is out, the service provider can also provide counseling information that can be practiced at their destination. If the person receiving care is in a medical facility, the service provider can also provide counseling information that can be practiced at the medical facility. In this way, optimal counseling information can be provided by considering geographical location information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without using a generation AI. For example, the service provider can input geographical location data into a generation AI and have the generation AI perform the task of providing optimal information.

[0056] The service provider can analyze the social media activity of the person being cared for when providing counseling information. For example, if the person being cared for posts on social media indicating they are feeling stressed, the service provider can provide relaxing counseling information. If the person being cared for posts on social media indicating they are relaxed, the service provider can also provide detailed counseling information. If the person being cared for posts on social media indicating they are feeling anxious, the service provider can also provide reassuring counseling information. By analyzing social media activity, the service provider can provide more appropriate counseling information. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input social media posting data into a generative AI and have the generative AI perform the information provision.

[0057] The alert unit can select the optimal alerting method by referring to the care recipient's past alert history when an alert is issued. For example, the alert unit can select the optimal alerting method based on the care recipient's past alert history. The alert unit can also select an effective alerting method from the care recipient's past alert history. The alert unit can also analyze the care recipient's past alert history and select the most appropriate alerting method. In this way, the optimal alerting method can be selected by referring to past alert history. Some or all of the above processing in the alert unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the alert unit can input past alert history data into a generation AI and have the generation AI select the optimal alerting method.

[0058] The alert unit can issue the most appropriate alert when an alert is issued, taking into account the geographical location information of the person being cared for. For example, if the person being cared for is at home, the alert unit can issue an alert for an abnormality at home. If the person being cared for is out, the alert unit can also issue an alert for an abnormality at the location. If the person being cared for is in a medical facility, the alert unit can also issue an alert for an abnormality at the medical facility. In this way, the alert unit can issue the most appropriate alert by taking geographical location information into consideration. Some or all of the above processing in the alert unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the alert unit can input geographical location data into a generation AI and have the generation AI execute the process of issuing the most appropriate alert.

[0059] The alert unit can analyze the social media activity of the person being cared for and issue an alert when an alert is issued. For example, if the person being cared for posts on social media indicating they are feeling stressed, the alert unit will issue a gentle alert. The alert unit can also issue a normal alert if the person being cared for posts on social media indicating they are relaxed. The alert unit can also issue a reassuring alert if the person being cared for posts on social media indicating they are feeling anxious. This allows for more appropriate alerts by analyzing social media activity. Some or all of the above processing in the alert unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the alert unit can input social media posting data into a generative AI and have the generative AI issue an alert.

[0060] The action unit can select the optimal action in an emergency by referring to the care recipient's past emergency response history. For example, the action unit can select the optimal action based on the care recipient's past emergency response history. The action unit can also select an effective action from the care recipient's past emergency response history. The action unit can also analyze the care recipient's past emergency response history and select the most appropriate action. In this way, the optimal action can be selected by referring to past emergency response history. Some or all of the above processing in the action unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the action unit can input past emergency response history data into a generative AI and have the generative AI perform the selection of the optimal action.

[0061] The action unit can select the optimal action in an emergency, taking into account the geographical location of the person being cared for. For example, if the person being cared for is at home, the action unit will perform emergency response at home. If the person being cared for is out, the action unit can also perform emergency response at the location. If the person being cared for is in a medical facility, the action unit can also perform emergency response at the medical facility. In this way, the optimal action can be selected by taking geographical location information into account. Some or all of the above processing in the action unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the action unit can input geographical location data into a generative AI and have the generative AI select the optimal action.

[0062] The behavioral unit can analyze the social media activity of the person being cared for during emergencies and propose actions. For example, if the person being cared for posts on social media indicating they are feeling stressed, the behavioral unit can propose a calm response. If the person being cared for posts on social media indicating they are relaxed, the behavioral unit can also propose a normal response. If the person being cared for posts on social media indicating they are feeling anxious, the behavioral unit can also propose a reassuring response. In this way, more appropriate actions are proposed by analyzing social media activity. Some or all of the above processing in the behavioral unit may be performed using generative AI, or it may be performed without generative AI. For example, the behavioral unit can input social media posting data into generative AI and have the generative AI execute the action proposals.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The agent system can monitor not only the health of the person being cared for, but also the health of the caregiver. For example, it can monitor the caregiver's vital signs such as body temperature, heart rate, and blood pressure, and can quickly notify and suggest countermeasures if abnormalities are detected. This helps maintain the caregiver's health and improve the quality of care. It can also monitor the caregiver's stress level and provide relaxation methods and counseling information if stress levels rise. Furthermore, by integrating the caregiver's health data with the health data of the person being cared for and performing a comprehensive analysis, it can provide more accurate health management and proactive support.

[0065] The monitoring unit can collect not only health data of the person being cared for, but also environmental data. For example, it can collect environmental data such as room temperature, humidity, and illuminance to identify factors that affect the person being cared for. This allows for the proposal of appropriate measures in response to changes in the environment. Furthermore, by integrating environmental data with the person being cared for's health data and conducting a comprehensive analysis, it is possible to provide more accurate health management and proactive support. In addition, based on the environmental data, it is possible to make suggestions for maintaining a comfortable living environment for the person being cared for.

[0066] The detection unit can analyze not only the physical condition data of the person being cared for, but also their behavioral data. For example, it can analyze the walking patterns and activity levels of the person being cared for, and if an abnormality is detected, it can quickly notify the caregiver and suggest countermeasures. This can reduce the risk of falls for the person being cared for and support a safer life. Furthermore, by integrating behavioral data with the physical condition data of the person being cared for and performing a comprehensive analysis, it can provide more accurate health management and proactive support. In addition, it can also suggest improvements to the lifestyle habits of the person being cared for based on the behavioral data.

[0067] The proposal department can make dietary and exercise suggestions based on the health data of the person receiving care. For example, it can suggest nutritionally balanced meal menus tailored to the person's health condition, thereby supporting the maintenance of their health. It can also suggest appropriate exercise programs tailored to the person's health condition, thereby supporting the improvement of their physical fitness and rehabilitation. Furthermore, by providing dietary and exercise suggestions to caregivers, it can also support the maintenance of their health.

[0068] The service provider can suggest relaxation methods based on the health data of the person being cared for. For example, if the stress level of the person being cared for increases, relaxation music or meditation methods can be suggested. This can support the mental health of the person being cared for. Furthermore, massage or aromatherapy methods tailored to the person being cared for can also be suggested. This promotes relaxation and helps maintain the physical and mental health of the person being cared for. In addition, by providing relaxation method suggestions to caregivers, it can also support the reduction of caregiver stress.

