system

Abstract

The system according to this embodiment aims to comprehensively support elderly people in areas such as preventing wandering, managing excretion, responding to emergencies, and managing caregiver stress. [Solution] The system according to the embodiment comprises a prevention unit, a confirmation unit, a notification unit, a response unit, a management unit, and an assistant unit. The prevention unit provides a wandering prevention function. The confirmation unit performs safety checks based on the information provided by the prevention unit. The notification unit provides an excretion management notification function. The response unit performs autonomous emergency response based on the information provided by the notification unit. The management unit manages and cares for the caregiver's stress. The assistant unit provides real-time communication assistant functions.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 are many problems such as the risk of wandering of the elderly, the burden of fecal and urine management, the delay in emergency response, and the lack of mental health care for caregivers, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively support the prevention of wandering of the elderly, fecal and urine management, emergency response, stress management of caregivers, etc.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a prevention unit, a confirmation unit, a notification unit, a response unit, a management unit, and an assistant unit. The prevention unit provides a wandering prevention function. The confirmation unit performs safety checks based on the information provided by the prevention unit. The notification unit provides an excretion management notification function. The response unit performs autonomous emergency response based on the information provided by the notification unit. The management unit manages and provides care for the caregiver's stress. The assistant unit provides real-time communication assistant functions. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively support elderly people in areas such as preventing wandering, managing excretion, responding to emergencies, and managing caregiver stress. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent-based integrated care network assistant according to an embodiment of the present invention is a system designed to solve the challenges of elderly care in an aging society. This system incorporates functions for collaboration with medical, psychological, and care professionals, and integrates functions for wandering prevention and safety checks, excretion management notifications, an autonomous emergency response system, caregiver stress management and care, and a real-time communication assistant. This enables improvements in the quality of care through reliable advice from professionals, real-time health monitoring, personalized care plans, mental health care, and early risk detection. For example, the AI ​​agent performs wandering prevention and safety checks. When an elderly person leaves their home, the AI ​​agent monitors their movements and notifies family members or care staff if there are any abnormalities. This reduces the risk of wandering and ensures the safety of the elderly person. Next, it provides an excretion management notification function. The AI ​​agent monitors the excretion status of the elderly person and notifies family members or care staff as needed. This reduces the burden of excretion management and enables the provision of appropriate care. Furthermore, it includes an autonomous emergency response system. The AI ​​agent monitors the health status of the elderly person in real time and automatically takes emergency action if an emergency occurs. For example, if an elderly person falls, the AI ​​agent immediately notifies emergency contacts and instructs them on necessary actions. This prevents delays in emergency response and ensures the safety of the elderly person. It also provides stress management and care for caregivers. The AI ​​agent monitors the caregiver's mental health and, when necessary, provides referrals to specialists or advice on stress management. This reduces caregiver stress and allows for the provision of appropriate care. Furthermore, it provides a real-time communication assistant function. The AI ​​agent supports communication between the elderly person and their family or care staff, enabling real-time information sharing. This prevents isolation of the elderly person and strengthens collaboration with family and care staff.Thus, an AI-powered comprehensive care network assistant can improve the quality of care through reliable advice from experts, real-time health monitoring, personalized care plans, mental health care, and early risk detection. This reduces the burden on families and care staff of the elderly and provides an environment where seniors can live safely and securely at home.

[0029] The AI ​​agent-based integrated care network assistant according to this embodiment comprises a prevention unit, a confirmation unit, a notification unit, a response unit, a management unit, and an assistant unit. The prevention unit provides a wandering prevention function. For example, the prevention unit monitors the movements of an elderly person when they leave their home and notifies family members or care staff if there is an abnormality. For example, the prevention unit can track location information and issue an alert if the elderly person leaves their home. The prevention unit can also notify family members or care staff if it detects abnormal behavior. The confirmation unit performs safety checks based on the information provided by the prevention unit. For example, the confirmation unit periodically checks the elderly person's location information to confirm that there is no abnormality. For example, the confirmation unit can notify family members or care staff if an abnormality is detected. The confirmation unit can also learn the elderly person's behavior patterns and detect abnormal behavior early. The notification unit provides an excretion management notification function. For example, the notification unit monitors the excretion status of an elderly person and notifies family members or care staff as needed. The notification unit can, for example, use sensors to detect and notify about bowel movements. It can also predict the timing of bowel movements and provide advance notification. The response unit performs autonomous emergency response based on the information provided by the notification unit. For example, the response unit monitors the health status of elderly individuals in real time and automatically takes emergency action if an emergency occurs. For example, if an elderly person falls, the response unit can immediately notify emergency contacts and instruct them on necessary actions. It can also contact medical institutions for a swift response in the event of an emergency. The management unit manages and cares for caregivers' stress. For example, the management unit monitors the mental health status of caregivers and provides referrals to specialists or stress management advice as needed. For example, the management unit can measure the stress level of caregivers and provide appropriate care. It can also regularly check the mental health status of caregivers and provide counseling as needed. The assistant unit provides real-time communication assistant functionality.The assistant unit supports communication between the elderly, their families, and care staff, for example, and enables real-time information sharing. The assistant unit can provide video call and chat functions, for example, to facilitate communication between the elderly, their families, and care staff. The assistant unit can also save communication history, for example, and refer to it as needed. As a result, the AI ​​agent-based integrated care network assistant according to this embodiment can improve the quality of care by providing wandering prevention, safety checks, excretion management, autonomous emergency response, stress management, and real-time communication in an integrated manner.

[0030] The prevention unit provides a wandering prevention function. For example, it monitors the movements of elderly people when they leave their homes and notifies family members or caregivers if any abnormalities are detected. Specifically, the prevention unit uses wearable devices such as GPS devices and smartwatches to track the elderly person's location in real time. These devices can immediately issue an alert if the elderly person leaves their home. The prevention unit also uses AI to analyze behavioral patterns to detect abnormal behavior. For example, it can notify family members or caregivers if abnormal behavior is detected, such as when an elderly person moves beyond their usual range of movement or goes out at night. Furthermore, the prevention unit can not only detect abnormal behavior but also take preventative measures. For example, before an elderly person leaves their home, they can confirm their intention to go out through a voice assistant and contact family members if necessary. In this way, the prevention unit can ensure the safety of the elderly person and minimize the risks associated with wandering.

[0031] The verification unit performs safety checks based on information provided by the prevention unit. For example, the verification unit periodically checks the location information of elderly individuals to confirm that there are no abnormalities. Specifically, the verification unit uses AI to learn the behavioral patterns of elderly individuals and understand their normal range of activity and time of day. This allows for the early detection of abnormal behavior. For example, if an elderly person moves beyond their normal range of activity or goes out at night, the verification unit can notify family members or care staff. The verification unit also monitors the location information of elderly individuals in real time and can respond immediately if an abnormality is detected. For example, if an elderly person falls or remains motionless for a long period of time, the verification unit can notify family members or care staff to encourage a quick response. Furthermore, the verification unit can analyze the behavioral patterns of elderly individuals based on past data and predict abnormal behavior. This allows the verification unit to ensure the safety of elderly individuals and minimize the risks associated with abnormal behavior.

[0032] The notification unit provides a notification function for excretion management. For example, the notification unit monitors the excretion status of elderly individuals and notifies family members and care staff as needed. Specifically, the notification unit uses sensors to detect the excretion status of elderly individuals. For example, sensors installed on beds and chairs detect excretion and collect data in real time. This allows for accurate understanding of the elderly individual's excretion status and timely notifications. The notification unit can also predict the timing of excretion using AI. For example, by analyzing the elderly individual's past excretion data and predicting the timing of the next excretion, it can notify family members and care staff in advance. This allows the notification unit to efficiently manage the excretion of elderly individuals and reduce the burden of care. Furthermore, the notification unit can accumulate data on excretion status and use it for long-term health management. For example, it can monitor changes in the frequency and amount of excretion and detect changes in health status early. This allows the notification unit to support the health management of elderly individuals and enable early intervention.

[0033] The response unit performs autonomous emergency response based on information provided by the notification unit. For example, the response unit monitors the health status of elderly individuals in real time and automatically takes emergency action when an emergency occurs. Specifically, the response unit uses sensors to measure vital signs such as heart rate, blood pressure, and body temperature to monitor the health status of elderly individuals. This data is collected in real time and analyzed by AI. For example, if an elderly person falls or if an abnormality is detected in their heart rate or blood pressure, the response unit can immediately notify emergency contacts and instruct them on the necessary actions. The response unit can also contact medical institutions to ensure a rapid response in the event of an emergency. For example, if an elderly person falls, it can arrange for an ambulance and contact medical institutions to report the situation. This allows the response unit to monitor the health status of elderly individuals in real time and respond quickly to emergencies. Furthermore, the response unit saves a history of emergency responses for later reference. This allows for the use of past emergency response data to improve future responses and develop preventive measures.

[0034] The management department is responsible for managing and caring for caregivers' stress. For example, the management department monitors the mental health status of caregivers and provides referrals to specialists and stress management advice as needed. Specifically, the management department uses questionnaires and sensors to measure caregivers' stress levels. For example, it regularly assesses caregivers' stress levels through questionnaires and measures physiological indicators such as heart rate and skin electrical activity using sensors. This data is analyzed by AI to assess the caregiver's stress level. The management department can also regularly check the mental health status of caregivers and provide counseling as needed. For example, if a high stress level is determined, it can provide referrals to specialists and stress management advice. In this way, the management department can support the mental health of caregivers and improve the quality of care. Furthermore, the management department can save a history of caregivers' stress management and use it to develop long-term care plans. This allows for continuous stress management of caregivers and reduces the burden of caregiving.

