system

Abstract

The system according to this embodiment aims to automate and streamline the workflow in care support. [Solution] The system according to the embodiment comprises a planning unit, a monitoring unit, an instruction unit, an execution monitoring unit, and a notification unit. The planning unit plans a care plan. The monitoring unit monitors the health status based on the care plan planned by the planning unit. The instruction unit instructs medical measures based on the health status monitored by the monitoring unit. The execution monitoring unit monitors the implementation status of the care plan based on the medical measures instructed by the instruction unit. The notification unit issues emergency alerts and notifies external parties based on the implementation status of the care plan monitored by the execution monitoring unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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, although AI is introduced into a part of the business process in caregiving support, there is room for improvement in automating the entire business process.

[0005] The system according to the embodiment aims to automate and improve the business process in caregiving support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a planning unit, a monitoring unit, an instruction unit, an execution monitoring unit, and a notification unit. The planning unit plans a care plan. The monitoring unit monitors the health status based on the care plan planned by the planning unit. The instruction unit instructs medical measures based on the health status monitored by the monitoring unit. The execution monitoring unit monitors the implementation status of the care plan based on the medical measures instructed by the instruction unit. The notification unit issues emergency alerts and notifies external parties based on the implementation status of the care plan monitored by the execution monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate and streamline the workflow in care support. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 care support system according to an embodiment of the present invention is a system for advancing the use of AI in care support. While AI has already been introduced into some areas of care support systems, such as the creation of care plans, human intervention is still required for tasks that connect these processes. By integrating these processes and building a system in which AI automatically makes decisions and repeatedly learns while considering the overall workflow, the aim is to contribute to solving Japan's increasingly serious aging problem. For example, in the care support system, the AI ​​formulates a care plan. Next, the AI ​​monitors the user's health condition and instructs medical measures as needed. Furthermore, the care support system monitors the implementation status of the care plan and issues warnings or notifies external parties in case of emergencies. In addition, the care support system grasps all information related to care and takes the following actions. This includes managing finances, coordinating with designated driver services, managing hygiene in the living space, providing entertainment, reporting to family members about the care situation, and acting as a question answerer or conversational partner. For example, in the care support system, the AI ​​formulates a care plan. In this process, the AI ​​creates an optimal care plan based on information such as the user's health condition, living environment, and past care plans. For example, if a user's health deteriorates, the AI ​​will create a corresponding care plan and instruct necessary medical actions. Next, the care support system uses AI to monitor the user's health. The AI ​​monitors the user's health data, such as body temperature, blood pressure, and heart rate, in real time, and instructs medical actions if an abnormality is detected. For example, if a user's body temperature rises rapidly, the AI ​​will contact a medical institution and instruct appropriate action. Furthermore, the care support system uses AI to monitor the implementation of the care plan. The AI ​​monitors whether the care plan is being properly implemented and issues warnings or reports to external parties if problems occur. For example, if a user is not following the care plan, the AI ​​will alert the caregiver and contact family or medical institutions as needed. The care support system also grasps all information related to care and takes the following actions. This includes managing finances, coordinating with designated driver services, managing the hygiene of the living space, providing entertainment, reporting to family members about the care situation, and acting as a question answerer or conversational partner. For example, the care support system manages the user's finances and automatically makes necessary payments.Furthermore, the care support system will integrate with a designated driver service to assist users with their transportation. In addition, the care support system will manage the hygiene of the living space, providing a comfortable environment for users. In this way, the care support system will improve the efficiency and quality of caregiving operations, contributing to the resolution of Japan's increasingly serious aging population problem. Thus, the care support system will improve the efficiency and quality of caregiving operations.

[0029] The care support system according to this embodiment comprises a planning unit, a monitoring unit, an instruction unit, an execution monitoring unit, and a notification unit. The planning unit plans a care plan. The planning unit creates an optimal care plan based on information such as the user's health condition, living environment, and past care plans. For example, if the user's health condition deteriorates, the planning unit can plan a corresponding care plan and instruct necessary medical measures. The planning unit can also use AI to analyze the user's health condition and living environment and plan an optimal care plan. The monitoring unit monitors the health condition based on the care plan planned by the planning unit. The monitoring unit monitors health data such as the user's body temperature, blood pressure, and heart rate in real time. For example, if an abnormality is detected, the monitoring unit can instruct medical measures. The monitoring unit can also use AI to analyze health data and detect abnormalities. The instruction unit instructs medical measures based on the health condition monitored by the monitoring unit. The instruction unit can, for example, contact a medical institution and instruct appropriate action if the user's body temperature rises rapidly. The instruction unit can also, for example, use AI to analyze health data and instruct appropriate medical measures. The execution monitoring unit monitors the implementation status of the care plan based on the medical measures instructed by the instruction unit. The execution monitoring unit monitors, for example, whether the care plan is being implemented appropriately. The execution monitoring unit can, for example, issue warnings or notify external parties if a problem occurs. The execution monitoring unit can also, for example, use AI to monitor the implementation status of the care plan and detect problems. The notification unit issues warnings or notifies external parties in emergencies based on the implementation status of the care plan monitored by the execution monitoring unit. The notification unit can, for example, alert caregivers if the user is not following the care plan and contact family members or medical institutions as needed. The notification unit can also, for example, use AI to analyze the situation in an emergency and make appropriate notifications. As a result, the care support system according to this embodiment integrates everything from care plan planning to implementation, monitoring, and notification, and the AI ​​automatically makes decisions and repeatedly learns, thereby improving the efficiency and quality of care support.