[0069] The alert unit can suggest specific actions to caregivers when an abnormality is detected, based on the health data of the person being cared for. For example, if the heart rate suddenly increases, it can suggest specific measures to stabilize the heart rate to the caregiver. This allows caregivers to respond quickly and appropriately. It can also suggest to caregivers how to contact emergency contacts when an abnormality is detected. This supports quick and appropriate responses in emergencies. Furthermore, it can suggest to caregivers how to use necessary medical equipment when an abnormality is detected.

[0070] The behavioral unit can automate emergency actions based on the health data of the person being cared for. For example, if the heart rate suddenly increases, it can automatically call an ambulance. This enables a rapid response and ensures the safety of the person being cared for. It can also automatically send notifications to caregivers in emergencies to prompt appropriate action. This allows caregivers to respond quickly and appropriately. Furthermore, in emergencies, it can automatically contact medical institutions and arrange necessary medical support. This ensures the safety of the person being cared for.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The monitoring unit monitors the physical condition of the person receiving care. For example, it monitors vital signs such as body temperature, heart rate, and blood pressure, collects the data in real time using AI generation, and analyzes it. Step 2: The detection unit detects abnormalities based on the health data monitored by the monitoring unit. For example, it detects an abnormality when the heart rate suddenly increases and automatically detects it using a generation AI. Step 3: The proposal unit proposes notifications and countermeasures based on the anomalies detected by the detection unit. For example, when an anomaly is detected, it quickly proposes notifications and countermeasures, and uses generation AI to suggest the optimal countermeasures. Step 4: The provisioning department provides counseling information based on the measures proposed by the proposaling department. For example, it provides counseling information and advice from depression specialists and provides information to support mental health using generated AI. Step 5: The alert unit issues autonomous alerts based on the counseling information provided by the service provider. For example, it issues an autonomous alert when an anomaly is detected and uses a generation AI to issue a rapid alert. Step 6: The Action Unit automatically takes action in emergencies based on alerts issued by the Alert Unit. For example, it may automatically call an ambulance in an emergency and use a generating AI to automatically execute the optimal action.

[0073] (Example of form 2) The agent system according to an embodiment of the present invention is a system that constantly monitors the physical condition of a person receiving care and promptly notifies them or proposes countermeasures if any abnormalities are detected. This agent system utilizes generative AI to monitor the physical and mental state of the person receiving care in real time, and autonomously analyzes and issues alerts when abnormalities are detected. Furthermore, it provides counseling information and advice from depression specialists. Through data integration, it integrates with other health data to perform comprehensive analysis and provide highly accurate health management and proactive support. In addition, an autonomous alert system and a function that automatically takes action in emergencies are also added. This reduces the burden on caregivers and improves the quality of life of the person receiving care. For example, generative AI is used to monitor the physical and mental state of the person receiving care in real time. In this case, vital signs such as body temperature, heart rate, and blood pressure are monitored, and if abnormalities are detected, it autonomously analyzes and issues alerts. For example, if the heart rate suddenly increases, the generative AI detects the abnormality and issues an alert. Next, it provides counseling information and advice from depression specialists. For example, the generative AI analyzes the mental state of the person receiving care and provides counseling information and advice from depression specialists. This can support the mental health of the person receiving care. Furthermore, data integration allows for comprehensive analysis by combining data with other health data. For example, the generating AI integrates the vital sign data of the person being cared for with past medical records for comprehensive analysis. This enables highly accurate health management and proactive support. In addition, an autonomous alert system and a function that automatically takes action in emergencies will be added. For example, when the generating AI detects an abnormality, the autonomous alert system will issue an alert and, in the case of an emergency, will automatically take action such as calling an ambulance. This enables a rapid response and ensures the safety of the person being cared for. In this way, the agent system utilizing the generating AI can reduce the burden on caregivers and improve the quality of life for the person being cared for by constantly monitoring the health of the person being cared for and promptly notifying them and suggesting countermeasures if abnormalities are detected.This allows the agent system to constantly monitor the health of the person being cared for and quickly notify them or suggest countermeasures if any abnormalities are detected.

[0074] The agent system according to this embodiment comprises a monitoring unit, a detection unit, a suggestion unit, a provision unit, an alert unit, and an action unit. The monitoring unit monitors the physical condition of the person being cared for. The monitoring unit monitors vital signs such as body temperature, heart rate, and blood pressure. The monitoring unit can collect and analyze the physical condition data of the person being cared for in real time using a generating AI. The detection unit detects abnormalities based on the physical condition data monitored by the monitoring unit. The detection unit detects abnormalities, for example, when the heart rate suddenly increases. The detection unit can automatically detect abnormalities in physical condition data using a generating AI. The suggestion unit proposes notifications and countermeasures based on the abnormalities detected by the detection unit. The suggestion unit proposes notifications and countermeasures quickly when an abnormality is detected, for example. The suggestion unit can propose the optimal countermeasures for abnormalities using a generating AI. The provision unit provides counseling information based on the countermeasures proposed by the suggestion unit. The provision unit provides counseling information and advice from a depression specialist, for example. The provision unit can provide information to support the mental health of the person being cared for using a generating AI. The alert unit issues autonomous alerts based on counseling information provided by the service provider. For example, the alert unit issues an autonomous alert when an abnormality is detected. The alert unit can issue rapid alerts for abnormalities using a generation AI. The action unit automatically takes action in emergencies based on the alerts issued by the alert unit. For example, the action unit automatically calls an ambulance in an emergency. The action unit can automatically execute the most appropriate action in an emergency using a generation AI. As a result, the agent system according to this embodiment can constantly monitor the physical condition of the person being cared for and quickly notify or suggest countermeasures if an abnormality is detected. As a result, the agent system can reduce the burden on caregivers and improve the quality of life for the person being cared for.

[0075] The monitoring unit monitors the health of the person being cared for. For example, it monitors vital signs such as body temperature, heart rate, and blood pressure. Specifically, the monitoring unit collects these vital signs in real time using wearable devices and sensors attached to the person being cared for. Examples of wearable devices include wristband-type heart rate monitors and skin-attached thermometers. These devices transmit data to the monitoring unit via Bluetooth or Wi-Fi, which centrally manages this data. By using generative AI, the collected health data can be analyzed in real time, enabling early detection of abnormalities. For example, the generative AI can identify abnormal patterns by comparing them with past data and predict changes in health. This allows the monitoring unit to constantly monitor the health of the person being cared for and take preventative measures before abnormalities occur. Furthermore, the monitoring unit stores the collected data in the cloud, making it accessible to caregivers and medical professionals, allowing them to check the health of the person being cared for remotely. This enables the monitoring unit to efficiently and effectively manage the health of the person being cared for.