[0035] The Assistant Unit provides real-time communication assistance. For example, it supports communication between elderly individuals, their families, and care staff, enabling real-time information sharing. Specifically, the Assistant Unit provides video call and chat functions, facilitating smooth communication between elderly individuals, their families, and care staff. For instance, elderly individuals can reduce feelings of loneliness and receive emotional support by talking with family members via video calls. The Assistant Unit also saves communication history and allows for reference as needed. This enables the review of past communication content and the rapid provision of necessary information. Furthermore, the Assistant Unit can use AI to analyze communication content and understand the needs and condition of elderly individuals. For example, if an elderly individual frequently discusses a particular topic, providing information related to that topic can enrich communication. In this way, the Assistant Unit supports communication between elderly individuals, their families, and care staff, improving the quality of care.

[0036] The prevention unit can monitor the movements of elderly individuals when they leave their homes and notify family members or caregivers if any abnormalities are detected. For example, the prevention unit can track the location information of elderly individuals when they leave their homes and issue an alert if an abnormality is detected. The prevention unit can also notify family members or caregivers if elderly individuals deviate from their normal behavioral patterns. The prevention unit can also issue an alert if elderly individuals approach dangerous areas. This reduces the risk of elderly individuals wandering and ensures their safety. Some or all of the above-described processes in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the elderly individual's location information into the AI ​​and have the AI ​​perform abnormal behavior detection.

[0037] The notification unit can monitor the excretion status of elderly individuals and notify family members or care staff as needed. For example, the notification unit can detect the excretion status of elderly individuals using sensors and send notifications. The notification unit can also predict the timing of excretion and send notifications in advance. The notification unit can also periodically check the excretion status of elderly individuals and send notifications if abnormalities are detected. This reduces the burden of excretion management and allows for the provision of appropriate care. Some or all of the above processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can detect the excretion status of elderly individuals using sensors, input that data into AI, and have AI execute the notification timing.

[0038] The response unit can monitor the health status of elderly individuals in real time and automatically take emergency action in the event of an emergency. For example, the response unit can monitor the vital signs of elderly individuals and take emergency action if an abnormality is detected. For example, if an elderly person falls, the response unit can immediately notify emergency contacts and instruct them on the necessary actions. For example, in the event of an emergency, the response unit can contact medical institutions to ensure a rapid response. This prevents delays in emergency response and ensures the safety of elderly individuals. Some or all of the above-described processes in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the vital sign data of elderly individuals into AI and have the AI ​​detect abnormalities and instruct emergency response.

[0039] The management department can monitor the mental health status of caregivers and, if necessary, provide referrals to specialists or advice on stress management. For example, the management department can measure the stress levels of caregivers and provide appropriate care. The management department can also, for example, regularly check the mental health status of caregivers and provide counseling as needed. The management department can also, for example, analyze the stress levels of caregivers using AI and provide appropriate advice. This can reduce caregiver stress and provide appropriate care. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input caregiver stress level data into AI and have the AI ​​provide stress management advice.

[0040] The assistant unit can support communication between the elderly, their families, and care staff, and enable real-time information sharing. For example, the assistant unit can provide video call and chat functions to facilitate communication between the elderly, their families, and care staff. The assistant unit can also save communication history and refer to it as needed. For example, the assistant unit can support real-time information sharing, enabling rapid sharing of the elderly person's situation. This helps prevent isolation among the elderly and strengthens collaboration with families and care staff. Some or all of the above-described processes in the assistant unit may be performed using AI, or not. For example, the assistant unit can input video call and chat data into AI, allowing the AI ​​to perform communication support.

[0041] The prevention unit can analyze the elderly person's past behavioral patterns and identify times and places where wandering is likely. For example, the prevention unit can analyze the times when the elderly person has wandered in the past and pay particular attention to those times. For example, the prevention unit can identify places where the elderly person has wandered in the past and issue an alert when the elderly person approaches those places. For example, the prevention unit can learn the elderly person's behavioral patterns and take early action when signs of wandering are observed. This allows for the identification of wandering risk based on the elderly person's past behavioral patterns and early intervention. Some or all of the above processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the elderly person's past behavioral data into AI and have the AI ​​perform the identification of wandering risk.

[0042] The prevention unit can optimize wandering prevention alerts based on the health status and lifestyle of elderly individuals. For example, the prevention unit can monitor the health status of elderly individuals and strengthen wandering prevention alerts if they are unwell. The prevention unit can also learn the lifestyle of elderly individuals and issue alerts if they exhibit behavior that deviates from their normal rhythm. The prevention unit can also analyze the sleep patterns of elderly individuals and pay particular attention if there is a high risk of nighttime wandering. This allows the wandering prevention alerts to be optimized based on the health status and lifestyle of elderly individuals. Some or all of the above processes in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input elderly individuals' health status data into AI and have the AI ​​optimize wandering prevention alerts.

[0043] The prevention unit can take region-specific measures to prevent wandering by considering the geographical location information of the elderly person. For example, if the elderly person approaches a specific area, the prevention unit can take measures appropriate to the characteristics of that area. For example, the prevention unit can also notify family members or care staff if the elderly person has moved far from home. For example, the prevention unit can issue an alert if the elderly person approaches a dangerous area. This allows for region-specific wandering prevention measures to be taken based on the geographical location information of the elderly person. Some or all of the above processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the geographical location information of the elderly person into the AI ​​and have the AI ​​execute region-specific measures.

[0044] The prevention unit can analyze the social media activity of elderly people and detect early signs of an increased risk of wandering. For example, the prevention unit may determine that the risk of wandering is increased if an elderly person expresses anxiety on social media. The prevention unit may also determine that the risk of wandering is increased if an elderly person expresses loneliness on social media. The prevention unit may also determine that the risk of wandering is increased if an elderly person expresses excitement on social media. This allows for early detection of the risk of wandering based on the elderly person's social media activity. Some or all of the above processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the elderly person's social media data into AI and have the AI ​​perform the detection of the risk of wandering.

[0045] The verification unit can analyze the elderly person's past safety check history and identify the optimal timing for verification. For example, the verification unit can analyze the time periods when the elderly person has performed safety checks in the past and pay particular attention to those times. For example, the verification unit can identify locations where the elderly person has performed safety checks in the past and issue an alert when approaching those locations. For example, the verification unit can learn the elderly person's safety check history and identify the optimal timing for verification. This allows the optimal timing for verification to be identified based on the elderly person's past safety check history. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the elderly person's past safety check data into AI and have the AI ​​perform the task of identifying the optimal timing for verification.

[0046] The verification unit can customize its safety verification approach based on the health status and living environment of the elderly person. For example, the verification unit can monitor the health status of the elderly person and increase the frequency of safety checks if they are unwell. The verification unit can also consider the living environment of the elderly person and change the safety verification method under specific circumstances. The verification unit can also learn the elderly person's daily rhythm and perform safety checks if they observe behavior that deviates from their normal rhythm. This allows the safety verification approach to be customized based on the health status and living environment of the elderly person. Some or all of the above processes in the verification unit may be performed using AI, for example, or not. For example, the verification unit can input the elderly person's health status data into the AI ​​and have the AI ​​perform the customization of the safety verification approach.

[0047] The verification unit can take region-specific safety measures, taking into account the geographical location information of the elderly person. For example, if the elderly person approaches a specific area, the verification unit can take measures appropriate to the characteristics of that area. For example, the verification unit can also notify family members or care staff if the elderly person is far from home. For example, the verification unit can issue an alert if the elderly person approaches a dangerous area. This allows for region-specific safety measures to be taken based on the geographical location information of the elderly person. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the geographical location information of the elderly person into AI and have AI execute region-specific measures.

[0048] The verification unit can analyze the social media activity of elderly individuals and detect the need for safety checks early. For example, the verification unit may determine that the need for safety checks increases if an elderly individual expresses anxiety on social media. The verification unit may also determine that the need for safety checks increases if an elderly individual expresses loneliness on social media. The verification unit may also determine that the need for safety checks increases if an elderly individual expresses excitement on social media. This allows for the early detection of the need for safety checks based on the social media activity of elderly individuals. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the elderly individual's social media data into AI and have the AI ​​perform the detection of the need for safety checks.

[0049] The notification unit can analyze the elderly person's past bowel movement patterns and identify the optimal notification timing. For example, the notification unit can analyze the time periods when the elderly person has previously bowel movements and pay particular attention to those times. For example, the notification unit can identify the locations where the elderly person has previously bowel movements and issue an alert when approaching those locations. For example, the notification unit can learn the elderly person's bowel movement patterns and identify the optimal notification timing. This allows the notification unit to identify the optimal notification timing based on the elderly person's past bowel movement patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the elderly person's past bowel movement data into AI and have the AI ​​determine the optimal notification timing.

[0050] The notification unit can optimize excretion management alerts based on the health status and lifestyle of the elderly. For example, the notification unit can monitor the health status of the elderly and strengthen excretion management alerts if they are unwell. For example, the notification unit can learn the lifestyle of the elderly and issue an alert if they observe behavior that deviates from their normal rhythm. For example, the notification unit can analyze the sleep patterns of the elderly and pay particular attention if there is a high risk of nighttime excretion. This allows for the optimization of excretion management alerts based on the health status and lifestyle of the elderly. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the health status data of the elderly into AI and have the AI ​​perform the optimization of excretion management alerts.

[0051] The notification unit can take region-specific measures for excretion management, taking into account the geographical location of the elderly person. For example, if the elderly person approaches a specific area, the notification unit can take measures appropriate to the characteristics of that area. For example, the notification unit can also notify family members or care staff if the elderly person is far from home. For example, the notification unit can issue an alert if the elderly person approaches a dangerous area. This allows for region-specific excretion management measures to be taken based on the geographical location of the elderly person. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the geographical location of the elderly person into the AI ​​and have the AI ​​execute region-specific measures.