[0076] The detection unit detects abnormalities based on health data monitored by the monitoring unit. For example, the detection unit detects an abnormality when the heart rate suddenly increases. Specifically, the detection unit uses a generative AI to analyze the collected health data and automatically detect abnormal patterns and values. The generative AI uses a machine learning algorithm to learn the range of normal health data and identify abnormalities based on that. For example, when abnormal data is detected, such as when the heart rate exceeds the normal range or when the body temperature rises suddenly, the detection unit immediately issues an alert. Furthermore, the detection unit can issue different levels of alerts depending on the type and severity of the abnormality. For example, it notifies the caregiver in the case of a minor abnormality and issues an alert indicating that emergency action is required in the case of a severe abnormality. This allows the detection unit to quickly detect changes in the health of the person being cared for and prompt appropriate action. In addition, the detection unit can accumulate past abnormality data and analyze the frequency and patterns of abnormalities to predict future risks and take preventive measures. This allows the detection unit to more effectively manage the health of the person being cared for.

[0077] The suggestion unit proposes notifications and countermeasures based on abnormalities detected by the detection unit. For example, when an abnormality is detected, the suggestion unit quickly proposes notifications and countermeasures. Specifically, the suggestion unit can propose the optimal countermeasures for abnormalities using generative AI. The generative AI automatically generates countermeasures according to the type and severity of the abnormality, based on past data and medical knowledge. For example, if the heart rate rises sharply, it will suggest resting and hydration, and if the body temperature rises sharply, it will suggest taking cooling measures. The suggestion unit can also identify the cause of the abnormality and propose countermeasures based on that. For example, if the heart rate rises due to stress, it will suggest relaxation methods, and if the body temperature rises due to an infection, it will suggest seeking medical attention. In this way, the suggestion unit can propose quick and appropriate countermeasures for abnormalities in the health of the person being cared for, reducing the burden on caregivers. Furthermore, the suggestion unit notifies caregivers and medical professionals of the proposed content, enabling them to receive expert advice as needed. In this way, the suggestion unit can comprehensively support the health management of the person being cared for.

[0078] The service provider will provide counseling information based on the measures proposed by the proposal provider. For example, the service provider will provide counseling information and advice from specialists in depression. Specifically, the service provider can use a generative AI to provide information to support the mental health of the person being cared for. The generative AI will generate counseling information tailored to individual needs based on the person being cared for's physical condition data and past counseling history. For example, if a person's physical condition is deteriorating due to stress, the service provider will provide advice on relaxation methods and stress management, and if symptoms of depression are present, it will recommend that the person receive counseling from a specialist. The service provider can also propose regular counseling sessions to maintain the mental health of the person being cared for and can collaborate with specialists as needed. In this way, the service provider can comprehensively support the mental health of the person being cared for and improve their quality of life. Furthermore, the service provider can also provide counseling information to caregivers and family members to support the mental health of the person being cared for. In this way, the service provider can maintain the mental health of the person being cared for and reduce the burden on caregivers and family members.

[0079] The alert unit issues autonomous alerts based on counseling information provided by the service provider. For example, the alert unit issues an autonomous alert when an abnormality is detected. Specifically, the alert unit can issue rapid alerts for abnormalities using a generation AI. The generation AI automatically generates appropriate alert content according to the type and severity of the abnormality and notifies caregivers and medical professionals. For example, if the heart rate rises sharply, it issues an alert indicating that emergency response is necessary, and if the body temperature rises sharply, it issues an alert prompting the use of cooling measures. Furthermore, the alert unit can use different alert methods depending on the location and time of the abnormality. For example, if an abnormality occurs at night, it will use voice or vibration alerts to wake the caregiver, and if an abnormality occurs during the day, it will send an alert via smartphone notification or email. This allows the alert unit to encourage a quick and appropriate response to abnormalities and ensure the safety of the person being cared for. In addition, the alert unit can analyze past alert history and continuously improve the accuracy and effectiveness of alerts. This allows the alert unit to support a quick and appropriate response to abnormalities in the health of the person being cared for and improve the reliability and safety of the entire system.

[0080] The Action Unit automatically takes action in emergencies based on alerts issued by the Alert Unit. For example, the Action Unit can automatically call an ambulance in an emergency. Specifically, the Action Unit can use generative AI to automatically execute the most appropriate action in an emergency. The generative AI automatically determines and executes the appropriate action according to the type and severity of the abnormality. For example, if the heart rate rises sharply, it will call an ambulance and make an emergency contact with caregivers and family members. If the body temperature rises sharply, it will issue instructions to take cooling measures and, if necessary, encourage the person to seek medical attention. Furthermore, the Action Unit can pre-set an emergency action plan and respond quickly and appropriately based on it. For example, it can calculate the optimal emergency route based on the location of the person being cared for and the location of medical facilities, ensuring a smooth arrival of the ambulance. In addition, the Action Unit can record the history of emergency actions and analyze it later to continuously improve the accuracy and effectiveness of emergency responses. In this way, the Action Unit can support a quick and appropriate response to abnormal health conditions in the person being cared for, and reduce the burden on caregivers and family members.

[0081] The monitoring unit can monitor vital signs such as body temperature, heart rate, and blood pressure. For example, the monitoring unit may be equipped with a temperature sensor for measuring body temperature. The monitoring unit may also be equipped with a heart rate sensor for measuring heart rate. The monitoring unit may also be equipped with a blood pressure monitor for measuring blood pressure. This allows for a detailed understanding of the health condition of the person being cared for by monitoring vital signs. Some or all of the above-described processes in the monitoring unit may be performed using or without a generation AI. For example, the monitoring unit can input data acquired from the body temperature sensor into a generation AI to detect abnormalities in body temperature.

[0082] The detection unit can detect abnormalities when the heart rate increases rapidly. For example, the detection unit monitors changes in heart rate in real time and detects sudden increases. The detection unit can also determine abnormalities based on the rate of increase in heart rate. The detection unit can also analyze changes in heart rate over time and detect abnormalities. This allows for a rapid response by detecting sudden increases in heart rate. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input data acquired from a heart rate sensor into a generation AI to detect abnormalities in heart rate.

[0083] The suggestion unit can quickly provide notifications and propose countermeasures when an anomaly is detected. For example, the suggestion unit can issue an alert and propose countermeasures when an anomaly is detected. The suggestion unit can also propose the most appropriate countermeasures depending on the type of anomaly. The suggestion unit can also set notification priorities according to the urgency of the anomaly. This ensures the safety of the person being cared for by quickly providing notifications and proposing countermeasures when an anomaly is detected. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or not. For example, the suggestion unit can input anomaly data from the detection unit into a generation AI and propose the most appropriate countermeasures.

[0084] The service provider can provide counseling information and advice from depression specialists. For example, the service provider can collect counseling information from depression specialists and provide it to the person being cared for. The service provider can also support the mental health of the person being cared for based on the advice of depression specialists. The service provider can also provide counseling information in real time. This allows the service provider to support the mental health of the person being cared for by providing counseling information and advice from depression specialists. Some or all of the above processing in the service provider may be performed using generative AI, or it may not be performed using generative AI. For example, the service provider can use generative AI to analyze the mental state of the person being cared for and provide optimal counseling information.

[0085] The alert unit can issue autonomous alerts when an anomaly is detected. For example, the alert unit can issue an audio alert when an anomaly is detected. The alert unit can also issue message notifications. The alert unit can also change the content of the alert depending on the type of anomaly. This enables a rapid response by issuing autonomous alerts when an anomaly is detected. Some or all of the above processing in the alert unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the alert unit can input anomaly data from the detection unit into a generation AI and issue the most appropriate alert.

[0086] The action unit can take actions such as automatically calling an ambulance in an emergency. For example, the action unit can automatically call an ambulance in an emergency. The action unit can also send notifications to emergency contacts. The action unit can also automatically execute emergency response procedures. This ensures the safety of the person being cared for by automatically taking action in an emergency. Some or all of the above processing in the action unit may be performed using or without a generating AI. For example, the action unit can input abnormal data from the alert unit into a generating AI and execute the optimal action.

[0087] The monitoring unit can estimate the emotions of the person being cared for and adjust the monitoring frequency based on the estimated emotions. For example, if the person being cared for is stressed, the monitoring unit can increase the monitoring frequency to quickly detect changes in their physical condition. If the person being cared for is relaxed, the monitoring unit can also reduce the monitoring frequency to alleviate the burden. If the person being cared for is anxious, the monitoring unit can appropriately adjust the monitoring frequency to provide a sense of security. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the emotions of the person being cared for. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using generative AI or not. For example, the monitoring unit can input facial expression data of the person being cared for into the generative AI and have the generative AI perform emotion estimation.

[0088] The monitoring unit can analyze the care recipient's past health data and select the optimal monitoring method. For example, the monitoring unit can analyze the care recipient's past vital sign data and focus monitoring on times when abnormalities are likely to occur. The monitoring unit can also refer to the care recipient's past medical records and select a monitoring method for a specific disease. The monitoring unit can also create an optimal monitoring schedule based on the care recipient's past lifestyle data. This allows for the selection of the optimal monitoring method by analyzing past health data. Some or all of the above processes in the monitoring unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the monitoring unit can input past health data into a generation AI and have the generation AI select the optimal monitoring method.

[0089] The monitoring unit can filter data based on the lifestyle and environment of the person being cared for during monitoring. For example, the monitoring unit can focus on monitoring vital sign data after meals based on the person being cared for's eating patterns. The monitoring unit can also filter vital sign data during sleep to detect abnormalities based on the person being cared for's sleep environment. The monitoring unit can also filter vital sign data after exercise to detect abnormalities based on the person being cared for's exercise habits. This allows for more accurate monitoring by filtering data based on lifestyle and environment. Some or all of the above processing in the monitoring unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the monitoring unit can input lifestyle data into a generation AI and have the generation AI perform data filtering.

[0090] The monitoring unit can estimate the emotions of the person being cared for and determine the priority of vital signs to monitor based on the estimated emotions. For example, if the person being cared for is stressed, the monitoring unit may prioritize monitoring heart rate and blood pressure. If the person being cared for is relaxed, the monitoring unit may also prioritize monitoring body temperature. If the person being cared for is anxious, the monitoring unit may also prioritize monitoring heart rate and respiratory rate. This allows for more appropriate monitoring by determining the priority of vital signs to monitor according to the emotions of the person being cared for. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using or without generative AI. For example, the monitoring unit can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0091] The monitoring unit can prioritize acquiring highly relevant data during monitoring, taking into account the geographical location of the person being cared for. For example, if the person being cared for is out, the monitoring unit can prioritize acquiring vital sign data from that location based on their location. If the person being cared for is at home, the monitoring unit can also prioritize acquiring vital sign data from home based on their location. If the person being cared for is at a medical facility, the monitoring unit can also prioritize acquiring vital sign data from the medical facility based on their location. This allows for more accurate monitoring by prioritizing the acquisition of highly relevant data while considering geographical location. Some or all of the above processing in the monitoring unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the monitoring unit can input location data into a generation AI and have the generation AI acquire highly relevant data.

[0092] The monitoring unit can analyze the social media activity of the person being cared for during monitoring and acquire relevant data. For example, if the person being cared for posts on social media indicating they are stressed, the monitoring unit can prioritize acquiring heart rate and blood pressure data. If the person being cared for posts on social media indicating they are relaxed, the monitoring unit can also prioritize acquiring body temperature data. If the person being cared for posts on social media indicating anxiety, the monitoring unit can also prioritize acquiring heart rate and respiratory rate data. By analyzing social media activity, relevant data can be acquired, enabling more accurate monitoring. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the monitoring unit can input social media posting data into a generative AI and have the generative AI acquire the relevant data.

[0093] The detection unit can estimate the emotions of the person being cared for and adjust the abnormality detection criteria based on the estimated emotions. For example, if the person being cared for is stressed, the detection unit may set stricter abnormality detection criteria for heart rate and blood pressure. If the person being cared for is relaxed, the detection unit may also set looser abnormality detection criteria for body temperature. If the person being cared for is anxious, the detection unit may also set stricter abnormality detection criteria for heart rate and respiratory rate. By adjusting the abnormality detection criteria according to the emotions of the person being cared for, more accurate abnormality detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using or without a generative AI. For example, the detection unit can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0094] The detection unit can improve the accuracy of anomaly detection by considering the relationships of the person being cared for when detection occurs. For example, the detection unit can improve the accuracy of anomaly detection by analyzing the communication patterns of the person being cared for with family and friends. The detection unit can also improve the accuracy of anomaly detection by considering the relationships of the person being cared for with their caregivers. The detection unit can also improve the accuracy of anomaly detection by considering the relationships of the person being cared for with medical staff. In this way, the accuracy of anomaly detection is improved by considering the relationships. Some or all of the above processing in the detection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the detection unit can input the communication data of the person being cared for into a generative AI and have the generative AI perform an analysis of the relationships.

[0095] The detection unit can detect abnormalities by considering the attribute information of the person being cared for at the time of detection. For example, the detection unit sets criteria for abnormality detection by considering the age of the person being cared for. The detection unit can also set criteria for abnormality detection by considering the gender of the person being cared for. The detection unit can also set criteria for abnormality detection by considering the medical history of the person being cared for. This improves the accuracy of abnormality detection by considering attribute information. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input attribute data of the person being cared for into a generation AI and have the generation AI perform the setting of criteria for abnormality detection.

[0096] The detection unit can estimate the emotions of the person being cared for and adjust the order in which abnormality detection results are displayed based on the estimated emotions. For example, if the person being cared for is stressed, the detection unit can prioritize displaying abnormal heart rate and blood pressure detection results. If the person being cared for is relaxed, the detection unit can also prioritize displaying abnormal body temperature detection results. If the person being cared for is anxious, the detection unit can also prioritize displaying abnormal heart rate and respiratory rate detection results. This allows for the provision of more appropriate information by adjusting the order in which abnormality detection results are displayed according to the emotions of the person being cared for. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using a generative AI or not. For example, the detection unit can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0097] The detection unit can detect anomalies by considering the geographical distribution of the person being cared for. For example, if the person being cared for is out, the detection unit can detect anomalies based on location information. The detection unit can also detect anomalies based on location information if the person being cared for is at home. The detection unit can also detect anomalies based on location information if the person being cared for is in a medical facility. This improves the accuracy of anomaly detection by considering geographical distribution. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input location information data into a generation AI and have the generation AI perform anomaly detection.