[0052] The notification unit can analyze the social media activity of elderly individuals and detect the need for excretion management early. For example, the notification unit may determine that the need for excretion management is increasing if an elderly individual expresses anxiety on social media. The notification unit may also determine that the need for excretion management is increasing if an elderly individual expresses loneliness on social media. The notification unit may also determine that the need for excretion management is increasing if an elderly individual expresses excitement on social media. This allows for the early detection of the need for excretion management based on the social media activity of elderly individuals. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the elderly individual's social media data into AI and have the AI ​​perform the detection of the need for excretion management.

[0053] The response unit can analyze the past health status of elderly individuals and predict the risk of an emergency occurring. For example, the response unit can analyze the past health data of elderly individuals and issue an alert if the risk of an emergency is high. For example, the response unit can also consider the past medical history of elderly individuals and take emergency action if specific symptoms are observed. For example, the response unit can monitor the health status of elderly individuals and take early action if abnormalities are detected. This makes it possible to predict the risk of an emergency occurring based on the past health status of elderly individuals. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the past health data of elderly individuals into AI and have the AI ​​perform the prediction of the risk of an emergency occurring.

[0054] The response unit can customize its emergency response approach based on the elderly person's living environment and health condition. For example, the response unit can consider the elderly person's living environment and change the emergency response method under specific circumstances. For example, the response unit can monitor the elderly person's health condition and increase the frequency of emergency responses if they are unwell. For example, the response unit can learn the elderly person's daily rhythm and take emergency action if they observe behavior that deviates from their normal rhythm. This allows the emergency response approach to be customized based on the elderly person's living environment and health condition. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input data on the elderly person's living environment into AI and have the AI ​​perform the customization of the emergency response approach.

[0055] The response unit can take region-specific measures for emergency response, taking into account the geographical location information of the elderly person. For example, if the elderly person approaches a specific area, the response unit can take measures appropriate to the characteristics of that area. For example, if the elderly person is far from home, the response unit can also notify family members or care staff. For example, if the elderly person approaches a dangerous area, the response unit can issue an alert. This allows for region-specific emergency response measures to be taken based on the geographical location information of the elderly person. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the geographical location information of the elderly person into AI and have AI execute region-specific measures.

[0056] The response unit can analyze the social media activity of elderly individuals and detect the need for emergency response early. For example, the response unit may determine that the need for emergency response increases if an elderly individual expresses anxiety on social media. The response unit may also determine that the need for emergency response increases if an elderly individual expresses loneliness on social media. The response unit may also determine that the need for emergency response increases if an elderly individual expresses excitement on social media. This allows for the early detection of the need for emergency response based on the social media activity of elderly individuals. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the elderly individual's social media data into AI and have the AI ​​perform the detection of the need for emergency response.

[0057] The management department can analyze the caregiver's past mental health status and identify the optimal care method. For example, the management department can analyze the caregiver's past mental health data and propose the optimal care method. The management department can also consider the caregiver's past stress levels and modify the care method in specific situations. The management department can also monitor the caregiver's mental health status and take early action if any abnormalities are detected. This allows the management department to identify the optimal care method based on the caregiver's past mental health status. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the caregiver's past mental health data into AI and have the AI ​​identify the optimal care method.

[0058] The management department can customize stress management approaches based on the caregiver's living environment and workload. For example, the management department can consider the caregiver's living environment and change stress management methods under specific circumstances. The management department can also monitor the caregiver's workload and increase the frequency of stress management if the workload is heavy. The management department can also learn the caregiver's daily rhythm and implement stress management if behavior deviates from the normal rhythm is observed. This allows for the customization of stress management approaches based on the caregiver's living environment and workload. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input caregiver living environment data into AI and have the AI ​​customize the stress management approach.

[0059] The management department can take into account the caregiver's geographical location and implement region-specific measures for stress management. For example, if the caregiver is in a particular region, the management department can provide stress management methods tailored to the characteristics of that region. For example, if the caregiver is far from home, the management department can also suggest ways to relax. For example, if the caregiver is in a dangerous area, the management department can increase the frequency of stress management. This allows for region-specific stress management measures to be implemented based on the caregiver's geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the caregiver's geographical location into the AI ​​and have the AI ​​implement region-specific measures.

[0060] The management department can analyze caregivers' social media activity and detect the need for stress management early. For example, the management department may determine that the need for stress management is high if a caregiver expresses stress on social media. The management department may also determine that the need for stress management is high if a caregiver appears tired on social media. The management department may also provide normal stress management methods if a caregiver appears relaxed on social media. This allows for the early detection of the need for stress management based on the caregiver's social media activity. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input caregiver social media data into AI and have the AI ​​perform the detection of the need for stress management.

[0061] The assistant unit can analyze the elderly person's past communication history and identify the optimal method of information sharing. For example, the assistant unit can analyze the elderly person's past communication history and propose the optimal method of information sharing. The assistant unit can also consider the elderly person's past communication patterns and modify the method of information sharing in specific situations. The assistant unit can also monitor the elderly person's communication history and respond early if any abnormalities are detected. This allows the assistant unit to identify the optimal method of information sharing based on the elderly person's past communication history. Some or all of the above processes in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input the elderly person's past communication data into AI and have the AI ​​identify the optimal method of information sharing.

[0062] The assistant unit can customize its communication approach based on the elderly person's living environment and health condition. For example, the assistant unit can consider the elderly person's living environment and change its communication method under specific circumstances. For example, the assistant unit can monitor the elderly person's health condition and increase the frequency of communication if they are unwell. For example, the assistant unit can learn the elderly person's daily rhythm and communicate if they observe behavior that deviates from their usual rhythm. This allows the communication approach to be customized based on the elderly person's living environment and health condition. Some or all of the above processes in the assistant unit may be performed using AI, for example, or not using AI. For example, the assistant unit can input data on the elderly person's living environment into AI and have the AI ​​perform the customization of the communication approach.

[0063] The assistant unit can take region-specific measures for communication, taking into account the geographical location of the elderly person. For example, if the elderly person is in a specific area, the assistant unit can provide communication methods tailored to the characteristics of that area. For example, the assistant unit can also notify family members or caregivers if the elderly person is far from home. For example, if the elderly person is in a dangerous area, the assistant unit can increase the frequency of communication. This allows for region-specific communication measures to be taken based on the geographical location of the elderly person. Some or all of the above processing in the assistant unit may be performed using AI, for example, or not using AI. For example, the assistant unit can input the geographical location of the elderly person into the AI ​​and have the AI ​​implement region-specific measures.

[0064] The assistant unit can analyze the social media activity of elderly individuals and detect their need for communication early. For example, the assistant unit may determine that the need for communication is high if an elderly individual expresses anxiety on social media. The assistant unit may also determine that the need for communication is high if an elderly individual feels lonely on social media. The assistant unit may also determine that the need for communication is high if an elderly individual is excited on social media. This allows for the early detection of the need for communication based on the elderly individual's social media activity. Some or all of the above processing in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input the elderly individual's social media data into AI and have the AI ​​perform the detection of the need for communication.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The prevention unit can analyze the past behavioral patterns of elderly individuals and identify times and locations where wandering is likely. For example, it can analyze the times when elderly individuals have wandered in the past and pay particular attention to those times. It can also identify locations where elderly individuals have wandered in the past and issue an alert when they approach those locations. Furthermore, it can learn the behavioral patterns of elderly individuals and respond early if signs of wandering are observed. This allows for the identification of wandering risks based on the elderly individual's past behavioral patterns and enables early intervention.

[0067] The prevention unit can optimize wandering prevention alerts based on the health status and lifestyle of elderly individuals. For example, it can monitor the health status of elderly individuals and strengthen wandering prevention alerts if they are unwell. It can also learn the lifestyle of elderly individuals and issue alerts if behavior deviates from their normal rhythm. Furthermore, it can analyze the sleep patterns of elderly individuals and pay particular attention to those at high risk of nighttime wandering. This allows for the optimization of wandering prevention alerts based on the health status and lifestyle of elderly individuals.

[0068] The prevention unit can take into account the geographical location information of elderly people and implement region-specific measures to prevent wandering. For example, if an elderly person approaches a specific area, measures can be taken according to the characteristics of that area. It can also notify family members or care staff if an elderly person has moved far from home. Furthermore, it can issue an alert if an elderly person approaches a dangerous area. This allows for the implementation of region-specific wandering prevention measures based on the geographical location information of elderly people.

[0069] The verification unit can analyze the elderly person's past safety check history and identify the optimal timing for checks. For example, it can analyze the time periods when the elderly person has performed safety checks in the past and pay particular attention to those times. It can also identify locations where the elderly person has performed safety checks in the past and issue an alert when approaching those locations. Furthermore, it can learn from the elderly person's safety check history and identify the optimal timing for checks. This allows the system to determine the optimal timing for checks based on the elderly person's past safety check history.

[0070] The verification unit can customize its safety verification approach based on the health status and living environment of the elderly person. For example, it can monitor the elderly person's health and increase the frequency of safety checks if they are unwell. It can also consider the elderly person's living environment and modify the safety verification method under specific circumstances. Furthermore, it can learn the elderly person's daily rhythm and perform safety checks if behavior deviates from the normal rhythm is observed. In this way, the safety verification approach can be customized based on the health status and living environment of the elderly person.