[0098] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature on the person being cared for when detection occurs. For example, the detection unit can improve the accuracy of anomaly detection by referring to literature related to the medical history of the person being cared for. The detection unit can also improve the accuracy of anomaly detection by referring to literature related to the age and gender of the person being cared for. The detection unit can also improve the accuracy of anomaly detection by referring to literature related to the lifestyle of the person being cared for. In this way, the accuracy of anomaly detection is improved by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the detection unit can input relevant literature data into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0099] The suggestion unit can estimate the emotions of the person being cared for and adjust the expression of the suggestion based on the estimated emotions. For example, if the person being cared for is stressed, the suggestion unit will suggest a simple and easy-to-understand expression. If the person being cared for is relaxed, the suggestion unit may also suggest an expression that includes detailed information. If the person being cared for is anxious, the suggestion unit may also suggest an expression that provides a sense of security. By adjusting the expression of the suggestion according to the emotions of the person being cared for, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0100] The proposal unit can adjust the level of detail of its proposals based on the severity of the anomaly. For example, if a major anomaly is detected, the proposal unit will propose detailed countermeasures. If a minor anomaly is detected, the proposal unit may also propose concise countermeasures. If a moderate anomaly is detected, the proposal unit may also propose countermeasures with a moderate level of detail. By adjusting the level of detail of the proposals based on the severity of the anomaly, more appropriate countermeasures are proposed. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input anomaly data into a generation AI and have the generation AI perform the adjustment of the level of detail of the proposals.

[0101] The proposal unit can apply different proposal algorithms depending on the category of the anomaly during the proposal process. For example, if an abnormality in heart rate is detected, the proposal unit can apply a heart-related proposal algorithm. If an abnormality in blood pressure is detected, the proposal unit can also apply a blood pressure-related proposal algorithm. If an abnormality in body temperature is detected, the proposal unit can also apply a body temperature-related proposal algorithm. By applying the most appropriate proposal algorithm according to the category of the anomaly, more effective countermeasures are proposed. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input abnormal data into a generative AI and have the generative AI execute the application of different proposal algorithms.

[0102] The suggestion unit can estimate the emotions of the person being cared for and adjust the length of the suggestion based on the estimated emotions. For example, if the person being cared for is stressed, the suggestion unit will make a short, concise suggestion. If the person being cared for is relaxed, the suggestion unit may make a longer suggestion that includes detailed explanations. If the person being cared for is anxious, the suggestion unit may make a suggestion of an appropriate length to provide reassurance. By adjusting the length of the suggestion according to the emotions of the person being cared for, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0103] The proposal unit can determine the priority of proposals based on when the anomaly occurred. For example, the proposal unit will prioritize proposals for the most recent anomaly. The proposal unit can also make appropriate proposals for anomalies that have occurred in the past. The proposal unit can also dynamically adjust the priority of proposals based on when the anomaly occurred. This allows for a faster response by determining the priority of proposals based on when the anomaly occurred. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input anomaly occurrence time data into a generation AI and have the generation AI perform the determination of proposal priority.

[0104] The suggestion unit can adjust the order of suggestions based on the correlation of the abnormalities. For example, if abnormalities in the care recipient's heart rate and blood pressure are correlated, the suggestion unit will prioritize suggestions with high correlation. Similarly, if abnormalities in the care recipient's body temperature and respiratory rate are correlated, the suggestion unit can also prioritize suggestions with high correlation. The suggestion unit can also comprehensively analyze the care recipient's abnormality data and prioritize suggestions with high correlation. By adjusting the order of suggestions based on the correlation of the abnormalities, more effective countermeasures can be proposed. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input abnormality data into a generative AI and have the generative AI adjust the order of suggestions.

[0105] The service provider can estimate the emotions of the person being cared for and adjust the method of providing counseling information based on the estimated emotions. For example, if the person being cared for is feeling stressed, the service provider can provide relaxing counseling information. If the person being cared for is relaxed, the service provider can also provide detailed counseling information. If the person being cared for is feeling anxious, the service provider can also provide reassuring counseling information. By adjusting the method of providing counseling information according to the emotions of the person being cared for, more appropriate counseling becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using a generative AI or not using a generative AI. For example, the service provider can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0106] The service provider can provide optimal information by referring to the care recipient's past counseling history when providing counseling information. For example, the service provider can provide optimal counseling information based on the care recipient's past counseling history. The service provider can also provide effective advice based on the care recipient's past counseling history. The service provider can also analyze the care recipient's past counseling history and provide the most appropriate counseling information. In this way, optimal counseling information can be provided by referring to past counseling history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input past counseling history data into a generation AI and have the generation AI perform the task of providing optimal information.

[0107] The service provider can customize the information provided based on the current mental state of the person being cared for. For example, if the person being cared for is feeling stressed, the service provider can customize and provide relaxing counseling information. If the person being cared for is relaxed, the service provider can also customize and provide detailed counseling information. If the person being cared for is feeling anxious, the service provider can also customize and provide reassuring counseling information. By customizing the information based on the current mental state, more appropriate counseling becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0108] The service provider can estimate the emotions of the person being cared for and prioritize counseling information based on the estimated emotions. For example, if the person being cared for is feeling stressed, the service provider will prioritize providing relaxing counseling information. If the person being cared for is relaxed, the service provider may also prioritize providing detailed counseling information. If the person being cared for is feeling anxious, the service provider may also prioritize providing reassuring counseling information. By prioritizing counseling information according to the emotions of the person being cared for, more appropriate counseling becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0109] The service provider can provide optimal information by considering the geographical location of the person receiving care when providing counseling information. For example, if the person receiving care is at home, the service provider can provide counseling information that can be practiced at home. If the person receiving care is out, the service provider can also provide counseling information that can be practiced at their destination. If the person receiving care is in a medical facility, the service provider can also provide counseling information that can be practiced at the medical facility. In this way, optimal counseling information can be provided by considering geographical location information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without using a generation AI. For example, the service provider can input geographical location data into a generation AI and have the generation AI perform the task of providing optimal information.

[0110] The service provider can analyze the social media activity of the person being cared for when providing counseling information. For example, if the person being cared for posts on social media indicating they are feeling stressed, the service provider can provide relaxing counseling information. If the person being cared for posts on social media indicating they are relaxed, the service provider can also provide detailed counseling information. If the person being cared for posts on social media indicating they are feeling anxious, the service provider can also provide reassuring counseling information. By analyzing social media activity, the service provider can provide more appropriate counseling information. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input social media posting data into a generative AI and have the generative AI perform the information provision.

[0111] The alert unit can estimate the emotions of the person being cared for and adjust the method of issuing alerts based on the estimated emotions. For example, if the person being cared for is stressed, the alert unit will issue an alert with a gentle sound. If the person being cared for is relaxed, the alert unit can also issue an alert with a normal sound. If the person being cared for is anxious, the alert unit can also issue an alert with a reassuring sound. By adjusting the method of issuing alerts according to the emotions of the person being cared for, more appropriate alerts 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 processing in the alert unit may be performed using generative AI or not. For example, the alert unit can input facial expression data of the person being cared for into the generative AI and have the generative AI perform emotion estimation.

[0112] The alert unit can select the optimal alerting method by referring to the care recipient's past alert history when an alert is issued. For example, the alert unit can select the optimal alerting method based on the care recipient's past alert history. The alert unit can also select an effective alerting method from the care recipient's past alert history. The alert unit can also analyze the care recipient's past alert history and select the most appropriate alerting method. In this way, the optimal alerting method can be selected by referring to past alert history. Some or all of the above processing in the alert unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the alert unit can input past alert history data into a generation AI and have the generation AI select the optimal alerting method.

[0113] The alert unit can customize the content of an alert based on the current situation of the person being cared for when an alert is issued. For example, if the person being cared for is stressed, the alert unit can issue a calm alert. If the person being cared for is relaxed, the alert unit can also issue a normal alert. If the person being cared for is anxious, the alert unit can also issue a reassuring alert. By customizing the content of the alert based on the current situation, more appropriate alerts 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 processing in the alert unit may be performed using generative AI or not. For example, the alert unit can input facial expression data of the person being cared for into the generative AI and have the generative AI perform emotion estimation.

[0114] The alert unit can estimate the emotions of the person being cared for and determine the priority of alerts based on the estimated emotions. For example, if the person being cared for is stressed, the alert unit will prioritize issuing important alerts. If the person being cared for is relaxed, the alert unit can also issue normal alerts. If the person being cared for is anxious, the alert unit can also prioritize issuing reassuring alerts. This allows for more appropriate alerts by prioritizing alerts according to the emotions of the person being cared for. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using or without a generative AI. For example, the alert unit can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0115] The alert unit can issue the most appropriate alert when an alert is issued, taking into account the geographical location information of the person being cared for. For example, if the person being cared for is at home, the alert unit can issue an alert for an abnormality at home. If the person being cared for is out, the alert unit can also issue an alert for an abnormality at the location. If the person being cared for is in a medical facility, the alert unit can also issue an alert for an abnormality at the medical facility. In this way, the alert unit can issue the most appropriate alert by taking geographical location information into consideration. Some or all of the above processing in the alert unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the alert unit can input geographical location data into a generation AI and have the generation AI execute the process of issuing the most appropriate alert.

[0116] The alert unit can analyze the social media activity of the person being cared for and issue an alert when an alert is issued. For example, if the person being cared for posts on social media indicating they are feeling stressed, the alert unit will issue a gentle alert. The alert unit can also issue a normal alert if the person being cared for posts on social media indicating they are relaxed. The alert unit can also issue a reassuring alert if the person being cared for posts on social media indicating they are feeling anxious. This allows for more appropriate alerts by analyzing social media activity. Some or all of the above processing in the alert unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the alert unit can input social media posting data into a generative AI and have the generative AI issue an alert.

[0117] The behavioral unit can estimate the emotions of the person being cared for and adjust its emergency actions based on the estimated emotions. For example, if the person being cared for is stressed, the behavioral unit will respond calmly. If the person being cared for is relaxed, the behavioral unit can also respond normally. If the person being cared for is anxious, the behavioral unit can also respond in a way that provides reassurance. This allows for more appropriate responses by adjusting emergency actions according to the emotions of the person being cared for. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the behavioral unit may be performed using or without a generative AI. For example, the behavioral unit can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0118] The action unit can select the optimal action in an emergency by referring to the care recipient's past emergency response history. For example, the action unit can select the optimal action based on the care recipient's past emergency response history. The action unit can also select an effective action from the care recipient's past emergency response history. The action unit can also analyze the care recipient's past emergency response history and select the most appropriate action. In this way, the optimal action can be selected by referring to past emergency response history. Some or all of the above processing in the action unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the action unit can input past emergency response history data into a generative AI and have the generative AI perform the selection of the optimal action.

[0119] The behavioral unit can customize its actions in emergency situations based on the current situation of the person being cared for. For example, if the person being cared for is stressed, the behavioral unit will respond calmly. If the person being cared for is relaxed, the behavioral unit can also respond normally. If the person being cared for is anxious, the behavioral unit can also respond in a way that provides reassurance. This allows for more appropriate responses by customizing actions based on the current situation. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the behavioral unit may be performed using generative AI or not. For example, the behavioral unit can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0120] The behavioral unit can estimate the emotions of the person being cared for and determine the priority of emergency actions based on the estimated emotions. For example, if the person being cared for is stressed, the behavioral unit will prioritize important actions. If the person being cared for is relaxed, the behavioral unit can also perform normal actions. If the person being cared for is anxious, the behavioral unit can also prioritize actions that provide reassurance. This allows for a more appropriate response by determining the priority of emergency actions according to the emotions of the person being cared for. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the behavioral unit may be performed using or without a generative AI. For example, the behavioral unit can input facial expression data of the person being cared for into a generative AI and have the generative AI perform emotion estimation.

[0121] The action unit can select the optimal action in an emergency, taking into account the geographical location of the person being cared for. For example, if the person being cared for is at home, the action unit will perform emergency response at home. If the person being cared for is out, the action unit can also perform emergency response at the location. If the person being cared for is in a medical facility, the action unit can also perform emergency response at the medical facility. In this way, the optimal action can be selected by taking geographical location information into account. Some or all of the above processing in the action unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the action unit can input geographical location data into a generative AI and have the generative AI select the optimal action.

[0122] The behavioral unit can analyze the social media activity of the person being cared for during emergencies and propose actions. For example, if the person being cared for posts on social media indicating they are feeling stressed, the behavioral unit can propose a calm response. If the person being cared for posts on social media indicating they are relaxed, the behavioral unit can also propose a normal response. If the person being cared for posts on social media indicating they are feeling anxious, the behavioral unit can also propose a reassuring response. In this way, more appropriate actions are proposed by analyzing social media activity. Some or all of the above processing in the behavioral unit may be performed using generative AI, or it may be performed without generative AI. For example, the behavioral unit can input social media posting data into generative AI and have the generative AI execute the action proposals.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The agent system can monitor not only the health of the person being cared for, but also the health of the caregiver. For example, it can monitor the caregiver's vital signs such as body temperature, heart rate, and blood pressure, and can quickly notify and suggest countermeasures if abnormalities are detected. This helps maintain the caregiver's health and improve the quality of care. It can also monitor the caregiver's stress level and provide relaxation methods and counseling information if stress levels rise. Furthermore, by integrating the caregiver's health data with the health data of the person being cared for and performing a comprehensive analysis, it can provide more accurate health management and proactive support.