[0071] The monitoring unit can take region-specific safety measures into account, considering the geographical location of elderly individuals. For example, if an elderly person approaches a specific area, measures tailored to the characteristics of that area can be taken. Furthermore, if an elderly person travels far from home, family members or care staff can be notified. Additionally, an alert can be issued if an elderly person approaches a dangerous area. This allows for region-specific safety measures to be implemented based on the geographical location of elderly individuals.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The prevention unit provides a wandering prevention function. The prevention unit monitors the movements of elderly people when they leave their homes and notifies family members or caregivers if any abnormalities are detected. The prevention unit can track location information and issue an alert if the elderly person leaves their home. It can also notify family members or caregivers if abnormal behavior is detected. Step 2: The verification unit performs safety checks based on the information provided by the prevention unit. The verification unit periodically checks the location information of the elderly person and verifies for any abnormalities. If an abnormality is detected, it can notify family members or care staff. It can also learn the behavioral patterns of the elderly person and detect abnormal behavior at an early stage. Step 3: The notification unit provides an excretion management notification function. The notification unit monitors the excretion status of the elderly person and notifies family members or care staff as needed. It can detect and notify about excretion status using sensors. It can also predict the timing of excretion and notify in advance. Step 4: The response unit performs autonomous emergency response based on the information provided by the notification unit. The response unit monitors the health status of the elderly person in real time and automatically takes emergency action when an emergency occurs. If the elderly person falls, it can immediately notify emergency contacts and instruct them on the necessary actions. It can also contact medical institutions in the event of an emergency to ensure a swift response. Step 5: The management department manages and cares for caregivers' stress. The management department monitors the caregivers' mental health status and provides referrals to specialists and stress management advice as needed. They can measure the caregivers' stress levels and provide appropriate care. They can also regularly check the caregivers' mental health status and provide counseling as needed. Step 6: The Assistant Unit provides real-time communication assistant functionality. The Assistant Unit supports communication between the elderly, their families, and care staff, enabling real-time information sharing. It provides video call and chat functions to facilitate smooth communication between the elderly, their families, and care staff. It also saves the communication history, which can be referenced as needed.

[0074] (Example of form 2) The AI ​​agent-based integrated care network assistant according to an embodiment of the present invention is a system designed to solve the challenges of elderly care in an aging society. This system incorporates functions for collaboration with medical, psychological, and care professionals, and integrates functions for wandering prevention and safety checks, excretion management notifications, an autonomous emergency response system, caregiver stress management and care, and a real-time communication assistant. This enables improvements in the quality of care through reliable advice from professionals, real-time health monitoring, personalized care plans, mental health care, and early risk detection. For example, the AI ​​agent performs wandering prevention and safety checks. When an elderly person leaves their home, the AI ​​agent monitors their movements and notifies family members or care staff if there are any abnormalities. This reduces the risk of wandering and ensures the safety of the elderly person. Next, it provides an excretion management notification function. The AI ​​agent monitors the excretion status of the elderly person and notifies family members or care staff as needed. This reduces the burden of excretion management and enables the provision of appropriate care. Furthermore, it includes an autonomous emergency response system. The AI ​​agent monitors the health status of the elderly person in real time and automatically takes emergency action if an emergency occurs. For example, if an elderly person falls, the AI ​​agent immediately notifies emergency contacts and instructs them on necessary actions. This prevents delays in emergency response and ensures the safety of the elderly person. It also provides stress management and care for caregivers. The AI ​​agent monitors the caregiver's mental health and, when necessary, provides referrals to specialists or advice on stress management. This reduces caregiver stress and allows for the provision of appropriate care. Furthermore, it provides a real-time communication assistant function. The AI ​​agent supports communication between the elderly person and their family or care staff, enabling real-time information sharing. This prevents isolation of the elderly person and strengthens collaboration with family and care staff.Thus, an AI-powered comprehensive care network assistant can improve the quality of care through reliable advice from experts, real-time health monitoring, personalized care plans, mental health care, and early risk detection. This reduces the burden on families and care staff of the elderly and provides an environment where seniors can live safely and securely at home.

[0075] The AI ​​agent-based integrated care network assistant according to this embodiment comprises a prevention unit, a confirmation unit, a notification unit, a response unit, a management unit, and an assistant unit. The prevention unit provides a wandering prevention function. For example, the prevention unit monitors the movements of an elderly person when they leave their home and notifies family members or care staff if there is an abnormality. For example, the prevention unit can track location information and issue an alert if the elderly person leaves their home. The prevention unit can also notify family members or care staff if it detects abnormal behavior. The confirmation unit performs safety checks based on the information provided by the prevention unit. For example, the confirmation unit periodically checks the elderly person's location information to confirm that there is no abnormality. For example, the confirmation unit can notify family members or care staff if an abnormality is detected. The confirmation unit can also learn the elderly person's behavior patterns and detect abnormal behavior early. The notification unit provides an excretion management notification function. For example, the notification unit monitors the excretion status of an elderly person and notifies family members or care staff as needed. The notification unit can, for example, use sensors to detect and notify about bowel movements. It can also predict the timing of bowel movements and provide advance notification. The response unit performs autonomous emergency response based on the information provided by the notification unit. For example, the response unit monitors the health status of elderly individuals in real time and automatically takes emergency action if an emergency occurs. For example, if an elderly person falls, the response unit can immediately notify emergency contacts and instruct them on necessary actions. It can also contact medical institutions for a swift response in the event of an emergency. The management unit manages and cares for caregivers' stress. For example, the management unit monitors the mental health status of caregivers and provides referrals to specialists or stress management advice as needed. For example, the management unit can measure the stress level of caregivers and provide appropriate care. It can also regularly check the mental health status of caregivers and provide counseling as needed. The assistant unit provides real-time communication assistant functionality.The assistant unit supports communication between the elderly, their families, and care staff, for example, and enables real-time information sharing. The assistant unit can provide video call and chat functions, for example, to facilitate communication between the elderly, their families, and care staff. The assistant unit can also save communication history, for example, and refer to it as needed. As a result, the AI ​​agent-based integrated care network assistant according to this embodiment can improve the quality of care by providing wandering prevention, safety checks, excretion management, autonomous emergency response, stress management, and real-time communication in an integrated manner.

[0076] The prevention unit provides a wandering prevention function. For example, it monitors the movements of elderly people when they leave their homes and notifies family members or caregivers if any abnormalities are detected. Specifically, the prevention unit uses wearable devices such as GPS devices and smartwatches to track the elderly person's location in real time. These devices can immediately issue an alert if the elderly person leaves their home. The prevention unit also uses AI to analyze behavioral patterns to detect abnormal behavior. For example, it can notify family members or caregivers if abnormal behavior is detected, such as when an elderly person moves beyond their usual range of movement or goes out at night. Furthermore, the prevention unit can not only detect abnormal behavior but also take preventative measures. For example, before an elderly person leaves their home, they can confirm their intention to go out through a voice assistant and contact family members if necessary. In this way, the prevention unit can ensure the safety of the elderly person and minimize the risks associated with wandering.

[0077] The verification unit performs safety checks based on information provided by the prevention unit. For example, the verification unit periodically checks the location information of elderly individuals to confirm that there are no abnormalities. Specifically, the verification unit uses AI to learn the behavioral patterns of elderly individuals and understand their normal range of activity and time of day. This allows for the early detection of abnormal behavior. For example, if an elderly person moves beyond their normal range of activity or goes out at night, the verification unit can notify family members or care staff. The verification unit also monitors the location information of elderly individuals in real time and can respond immediately if an abnormality is detected. For example, if an elderly person falls or remains motionless for a long period of time, the verification unit can notify family members or care staff to encourage a quick response. Furthermore, the verification unit can analyze the behavioral patterns of elderly individuals based on past data and predict abnormal behavior. This allows the verification unit to ensure the safety of elderly individuals and minimize the risks associated with abnormal behavior.

[0078] The notification unit provides a notification function for excretion management. For example, the notification unit monitors the excretion status of elderly individuals and notifies family members and care staff as needed. Specifically, the notification unit uses sensors to detect the excretion status of elderly individuals. For example, sensors installed on beds and chairs detect excretion and collect data in real time. This allows for accurate understanding of the elderly individual's excretion status and timely notifications. The notification unit can also predict the timing of excretion using AI. For example, by analyzing the elderly individual's past excretion data and predicting the timing of the next excretion, it can notify family members and care staff in advance. This allows the notification unit to efficiently manage the excretion of elderly individuals and reduce the burden of care. Furthermore, the notification unit can accumulate data on excretion status and use it for long-term health management. For example, it can monitor changes in the frequency and amount of excretion and detect changes in health status early. This allows the notification unit to support the health management of elderly individuals and enable early intervention.

[0079] The response unit performs autonomous emergency response based on information provided by the notification unit. For example, the response unit monitors the health status of elderly individuals in real time and automatically takes emergency action when an emergency occurs. Specifically, the response unit uses sensors to measure vital signs such as heart rate, blood pressure, and body temperature to monitor the health status of elderly individuals. This data is collected in real time and analyzed by AI. For example, if an elderly person falls or if an abnormality is detected in their heart rate or blood pressure, the response unit can immediately notify emergency contacts and instruct them on the necessary actions. The response unit can also contact medical institutions to ensure a rapid response in the event of an emergency. For example, if an elderly person falls, it can arrange for an ambulance and contact medical institutions to report the situation. This allows the response unit to monitor the health status of elderly individuals in real time and respond quickly to emergencies. Furthermore, the response unit saves a history of emergency responses for later reference. This allows for the use of past emergency response data to improve future responses and develop preventive measures.

[0080] The management department is responsible for managing and caring for caregivers' stress. For example, the management department monitors the mental health status of caregivers and provides referrals to specialists and stress management advice as needed. Specifically, the management department uses questionnaires and sensors to measure caregivers' stress levels. For example, it regularly assesses caregivers' stress levels through questionnaires and measures physiological indicators such as heart rate and skin electrical activity using sensors. This data is analyzed by AI to assess the caregiver's stress level. The management department can also regularly check the mental health status of caregivers and provide counseling as needed. For example, if a high stress level is determined, it can provide referrals to specialists and stress management advice. In this way, the management department can support the mental health of caregivers and improve the quality of care. Furthermore, the management department can save a history of caregivers' stress management and use it to develop long-term care plans. This allows for continuous stress management of caregivers and reduces the burden of caregiving.