[0125] The monitoring unit can collect not only health data of the person being cared for, but also environmental data. For example, it can collect environmental data such as room temperature, humidity, and illuminance to identify factors that affect the person being cared for. This allows for the proposal of appropriate measures in response to changes in the environment. Furthermore, by integrating environmental data with the person being cared for's health data and conducting a comprehensive analysis, it is possible to provide more accurate health management and proactive support. In addition, based on the environmental data, it is possible to make suggestions for maintaining a comfortable living environment for the person being cared for.

[0126] The detection unit can analyze not only the physical condition data of the person being cared for, but also their behavioral data. For example, it can analyze the walking patterns and activity levels of the person being cared for, and if an abnormality is detected, it can quickly notify the caregiver and suggest countermeasures. This can reduce the risk of falls for the person being cared for and support a safer life. Furthermore, by integrating behavioral data with the physical condition data of the person being cared for and performing a comprehensive analysis, it can provide more accurate health management and proactive support. In addition, it can also suggest improvements to the lifestyle habits of the person being cared for based on the behavioral data.

[0127] The proposal department can make dietary and exercise suggestions based on the health data of the person receiving care. For example, it can suggest nutritionally balanced meal menus tailored to the person's health condition, thereby supporting the maintenance of their health. It can also suggest appropriate exercise programs tailored to the person's health condition, thereby supporting the improvement of their physical fitness and rehabilitation. Furthermore, by providing dietary and exercise suggestions to caregivers, it can also support the maintenance of their health.

[0128] The service provider can suggest relaxation methods based on the health data of the person being cared for. For example, if the stress level of the person being cared for increases, relaxation music or meditation methods can be suggested. This can support the mental health of the person being cared for. Furthermore, massage or aromatherapy methods tailored to the person being cared for can also be suggested. This promotes relaxation and helps maintain the physical and mental health of the person being cared for. In addition, by providing relaxation method suggestions to caregivers, it can also support the reduction of caregiver stress.

[0129] The alert unit can suggest specific actions to caregivers when an abnormality is detected, based on the health data of the person being cared for. For example, if the heart rate suddenly increases, it can suggest specific measures to stabilize the heart rate to the caregiver. This allows caregivers to respond quickly and appropriately. It can also suggest to caregivers how to contact emergency contacts when an abnormality is detected. This supports quick and appropriate responses in emergencies. Furthermore, it can suggest to caregivers how to use necessary medical equipment when an abnormality is detected.

[0130] The behavioral unit can automate emergency actions based on the health data of the person being cared for. For example, if the heart rate suddenly increases, it can automatically call an ambulance. This enables a rapid response and ensures the safety of the person being cared for. It can also automatically send notifications to caregivers in emergencies to prompt appropriate action. This allows caregivers to respond quickly and appropriately. Furthermore, in emergencies, it can automatically contact medical institutions and arrange necessary medical support. This ensures the safety of the person being cared for.

[0131] The monitoring unit can estimate the emotions of the person being cared for and adjust the monitoring frequency based on the estimated emotions. For example, if the person being cared for is feeling stressed, the monitoring frequency can be increased to quickly detect changes in their physical condition. This can reduce the stress on the person being cared for and maintain their physical and mental health. Conversely, if the person being cared for is relaxed, the monitoring frequency can be reduced to lessen the burden. This can promote relaxation and maintain the physical and mental health of the person being cared for. Furthermore, if the person being cared for is feeling anxious, the monitoring frequency can be appropriately adjusted to provide a sense of security.

[0132] The detection unit can estimate the emotions of the person being cared for and adjust the abnormality detection criteria based on the estimated emotions. For example, if the person being cared for is stressed, the abnormality detection criteria for heart rate and blood pressure can be set more strictly. This allows for rapid detection of changes in physical condition due to stress and the suggestion of appropriate countermeasures. Conversely, if the person being cared for is relaxed, the abnormality detection criteria for body temperature can be set more loosely. This allows for appropriate management of changes in physical condition during relaxation. Furthermore, if the person being cared for is anxious, the abnormality detection criteria for heart rate and respiratory rate can be set more strictly. This allows for rapid detection of changes in physical condition due to anxiety and the suggestion of appropriate countermeasures.