[0081] The Assistant Unit provides real-time communication assistance. For example, it supports communication between elderly individuals, their families, and care staff, enabling real-time information sharing. Specifically, the Assistant Unit provides video call and chat functions, facilitating smooth communication between elderly individuals, their families, and care staff. For instance, elderly individuals can reduce feelings of loneliness and receive emotional support by talking with family members via video calls. The Assistant Unit also saves communication history and allows for reference as needed. This enables the review of past communication content and the rapid provision of necessary information. Furthermore, the Assistant Unit can use AI to analyze communication content and understand the needs and condition of elderly individuals. For example, if an elderly individual frequently discusses a particular topic, providing information related to that topic can enrich communication. In this way, the Assistant Unit supports communication between elderly individuals, their families, and care staff, improving the quality of care.

[0082] The prevention unit can monitor the movements of elderly individuals when they leave their homes and notify family members or caregivers if any abnormalities are detected. For example, the prevention unit can track the location information of elderly individuals when they leave their homes and issue an alert if an abnormality is detected. The prevention unit can also notify family members or caregivers if elderly individuals deviate from their normal behavioral patterns. The prevention unit can also issue an alert if elderly individuals approach dangerous areas. This reduces the risk of elderly individuals wandering and ensures their safety. Some or all of the above-described processes in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the elderly individual's location information into the AI ​​and have the AI ​​perform abnormal behavior detection.

[0083] The notification unit can monitor the excretion status of elderly individuals and notify family members or care staff as needed. For example, the notification unit can detect the excretion status of elderly individuals using sensors and send notifications. The notification unit can also predict the timing of excretion and send notifications in advance. The notification unit can also periodically check the excretion status of elderly individuals and send notifications if abnormalities are detected. This reduces the burden of excretion management and allows for the provision of appropriate care. Some or all of the above processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can detect the excretion status of elderly individuals using sensors, input that data into AI, and have AI execute the notification timing.

[0084] The response unit can monitor the health status of elderly individuals in real time and automatically take emergency action in the event of an emergency. For example, the response unit can monitor the vital signs of elderly individuals and take emergency action if an abnormality is detected. For example, if an elderly person falls, the response unit can immediately notify emergency contacts and instruct them on the necessary actions. For example, in the event of an emergency, the response unit can contact medical institutions to ensure a rapid response. This prevents delays in emergency response and ensures the safety of elderly individuals. Some or all of the above-described processes in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the vital sign data of elderly individuals into AI and have the AI ​​detect abnormalities and instruct emergency response.

[0085] The management department can monitor the mental health status of caregivers and, if necessary, provide referrals to specialists or advice on stress management. For example, the management department can measure the stress levels of caregivers and provide appropriate care. The management department can also, for example, regularly check the mental health status of caregivers and provide counseling as needed. The management department can also, for example, analyze the stress levels of caregivers using AI and provide appropriate advice. This can reduce caregiver stress and provide appropriate care. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input caregiver stress level data into AI and have the AI ​​provide stress management advice.

[0086] The assistant unit can support communication between the elderly, their families, and care staff, and enable real-time information sharing. For example, the assistant unit can provide video call and chat functions to facilitate communication between the elderly, their families, and care staff. The assistant unit can also save communication history and refer to it as needed. For example, the assistant unit can support real-time information sharing, enabling rapid sharing of the elderly person's situation. This helps prevent isolation among the elderly and strengthens collaboration with families and care staff. Some or all of the above-described processes in the assistant unit may be performed using AI, or not. For example, the assistant unit can input video call and chat data into AI, allowing the AI ​​to perform communication support.

[0087] The prevention unit can estimate the emotions of elderly individuals, predict the risk of wandering based on the estimated emotions, and take preventive measures. For example, if an elderly person is feeling anxious, the AI ​​in the prevention unit can detect that emotion and notify family members or caregivers for early intervention. For example, if an elderly person is feeling lonely, the AI ​​in the prevention unit can detect that emotion and suggest actions to facilitate communication. For example, if an elderly person is agitated, the AI ​​in the prevention unit can detect that emotion and provide calming music or videos. This allows for the prediction of the risk of wandering based on the emotions of elderly individuals and the implementation of appropriate preventive measures. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the prevention unit may be performed using AI or not using AI. For example, the prevention unit can input emotional data of elderly people into AI, allowing the AI ​​to predict the risk of wandering and suggest preventive measures.

[0088] The prevention unit can analyze the elderly person's past behavioral patterns and identify times and places where wandering is likely. For example, the prevention unit can analyze the times when the elderly person has wandered in the past and pay particular attention to those times. For example, the prevention unit can identify places where the elderly person has wandered in the past and issue an alert when the elderly person approaches those places. For example, the prevention unit can learn the elderly person's behavioral patterns and take early action when signs of wandering are observed. This allows for the identification of wandering risk based on the elderly person's past behavioral patterns and early intervention. Some or all of the above processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the elderly person's past behavioral data into AI and have the AI ​​perform the identification of wandering risk.

[0089] The prevention unit can optimize wandering prevention alerts based on the health status and lifestyle of elderly individuals. For example, the prevention unit can monitor the health status of elderly individuals and strengthen wandering prevention alerts if they are unwell. The prevention unit can also learn the lifestyle of elderly individuals and issue alerts if they exhibit behavior that deviates from their normal rhythm. The prevention unit can also analyze the sleep patterns of elderly individuals and pay particular attention if there is a high risk of nighttime wandering. This allows the wandering prevention alerts to be optimized based on the health status and lifestyle of elderly individuals. Some or all of the above processes in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input elderly individuals' health status data into AI and have the AI ​​optimize wandering prevention alerts.

[0090] The prevention unit can estimate the emotions of elderly individuals and adjust the wandering prevention notification method based on the estimated emotions. For example, if an elderly person is feeling anxious, the prevention unit can notify them in a gentle voice. For example, if an elderly person is agitated, the prevention unit can play calming music when notifying them. For example, if an elderly person is feeling lonely, the prevention unit can suggest a video call with family members. This allows the wandering prevention notification method to be adjusted based on the emotions of the elderly person. 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 prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the elderly person's emotion data into an AI and have the AI ​​adjust the notification method.

[0091] The prevention unit can take region-specific measures to prevent wandering by considering the geographical location information of the elderly person. For example, if the elderly person approaches a specific area, the prevention unit can take measures appropriate to the characteristics of that area. For example, the prevention unit can also notify family members or care staff if the elderly person has moved far from home. For example, the prevention unit can issue an alert if the elderly person approaches a dangerous area. This allows for region-specific wandering prevention measures to be taken based on the geographical location information of the elderly person. Some or all of the above processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the geographical location information of the elderly person into the AI ​​and have the AI ​​execute region-specific measures.

[0092] The prevention unit can analyze the social media activity of elderly people and detect early signs of an increased risk of wandering. For example, the prevention unit may determine that the risk of wandering is increased if an elderly person expresses anxiety on social media. The prevention unit may also determine that the risk of wandering is increased if an elderly person expresses loneliness on social media. The prevention unit may also determine that the risk of wandering is increased if an elderly person expresses excitement on social media. This allows for early detection of the risk of wandering based on the elderly person's social media activity. Some or all of the above processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the elderly person's social media data into AI and have the AI ​​perform the detection of the risk of wandering.

[0093] The verification unit can estimate the emotions of elderly individuals and adjust the frequency and method of safety checks based on the estimated emotions. For example, if an elderly person is feeling anxious, the verification unit can increase the frequency of safety checks. For example, if an elderly person is relaxed, the verification unit can decrease the frequency of safety checks. For example, if an elderly person is agitated, the verification unit can change the method of safety checks. This allows the frequency and method of safety checks to be adjusted based on the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the emotional data of elderly individuals into an AI and have the AI ​​adjust the frequency and method of safety checks.

[0094] The verification unit can analyze the elderly person's past safety check history and identify the optimal timing for verification. For example, the verification unit can analyze the time periods when the elderly person has performed safety checks in the past and pay particular attention to those times. For example, the verification unit can identify locations where the elderly person has performed safety checks in the past and issue an alert when approaching those locations. For example, the verification unit can learn the elderly person's safety check history and identify the optimal timing for verification. This allows the optimal timing for verification to be identified based on the elderly person's past safety check history. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the elderly person's past safety check data into AI and have the AI ​​perform the task of identifying the optimal timing for verification.

[0095] The verification unit can customize its safety verification approach based on the health status and living environment of the elderly person. For example, the verification unit can monitor the health status of the elderly person and increase the frequency of safety checks if they are unwell. The verification unit can also consider the living environment of the elderly person and change the safety verification method under specific circumstances. The verification unit can also learn the elderly person's daily rhythm and perform safety checks if they observe behavior that deviates from their normal rhythm. This allows the safety verification approach to be customized based on the health status and living environment of the elderly person. Some or all of the above processes in the verification unit may be performed using AI, for example, or not. For example, the verification unit can input the elderly person's health status data into the AI ​​and have the AI ​​perform the customization of the safety verification approach.

[0096] The verification unit can estimate the emotions of elderly individuals and adjust the safety confirmation notification method based on the estimated emotions. For example, if an elderly person is feeling anxious, the verification unit can notify them in a gentle voice. For example, if an elderly person is feeling agitated, the verification unit can play calming music when notifying them. For example, if an elderly person is feeling lonely, the verification unit can suggest a video call with family members. This allows the safety confirmation notification method to be adjusted based on the emotions of the elderly person. 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 verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the elderly person's emotion data into an AI and have the AI ​​adjust the notification method.

[0097] The verification unit can take region-specific safety measures, taking into account the geographical location information of the elderly person. For example, if the elderly person approaches a specific area, the verification unit can take measures appropriate to the characteristics of that area. For example, the verification unit can also notify family members or care staff if the elderly person is far from home. For example, the verification unit can issue an alert if the elderly person approaches a dangerous area. This allows for region-specific safety measures to be taken based on the geographical location information of the elderly person. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the geographical location information of the elderly person into AI and have AI execute region-specific measures.

[0098] The verification unit can analyze the social media activity of elderly individuals and detect the need for safety checks early. For example, the verification unit may determine that the need for safety checks increases if an elderly individual expresses anxiety on social media. The verification unit may also determine that the need for safety checks increases if an elderly individual expresses loneliness on social media. The verification unit may also determine that the need for safety checks increases if an elderly individual expresses excitement on social media. This allows for the early detection of the need for safety checks based on the social media activity of elderly individuals. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the elderly individual's social media data into AI and have the AI ​​perform the detection of the need for safety checks.

[0099] The notification unit can estimate the emotions of elderly individuals and adjust the notification method for incontinence management based on the estimated emotions. For example, if an elderly individual is feeling anxious, the notification unit can notify them in a gentle voice. For example, if an elderly individual is feeling agitated, the notification unit can play calming music when notifying them. For example, if an elderly individual is feeling lonely, the notification unit can suggest a video call with family members. This allows the notification method for incontinence management to be adjusted based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input the elderly individual's emotion data into an AI and have the AI ​​adjust the notification method.

[0100] The notification unit can analyze the elderly person's past bowel movement patterns and identify the optimal notification timing. For example, the notification unit can analyze the time periods when the elderly person has previously bowel movements and pay particular attention to those times. For example, the notification unit can identify the locations where the elderly person has previously bowel movements and issue an alert when approaching those locations. For example, the notification unit can learn the elderly person's bowel movement patterns and identify the optimal notification timing. This allows the notification unit to identify the optimal notification timing based on the elderly person's past bowel movement patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the elderly person's past bowel movement data into AI and have the AI ​​determine the optimal notification timing.

[0101] The notification unit can optimize excretion management alerts based on the health status and lifestyle of the elderly. For example, the notification unit can monitor the health status of the elderly and strengthen excretion management alerts if they are unwell. For example, the notification unit can learn the lifestyle of the elderly and issue an alert if they observe behavior that deviates from their normal rhythm. For example, the notification unit can analyze the sleep patterns of the elderly and pay particular attention if there is a high risk of nighttime excretion. This allows for the optimization of excretion management alerts based on the health status and lifestyle of the elderly. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the health status data of the elderly into AI and have the AI ​​perform the optimization of excretion management alerts.

[0102] The notification unit can estimate the emotions of elderly individuals and adjust the content of incontinence management notifications based on the estimated emotions. For example, if an elderly individual is feeling anxious, the notification unit can send a notification in a gentle voice. For example, if an elderly individual is feeling agitated, the notification unit can play calming music while sending the notification. For example, if an elderly individual is feeling lonely, the notification unit can suggest a video call with family members. This allows the content of incontinence management notifications to be adjusted based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI or not using AI. For example, the notification unit can input the elderly individual's emotion data into an AI and have the AI ​​adjust the notification content.

[0103] The notification unit can take region-specific measures for excretion management, taking into account the geographical location of the elderly person. For example, if the elderly person approaches a specific area, the notification unit can take measures appropriate to the characteristics of that area. For example, the notification unit can also notify family members or care staff if the elderly person is far from home. For example, the notification unit can issue an alert if the elderly person approaches a dangerous area. This allows for region-specific excretion management measures to be taken based on the geographical location of the elderly person. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the geographical location of the elderly person into the AI ​​and have the AI ​​execute region-specific measures.

[0104] The notification unit can analyze the social media activity of elderly individuals and detect the need for excretion management early. For example, the notification unit may determine that the need for excretion management is increasing if an elderly individual expresses anxiety on social media. The notification unit may also determine that the need for excretion management is increasing if an elderly individual expresses loneliness on social media. The notification unit may also determine that the need for excretion management is increasing if an elderly individual expresses excitement on social media. This allows for the early detection of the need for excretion management based on the social media activity of elderly individuals. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the elderly individual's social media data into AI and have the AI ​​perform the detection of the need for excretion management.

[0105] The response unit can estimate the emotions of elderly individuals and adjust its emergency response methods based on the estimated emotions. For example, if an elderly person is feeling anxious, the response unit will provide a quick and courteous response. If an elderly person is agitated, the response unit may prioritize calming the person. If an elderly person is relaxed, the response unit may provide a normal response. This allows the emergency response method to be adjusted based on the emotions of the elderly person. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the emotional data of elderly individuals into an AI and have the AI ​​adjust the emergency response method.

[0106] The response unit can analyze the past health status of elderly individuals and predict the risk of an emergency occurring. For example, the response unit can analyze the past health data of elderly individuals and issue an alert if the risk of an emergency is high. For example, the response unit can also consider the past medical history of elderly individuals and take emergency action if specific symptoms are observed. For example, the response unit can monitor the health status of elderly individuals and take early action if abnormalities are detected. This makes it possible to predict the risk of an emergency occurring based on the past health status of elderly individuals. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the past health data of elderly individuals into AI and have the AI ​​perform the prediction of the risk of an emergency occurring.

[0107] The response unit can customize its emergency response approach based on the elderly person's living environment and health condition. For example, the response unit can consider the elderly person's living environment and change the emergency response method under specific circumstances. For example, the response unit can monitor the elderly person's health condition and increase the frequency of emergency responses if they are unwell. For example, the response unit can learn the elderly person's daily rhythm and take emergency action if they observe behavior that deviates from their normal rhythm. This allows the emergency response approach to be customized based on the elderly person's living environment and health condition. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input data on the elderly person's living environment into AI and have the AI ​​perform the customization of the emergency response approach.

[0108] The response unit can estimate the emotions of elderly individuals and adjust the emergency response notification method based on the estimated emotions. For example, if an elderly person is feeling anxious, the response unit can notify them in a gentle voice. For example, if an elderly person is feeling agitated, the response unit can play calming music when notifying them. For example, if an elderly person is feeling lonely, the response unit can suggest a video call with family members. This allows the emergency response notification method to be adjusted based on the emotions of the elderly person. 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 response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the elderly person's emotion data into an AI and have the AI ​​adjust the notification method.

[0109] The response unit can take region-specific measures for emergency response, taking into account the geographical location information of the elderly person. For example, if the elderly person approaches a specific area, the response unit can take measures appropriate to the characteristics of that area. For example, if the elderly person is far from home, the response unit can also notify family members or care staff. For example, if the elderly person approaches a dangerous area, the response unit can issue an alert. This allows for region-specific emergency response measures to be taken based on the geographical location information of the elderly person. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the geographical location information of the elderly person into AI and have AI execute region-specific measures.

[0110] The response unit can analyze the social media activity of elderly individuals and detect the need for emergency response early. For example, the response unit may determine that the need for emergency response increases if an elderly individual expresses anxiety on social media. The response unit may also determine that the need for emergency response increases if an elderly individual expresses loneliness on social media. The response unit may also determine that the need for emergency response increases if an elderly individual expresses excitement on social media. This allows for the early detection of the need for emergency response based on the social media activity of elderly individuals. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the elderly individual's social media data into AI and have the AI ​​perform the detection of the need for emergency response.

[0111] The management unit can estimate the caregiver's emotions and adjust stress management methods based on the estimated emotions. For example, if the caregiver is feeling stressed, the management unit can provide relaxing music or videos. For example, if the caregiver is tired, the management unit can also send notifications encouraging them to take a break. For example, if the caregiver is relaxed, the management unit can also provide standard stress management methods. This allows for adjustment of stress management methods based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not using AI. For example, the management unit can input caregiver emotion data into AI and have the AI ​​adjust stress management methods.

[0112] The management department can analyze the caregiver's past mental health status and identify the optimal care method. For example, the management department can analyze the caregiver's past mental health data and propose the optimal care method. The management department can also consider the caregiver's past stress levels and modify the care method in specific situations. The management department can also monitor the caregiver's mental health status and take early action if any abnormalities are detected. This allows the management department to identify the optimal care method based on the caregiver's past mental health status. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the caregiver's past mental health data into AI and have the AI ​​identify the optimal care method.

[0113] The management department can customize stress management approaches based on the caregiver's living environment and workload. For example, the management department can consider the caregiver's living environment and change stress management methods under specific circumstances. The management department can also monitor the caregiver's workload and increase the frequency of stress management if the workload is heavy. The management department can also learn the caregiver's daily rhythm and implement stress management if behavior deviates from the normal rhythm is observed. This allows for the customization of stress management approaches based on the caregiver's living environment and workload. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input caregiver living environment data into AI and have the AI ​​customize the stress management approach.

[0114] The management unit can estimate the caregiver's emotions and adjust the stress management notification method based on the estimated emotions. For example, if the caregiver is stressed, the management unit may notify them in a gentle voice. For example, if the caregiver is tired, the management unit may notify them by playing relaxing music. For example, if the caregiver is relaxed, the management unit may provide the normal notification method. This allows the stress management notification method to be adjusted based on the caregiver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not using AI. For example, the management unit can input caregiver emotion data into AI and have the AI ​​adjust the notification method.

[0115] The management department can take into account the caregiver's geographical location and implement region-specific measures for stress management. For example, if the caregiver is in a particular region, the management department can provide stress management methods tailored to the characteristics of that region. For example, if the caregiver is far from home, the management department can also suggest ways to relax. For example, if the caregiver is in a dangerous area, the management department can increase the frequency of stress management. This allows for region-specific stress management measures to be implemented based on the caregiver's geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the caregiver's geographical location into the AI ​​and have the AI ​​implement region-specific measures.