[0133] The proposal function can estimate the emotions of the person receiving care and adjust the way the proposal is expressed based on those estimated emotions. For example, if the person receiving care is feeling stressed, it can propose a simple and easy-to-understand expression. This makes it easier for the person receiving care to understand the proposal and implement appropriate measures. If the person receiving care is relaxed, it can propose an expression that includes detailed information. This allows the person receiving care to understand the proposal more deeply and implement more effective measures. Furthermore, if the person receiving care is feeling anxious, it can propose an expression that provides a sense of security. This reduces the person receiving care's anxiety and allows them to implement appropriate measures.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The monitoring unit monitors the physical condition of the person receiving care. For example, it monitors vital signs such as body temperature, heart rate, and blood pressure, collects the data in real time using AI generation, and analyzes it. Step 2: The detection unit detects abnormalities based on the health data monitored by the monitoring unit. For example, it detects an abnormality when the heart rate suddenly increases and automatically detects it using a generation AI. Step 3: The proposal unit proposes notifications and countermeasures based on the anomalies detected by the detection unit. For example, when an anomaly is detected, it quickly proposes notifications and countermeasures, and uses generation AI to suggest the optimal countermeasures. Step 4: The provisioning department provides counseling information based on the measures proposed by the proposaling department. For example, it provides counseling information and advice from depression specialists and provides information to support mental health using generated AI. Step 5: The alert unit issues autonomous alerts based on the counseling information provided by the service provider. For example, it issues an autonomous alert when an anomaly is detected and uses a generation AI to issue a rapid alert. Step 6: The Action Unit automatically takes action in emergencies based on alerts issued by the Alert Unit. For example, it may automatically call an ambulance in an emergency and use a generating AI to automatically execute the optimal action.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the monitoring unit, detection unit, proposal unit, provision unit, alert unit, and action unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the physical condition of the person being cared for using the camera 42 and sensors of the smart device 14 and collects data using the control unit 46A. The detection unit analyzes the monitored data using the specific processing unit 290 of the data processing unit 12 and detects abnormalities. The proposal unit proposes notifications and countermeasures for abnormalities using the specific processing unit 290 of the data processing unit 12. The provision unit provides counseling information using the specific processing unit 290 of the data processing unit 12. The alert unit issues an autonomous alert using the control unit 46A of the smart device 14. The action unit automatically takes action in emergencies using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the monitoring unit, detection unit, proposal unit, provision unit, alert unit, and action unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the physical condition of the person being cared for using the camera 42 and sensors of the smart glasses 214 and collects data using the control unit 46A. The detection unit analyzes the monitored data using the specific processing unit 290 of the data processing unit 12 and detects abnormalities. The proposal unit proposes notifications and countermeasures for abnormalities using the specific processing unit 290 of the data processing unit 12. The provision unit provides counseling information using the specific processing unit 290 of the data processing unit 12. The alert unit issues an autonomous alert using the control unit 46A of the smart glasses 214. The action unit automatically takes action in emergencies using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the monitoring unit, detection unit, proposal unit, provision unit, alert unit, and action unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the physical condition of the person being cared for using the camera 42 and sensors of the headset terminal 314 and collects data using the control unit 46A. The detection unit analyzes the monitored data using the specific processing unit 290 of the data processing unit 12 and detects abnormalities. The proposal unit proposes notifications and countermeasures for abnormalities using the specific processing unit 290 of the data processing unit 12. The provision unit provides counseling information using the specific processing unit 290 of the data processing unit 12. The alert unit issues an autonomous alert using the control unit 46A of the headset terminal 314. The action unit automatically takes action in emergencies using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the monitoring unit, detection unit, proposal unit, provision unit, alert unit, and action unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the physical condition of the person being cared for using the camera 42 and sensors of the robot 414 and collects data using the control unit 46A. The detection unit analyzes the monitored data using the specific processing unit 290 of the data processing unit 12 and detects abnormalities. The proposal unit proposes notifications and countermeasures for abnormalities using the specific processing unit 290 of the data processing unit 12. The provision unit provides counseling information using the specific processing unit 290 of the data processing unit 12. The alert unit issues an autonomous alert using the control unit 46A of the robot 414. The action unit automatically takes action in emergencies using the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] (Note 1) The monitoring department monitors the health condition of those receiving care, A detection unit that detects abnormalities based on health data monitored by the aforementioned monitoring unit, A proposal unit that proposes notifications and countermeasures based on the abnormality detected by the aforementioned detection unit, A provisioning unit that provides counseling information based on the measures proposed by the aforementioned proposal unit, An alert unit that issues autonomous alerts based on counseling information provided by the aforementioned provision unit, The system includes an action unit that automatically takes action in an emergency based on an alert issued by the alert unit. A system characterized by the following features. (Note 2) The monitoring unit, Monitor vital signs such as body temperature, heart rate, and blood pressure. The system described in Appendix 1, characterized by the features described herein. (Note 3) The detection unit, It detects abnormalities when the heart rate increases rapidly. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, When an anomaly is detected, we will quickly notify you and propose countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide counseling information and advice from depression specialists. The system described in Appendix 1, characterized by the features described herein. (Note 6) The alert unit is, An autonomous alert is issued when an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned action unit is In emergencies, it automatically takes action such as calling an ambulance. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, The system estimates the emotions of the person being cared for and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, Analyze the past health data of the person receiving care and select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, During monitoring, data is filtered based on the lifestyle and environment of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, The system estimates the emotions of the person being cared for and prioritizes monitoring vital signs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The monitoring unit, During monitoring, the system prioritizes acquiring highly relevant data by considering the geographical location of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 13) The monitoring unit, During monitoring, we analyze the social media activity of the person receiving care and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit, The system estimates the emotions of the person receiving care and adjusts the criteria for detecting abnormalities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit, When detecting anomalies, the accuracy of anomaly detection is improved by considering the relationships between the people being cared for. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit, When detection occurs, the system takes into account the attribute information of the person receiving care to detect abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit, The system estimates the emotions of the person being cared for and adjusts the order in which abnormality detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit, When detecting an anomaly, the system takes into account the geographical distribution of the people receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit, When detecting anomalies, we improve the accuracy of anomaly detection by referring to relevant literature on the person being cared for. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, The system estimates the emotions of the person receiving care and adjusts the way the proposal is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the anomaly category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, The system estimates the emotions of the person being cared for and adjusts the length of the suggestion based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the emotions of the person receiving care and adjusts the method of providing counseling information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing counseling information, we refer to the care recipient's past counseling history to provide the most appropriate information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing counseling information, customize the information based on the current mental state of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the emotions of the person receiving care and prioritizes counseling information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing counseling information, we will consider the geographical location of the person receiving care to provide the most appropriate information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing counseling information, we analyze the social media activity of the person receiving care and provide that information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The alert unit is, The system estimates the emotions of the person receiving care and adjusts how alerts are issued based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The alert unit is, When an alert is issued, the system refers to the care recipient's past alert history to select the most appropriate notification method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The alert unit is, When an alert is issued, the content of the alert is customized based on the current situation of the person being cared for. The system described in Appendix 1, characterized by the features described herein. (Note 35) The alert unit is, The system estimates the emotions of the person being cared for and prioritizes alerts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The alert unit is, When an alert is issued, the system considers the geographical location of the person receiving care to send the most appropriate alert. The system described in Appendix 1, characterized by the features described herein. (Note 37) The alert unit is, When an alert is issued, the system analyzes the social media activity of the person receiving care and then sends the alert. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned action unit is The system estimates the emotions of the person being cared for and adjusts emergency actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned action unit is In emergency situations, the system selects the most appropriate course of action by referring to the care recipient's past emergency response history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned action unit is In emergency situations, customize actions based on the current situation of the person being cared for. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned action unit is The system estimates the emotions of the person receiving care and prioritizes emergency actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned action unit is In emergency situations, the optimal course of action is selected by considering the geographical location of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned action unit is In emergency situations, we analyze the social media activity of the person receiving care and propose appropriate actions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The monitoring department monitors the health condition of those receiving care, A detection unit that detects abnormalities based on health data monitored by the aforementioned monitoring unit, A proposal unit that proposes notifications and countermeasures based on the abnormality detected by the aforementioned detection unit, A provisioning unit that provides counseling information based on the measures proposed by the aforementioned proposal unit, An alert unit that issues autonomous alerts based on counseling information provided by the aforementioned provision unit, The system includes an action unit that automatically takes action in an emergency based on an alert issued by the alert unit. A system characterized by the following features.

2. The monitoring unit, Monitor vital signs such as body temperature, heart rate, and blood pressure. The system according to feature 1.

3. The detection unit is It detects abnormalities when the heart rate increases rapidly. The system according to feature 1.

4. The aforementioned proposal section is, When an anomaly is detected, we will quickly notify you and propose countermeasures. The system according to feature 1.

5. The aforementioned supply unit is, We provide counseling information and advice from depression specialists. The system according to feature 1.

6. The alert unit is, An autonomous alert is issued when an anomaly is detected. The system according to feature 1.

7. The aforementioned action unit is In emergencies, it automatically takes action such as calling an ambulance. The system according to feature 1.

8. The monitoring unit, The system estimates the emotions of the person being cared for and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

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