[0116] The management department can analyze caregivers' social media activity and detect the need for stress management early. For example, the management department may determine that the need for stress management is high if a caregiver expresses stress on social media. The management department may also determine that the need for stress management is high if a caregiver appears tired on social media. The management department may also provide normal stress management methods if a caregiver appears relaxed on social media. This allows for the early detection of the need for stress management based on the caregiver's social media activity. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input caregiver social media data into AI and have the AI ​​perform the detection of the need for stress management.

[0117] The assistant unit can estimate the emotions of elderly individuals and adjust its communication methods based on those estimated emotions. For example, if an elderly individual is feeling anxious, the assistant unit can communicate in a gentle voice. If an elderly individual is feeling agitated, the assistant unit can also communicate while playing calming music. If an elderly individual is feeling lonely, the assistant unit can also suggest a video call with family members. This allows the communication methods to be adjusted based on the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the assistant unit may be performed using AI or not. For example, the assistant unit can input the elderly individual's emotional data into an AI and have the AI ​​adjust the communication methods.

[0118] The assistant unit can analyze the elderly person's past communication history and identify the optimal method of information sharing. For example, the assistant unit can analyze the elderly person's past communication history and propose the optimal method of information sharing. The assistant unit can also consider the elderly person's past communication patterns and modify the method of information sharing in specific situations. The assistant unit can also monitor the elderly person's communication history and respond early if any abnormalities are detected. This allows the assistant unit to identify the optimal method of information sharing based on the elderly person's past communication history. Some or all of the above processes in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input the elderly person's past communication data into AI and have the AI ​​identify the optimal method of information sharing.

[0119] The assistant unit can customize its communication approach based on the elderly person's living environment and health condition. For example, the assistant unit can consider the elderly person's living environment and change its communication method under specific circumstances. For example, the assistant unit can monitor the elderly person's health condition and increase the frequency of communication if they are unwell. For example, the assistant unit can learn the elderly person's daily rhythm and communicate if they observe behavior that deviates from their usual rhythm. This allows the communication approach to be customized based on the elderly person's living environment and health condition. Some or all of the above processes in the assistant unit may be performed using AI, for example, or not using AI. For example, the assistant unit can input data on the elderly person's living environment into AI and have the AI ​​perform the customization of the communication approach.

[0120] The assistant unit can estimate the emotions of elderly individuals and adjust the communication notification method based on the estimated emotions. For example, if an elderly individual is feeling anxious, the assistant unit can notify them in a gentle voice. For example, if an elderly individual is feeling agitated, the assistant unit can play calming music when notifying them. For example, if an elderly individual is feeling lonely, the assistant unit can suggest a video call with family members. This allows the communication notification method to be adjusted based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the assistant unit may be performed using AI, for example, or not using AI. For example, the assistant unit can input the elderly individual's emotion data into an AI and have the AI ​​adjust the notification method.

[0121] The assistant unit can take region-specific measures for communication, taking into account the geographical location of the elderly person. For example, if the elderly person is in a specific area, the assistant unit can provide communication methods tailored to the characteristics of that area. For example, the assistant unit can also notify family members or caregivers if the elderly person is far from home. For example, if the elderly person is in a dangerous area, the assistant unit can increase the frequency of communication. This allows for region-specific communication measures to be taken based on the geographical location of the elderly person. Some or all of the above processing in the assistant unit may be performed using AI, for example, or not using AI. For example, the assistant unit can input the geographical location of the elderly person into the AI ​​and have the AI ​​implement region-specific measures.

[0122] The assistant unit can analyze the social media activity of elderly individuals and detect their need for communication early. For example, the assistant unit may determine that the need for communication is high if an elderly individual expresses anxiety on social media. The assistant unit may also determine that the need for communication is high if an elderly individual feels lonely on social media. The assistant unit may also determine that the need for communication is high if an elderly individual is excited on social media. This allows for the early detection of the need for communication based on the elderly individual's social media activity. Some or all of the above processing in the assistant unit may be performed using AI, for example, or without AI. For example, the assistant unit can input the elderly individual's social media data into AI and have the AI ​​perform the detection of the need for communication.

[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 prevention unit can estimate the emotions of elderly individuals, predict the risk of wandering based on those estimated emotions, and take preventative measures. For example, if an elderly person is feeling anxious, the AI ​​can detect that emotion and notify family members or caregivers for early intervention. Similarly, if an elderly person is feeling lonely, the AI ​​can detect that emotion and suggest actions to facilitate communication. Furthermore, if an elderly person is agitated, the AI ​​can detect that emotion and provide calming music or videos. This allows for the prediction of wandering risk based on the elderly person's emotions and the implementation of appropriate preventative measures.

[0125] The prevention unit can analyze the past behavioral patterns of elderly individuals and identify times and locations where wandering is likely. For example, it can analyze the times when elderly individuals have wandered in the past and pay particular attention to those times. It can also identify locations where elderly individuals have wandered in the past and issue an alert when they approach those locations. Furthermore, it can learn the behavioral patterns of elderly individuals and respond early if signs of wandering are observed. This allows for the identification of wandering risks based on the elderly individual's past behavioral patterns and enables early intervention.

[0126] The prevention unit can optimize wandering prevention alerts based on the health status and lifestyle of elderly individuals. For example, it can monitor the health status of elderly individuals and strengthen wandering prevention alerts if they are unwell. It can also learn the lifestyle of elderly individuals and issue alerts if behavior deviates from their normal rhythm. Furthermore, it can analyze the sleep patterns of elderly individuals and pay particular attention to those at high risk of nighttime wandering. This allows for the optimization of wandering prevention alerts based on the health status and lifestyle of elderly individuals.

[0127] The prevention unit can estimate the emotions of elderly individuals and adjust the method of notifying them of wandering prevention based on those estimated emotions. For example, if an elderly person is feeling anxious, it can notify them with a gentle voice. If an elderly person is agitated, it can play calming music while notifying them. Furthermore, if an elderly person is feeling lonely, it can suggest a video call with family members. In this way, the method of notifying elderly individuals of wandering prevention can be adjusted based on their emotions.

[0128] The prevention unit can take into account the geographical location information of elderly people and implement region-specific measures to prevent wandering. For example, if an elderly person approaches a specific area, measures can be taken according to the characteristics of that area. It can also notify family members or care staff if an elderly person has moved far from home. Furthermore, it can issue an alert if an elderly person approaches a dangerous area. This allows for the implementation of region-specific wandering prevention measures based on the geographical location information of elderly people.

[0129] The verification unit can estimate the emotions of elderly individuals and adjust the frequency and method of safety checks based on the estimated emotions. For example, if an elderly person is feeling anxious, the frequency of safety checks can be increased. Conversely, if an elderly person is relaxed, the frequency of safety checks can be decreased. Furthermore, if an elderly person is agitated, the method of safety checks can be changed. In this way, the frequency and method of safety checks can be adjusted based on the emotions of elderly individuals.

[0130] The verification unit can analyze the elderly person's past safety check history and identify the optimal timing for checks. For example, it can analyze the time periods when the elderly person has performed safety checks in the past and pay particular attention to those times. It can also identify locations where the elderly person has performed safety checks in the past and issue an alert when approaching those locations. Furthermore, it can learn from the elderly person's safety check history and identify the optimal timing for checks. This allows the system to determine the optimal timing for checks based on the elderly person's past safety check history.

[0131] The verification unit can customize its safety verification approach based on the health status and living environment of the elderly person. For example, it can monitor the elderly person's health and increase the frequency of safety checks if they are unwell. It can also consider the elderly person's living environment and modify the safety verification method under specific circumstances. Furthermore, it can learn the elderly person's daily rhythm and perform safety checks if behavior deviates from the normal rhythm is observed. In this way, the safety verification approach can be customized based on the health status and living environment of the elderly person.

[0132] The verification unit can estimate the emotions of elderly individuals and adjust the safety confirmation notification method based on those estimated emotions. For example, if an elderly person is feeling anxious, a gentle voice notification can be sent. If an elderly person is agitated, calming music can be played during the notification. Furthermore, if an elderly person is feeling lonely, a video call with family members can be suggested. This allows the safety confirmation notification method to be adjusted based on the emotions of the elderly person.

[0133] The monitoring unit can take region-specific safety measures into account, considering the geographical location of elderly individuals. For example, if an elderly person approaches a specific area, measures tailored to the characteristics of that area can be taken. Furthermore, if an elderly person travels far from home, family members or care staff can be notified. Additionally, an alert can be issued if an elderly person approaches a dangerous area. This allows for region-specific safety measures to be implemented based on the geographical location of elderly individuals.

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

[0135] Step 1: The prevention unit provides a wandering prevention function. The prevention unit monitors the movements of elderly people when they leave their homes and notifies family members or caregivers if any abnormalities are detected. The prevention unit can track location information and issue an alert if the elderly person leaves their home. It can also notify family members or caregivers if abnormal behavior is detected. Step 2: The verification unit performs safety checks based on the information provided by the prevention unit. The verification unit periodically checks the location information of the elderly person and verifies for any abnormalities. If an abnormality is detected, it can notify family members or care staff. It can also learn the behavioral patterns of the elderly person and detect abnormal behavior at an early stage. Step 3: The notification unit provides an excretion management notification function. The notification unit monitors the excretion status of the elderly person and notifies family members or care staff as needed. It can detect and notify about excretion status using sensors. It can also predict the timing of excretion and notify in advance. Step 4: The response unit performs autonomous emergency response based on the information provided by the notification unit. The response unit monitors the health status of the elderly person in real time and automatically takes emergency action when an emergency occurs. If the elderly person falls, it can immediately notify emergency contacts and instruct them on the necessary actions. It can also contact medical institutions in the event of an emergency to ensure a swift response. Step 5: The management department manages and cares for caregivers' stress. The management department monitors the caregivers' mental health status and provides referrals to specialists and stress management advice as needed. They can measure the caregivers' stress levels and provide appropriate care. They can also regularly check the caregivers' mental health status and provide counseling as needed. Step 6: The Assistant Unit provides real-time communication assistant functionality. The Assistant Unit supports communication between the elderly, their families, and care staff, enabling real-time information sharing. It provides video call and chat functions to facilitate smooth communication between the elderly, their families, and care staff. It also saves the communication history, which can be referenced as needed.

[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 prevention unit, confirmation unit, notification unit, response unit, management unit, and assistant unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the prevention unit monitors the movements of the elderly person using the camera 42 and microphone 38B of the smart device 14 and detects abnormalities with the control unit 46A. The confirmation unit periodically checks the location information of the elderly person using the identification processing unit 290 of the data processing unit 12 and performs safety checks. The notification unit monitors the excretion status using the sensors of the smart device 14 and notifies the user using the control unit 46A. The response unit performs emergency response using the identification processing unit 290 of the data processing unit 12 and instructs the necessary actions. The management unit monitors the mental health status of the caregiver using the identification processing unit 290 of the data processing unit 12 and provides appropriate care. The assistant unit supports real-time communication 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 examples 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 prevention unit, confirmation unit, notification unit, response unit, management unit, and assistant unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the prevention unit monitors the movements of the elderly person using the camera 42 and microphone 238 of the smart glasses 214 and detects abnormalities with the control unit 46A. The confirmation unit periodically checks the location information of the elderly person using the identification processing unit 290 of the data processing unit 12 and performs safety checks. The notification unit monitors the excretion status using the sensors of the smart glasses 214 and notifies the user using the control unit 46A. The response unit performs emergency response using the identification processing unit 290 of the data processing unit 12 and instructs the necessary actions. The management unit monitors the mental health status of the caregiver using the identification processing unit 290 of the data processing unit 12 and provides appropriate care. The assistant unit supports real-time communication 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 examples 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 prevention unit, confirmation unit, notification unit, response unit, management unit, and assistant unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the prevention unit monitors the movements of the elderly person using the camera 42 and microphone 238 of the headset terminal 314 and detects abnormalities with the control unit 46A. The confirmation unit periodically checks the location information of the elderly person using the identification processing unit 290 of the data processing unit 12 and performs safety checks. The notification unit monitors the excretion status using the sensors of the headset terminal 314 and notifies the user using the control unit 46A. The response unit performs emergency response using the identification processing unit 290 of the data processing unit 12 and instructs the necessary actions. The management unit monitors the mental health status of the caregiver using the identification processing unit 290 of the data processing unit 12 and provides appropriate care. The assistant unit supports real-time communication using the control unit 46A of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[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 prevention unit, confirmation unit, notification unit, response unit, management unit, and assistant unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the prevention unit monitors the movements of the elderly person using the camera 42 and microphone 238 of the robot 414 and detects abnormalities with the control unit 46A. The confirmation unit periodically checks the location information of the elderly person using the specific processing unit 290 of the data processing unit 12 and performs safety checks. The notification unit monitors the excretion status using the sensors of the robot 414 and notifies the user via the control unit 46A. The response unit performs emergency response using the specific processing unit 290 of the data processing unit 12 and instructs the necessary actions. The management unit monitors the mental health status of the caregiver using the specific processing unit 290 of the data processing unit 12 and provides appropriate care. The assistant unit supports real-time communication using the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples 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) A prevention unit that provides a wandering prevention function, A confirmation unit that performs safety checks based on the information provided by the prevention unit, An announcement unit that provides an excretion management notification function, A response unit that performs autonomous emergency response based on the information provided by the aforementioned notification unit, The management department is responsible for managing and caring for the stress of caregivers, It comprises an assistant unit that provides real-time communication assistant functionality. A system characterized by the following features. (Note 2) The aforementioned prevention unit is The system monitors the movements of elderly people when they leave their homes and notifies family members or care staff if any abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification section is, Monitor the excretory status of elderly individuals and notify family members and care staff as needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The corresponding part is, The system monitors the health status of elderly individuals in real time and automatically takes emergency action in the event of an emergency. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, We monitor the mental health of caregivers and provide referrals to specialists and stress management advice as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned assistant section is It supports communication between the elderly, their families, and care staff, and enables real-time information sharing. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned prevention unit is To estimate the emotions of elderly people, predict the risk of wandering based on the estimated emotions of elderly people, and take preventive measures. The system described in Appendix 2, characterized by the features described herein. (Note 8) The aforementioned prevention unit is Analyze the past behavioral patterns of elderly individuals to identify times and locations where wandering is likely. The system described in Appendix 2, characterized by the features described herein. (Note 9) The aforementioned prevention unit is Optimize wandering prevention alerts based on the health status and lifestyle of elderly individuals. The system described in Appendix 2, characterized by the features described herein. (Note 10) The aforementioned prevention unit is The system estimates the emotions of elderly individuals and adjusts the notification method for preventing wandering based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 11) The aforementioned prevention unit is Taking into account the geographical location information of elderly people, implement region-specific measures to prevent wandering. The system described in Appendix 2, characterized by the features described herein. (Note 12) The aforementioned prevention unit is Analyzing the social media activity of elderly people to detect early signs of increased risk of wandering. The system described in Appendix 2, characterized by the features described herein. (Note 13) The aforementioned verification unit is The system estimates the emotions of elderly individuals and adjusts the frequency and methods of safety checks based on these estimates. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned verification unit is Analyze the past safety check history of elderly individuals to identify the optimal timing for checks. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned verification unit is Customize safety assessment approaches based on the health status and living environment of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned verification unit is The system estimates the emotions of elderly individuals and adjusts the notification method for safety checks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned verification unit is Taking into account the geographical location information of elderly people, take region-specific measures to ensure their safety. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned verification unit is Analyze the social media activity of elderly individuals to detect the need for safety checks early. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification section is, The system estimates the emotions of elderly individuals and adjusts the notification method for excretion management based on the estimated emotions of the elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification section is, Analyze the past bowel movement patterns of elderly individuals to identify the optimal timing for notifications. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification section is, Optimize excretion management alerts based on the health status and lifestyle of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification section is, The system estimates the emotions of elderly individuals and adjusts the content of excretion management notifications based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification section is, Taking into account the geographical location of elderly people, region-specific measures should be implemented for excretion management. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification section is, Analyzing the social media activity of elderly individuals to detect the need for excretion management early. The system described in Appendix 1, characterized by the features described herein. (Note 25) The corresponding part is, The system estimates the emotions of elderly individuals and adjusts emergency response methods based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The corresponding part is, Analyzing the past health status of elderly individuals to predict the risk of emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 27) The corresponding part is, Customize emergency response approaches based on the living environment and health status of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 28) The corresponding part is, The system estimates the emotions of elderly individuals and adjusts emergency response notification methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The corresponding part is, Taking into account the geographical location of elderly people, region-specific measures will be implemented for emergency response. The system described in Appendix 1, characterized by the features described herein. (Note 30) The corresponding part is, Analyze the social media activity of the elderly to detect the need for emergency response early. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, The system estimates the caregiver's emotions and adjusts stress management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, Analyze the caregiver's past mental health history to identify the most suitable care methods. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, Customize stress management approaches based on the caregiver's living environment and workload. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, The system estimates the caregiver's emotions and adjusts stress management notification methods based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, Considering the geographical location of caregivers, implement region-specific measures for stress management. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned management department, Analyze caregivers' social media activity to detect the need for stress management early. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned assistant section is The system estimates the emotions of elderly individuals and adjusts communication methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned assistant section is Analyze the past communication history of elderly individuals to identify the optimal method of information sharing. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned assistant section is Customize communication approaches based on the living environment and health status of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned assistant section is The system estimates the emotions of older adults and adjusts communication notification methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned assistant section is Taking into account the geographical location of elderly people, implement region-specific measures for communication. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned assistant section is Analyze the social media activities of older adults to detect their communication needs early. 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. A prevention unit that provides a wandering prevention function, A confirmation unit that performs safety checks based on the information provided by the prevention unit, An announcement unit that provides an excretion management notification function, A response unit that performs autonomous emergency response based on the information provided by the aforementioned notification unit, The management department is responsible for managing and caring for the stress of caregivers, It comprises an assistant unit that provides real-time communication assistant functionality. A system characterized by the following features.

2. The aforementioned prevention unit is The system monitors the movements of elderly people when they leave their homes and notifies family members or care staff if any abnormalities are detected. The system according to feature 1.

3. The aforementioned notification section is, Monitor the excretory status of elderly individuals and notify family members or care staff as needed. The system according to feature 1.

4. The corresponding part is, The system monitors the health status of elderly individuals in real time and automatically takes emergency action in the event of an emergency. The system according to feature 1.

5. The aforementioned management department, We monitor the mental health of caregivers and provide referrals to specialists and stress management advice as needed. The system according to feature 1.

6. The aforementioned assistant section is It supports communication between the elderly, their families, and care staff, and enables real-time information sharing. The system according to feature 1.

7. The aforementioned prevention unit is To estimate the emotions of elderly people, predict the risk of wandering based on those estimated emotions, and take preventive measures. The system according to feature 2.

8. The aforementioned prevention unit is Analyze the past behavioral patterns of elderly individuals to identify times and locations where wandering is likely. The system according to feature 2.

9. The aforementioned prevention unit is Optimize wandering prevention alerts based on the health status and lifestyle of elderly individuals. The system according to feature 2.

Images