[0030] The planning department develops care plans. Based on information such as the user's health status, living environment, and past care plans, the planning department creates the optimal care plan. Specifically, data on the user's health status is collected from regular health checkup results and daily health monitoring data. This includes vital signs such as blood pressure, body temperature, heart rate, and blood glucose levels. Information on the living environment includes the user's residence, family structure, and daily activity patterns. Past care plans include data on the content and effectiveness of care provided to date, as well as the user's responses. The planning department comprehensively analyzes this information to assess the user's current condition and future risks. For example, if the user's health status deteriorates, the planning department can develop a corresponding care plan and instruct necessary medical measures. The planning department can also use AI to analyze the user's health status and living environment and develop the optimal care plan. By learning from past data and building pattern recognition and predictive models, the AI ​​can provide customized care plans for each user. For example, the AI ​​can analyze the user's health data and detect specific symptoms or risk factors early. This allows the planning department to quickly develop appropriate care plans tailored to the user's health condition, thereby improving the quality of care support. Furthermore, the planning department can share the contents of the care plan through communication with the user and their family, and reflect the user's intentions and wishes. This can increase user satisfaction and improve the implementation rate of care plans.

[0031] The monitoring unit monitors the user's health status based on the care plan developed by the planning unit. The monitoring unit monitors health data such as the user's body temperature, blood pressure, and heart rate in real time. Specifically, this data is continuously collected through wearable devices worn by the user and home health monitoring devices. These devices transmit data to a central database via Bluetooth® or Wi-Fi, allowing the monitoring unit to access it in real time. The monitoring unit can, for example, instruct medical action if an abnormality is detected. For instance, if a user's body temperature rises rapidly, the monitoring unit immediately issues an alert and contacts a medical institution. The monitoring unit can also use AI to analyze health data and detect abnormalities. The AI ​​learns patterns in normal health data and uses algorithms to quickly identify abnormal data. This allows the monitoring unit to constantly monitor the user's health status and respond quickly when an abnormality occurs. Furthermore, the monitoring unit can analyze trends in the user's health status based on past health data and assess long-term risks. For example, it can analyze blood pressure data from the past few months to assess whether the user is at risk of hypertension. This allows the monitoring department to develop preventative care plans and support users in maintaining their health.

[0032] The command unit directs medical actions based on the health status monitored by the monitoring unit. For example, if a user's body temperature rises rapidly, the command unit can contact a medical institution and instruct appropriate action. Specifically, the command unit analyzes the user's health data in real time, and if an abnormality is detected, it instructs action according to a pre-configured protocol. For example, if the body temperature exceeds a certain threshold, it instructs cooling measures and contacts a medical institution as necessary. The command unit can also use AI to analyze health data and instruct appropriate medical actions. Based on past data, the AI ​​can learn the optimal response to specific symptoms and risk factors and issue instructions quickly and accurately. For example, if a user's heart rate is abnormally high, the AI ​​will instruct rest and contact a medical institution as necessary. Furthermore, the command unit can share the details of medical actions through communication with the user and their family, and reflect the user's intentions and wishes. This can increase the user's sense of security and improve the rate of medical action implementation. In addition, the command unit can strengthen cooperation with medical institutions and care facilities to achieve quick and appropriate responses. For example, by sharing data with medical institutions and providing real-time information on the user's health status, medical institutions can respond quickly. This allows the command center to promptly issue appropriate medical instructions based on the user's health condition, thereby improving the quality of care support.

[0033] The Execution Monitoring Unit monitors the implementation status of care plans based on medical procedures instructed by the Instruction Unit. For example, the Execution Monitoring Unit monitors whether the care plan is being implemented appropriately. Specifically, the Execution Monitoring Unit conducts regular checks and monitoring to ensure that users and caregivers are acting in accordance with the care plan. For example, it verifies whether users are taking prescribed medications at the appropriate time and whether they are performing regular exercise and rehabilitation. The Execution Monitoring Unit can also issue warnings and notify external parties if problems occur. For example, if a user forgets to take their medication, it will issue an alert and notify the caregiver. Furthermore, the Execution Monitoring Unit can use AI to monitor the implementation status of care plans and detect problems. The AI ​​analyzes user behavior data and can respond quickly if it detects deviations from the care plan or abnormal behavior. For example, if a user neglects regular exercise, it will issue an alert and notify the caregiver. The Execution Monitoring Unit can also share the status of care plan implementation through communication with users and caregivers and respond quickly if problems occur. This allows the Execution Monitoring Unit to constantly understand the status of care plan implementation and respond quickly when problems arise. Furthermore, the implementation monitoring unit can accumulate data on the implementation status of care plans and use it to improve future care plans. This allows the implementation monitoring unit to monitor the implementation status of care plans and respond quickly when problems arise, thereby improving the quality of care support.

[0034] The notification unit issues emergency alerts and makes external notifications based on the implementation status of care plans monitored by the execution monitoring unit. For example, if a user is not following the care plan, the notification unit can alert the caregiver and contact family or medical institutions as needed. Specifically, the notification unit receives alerts from the execution monitoring unit and instructs the user or caregiver to take appropriate action. For example, if a user forgets to take their medication, the notification unit will notify the caregiver and contact family or medical institutions as needed. The notification unit can also use AI to analyze emergency situations and make appropriate notifications. Based on past data, the AI ​​can learn emergency patterns and respond quickly and accurately. For example, if a user's health condition rapidly deteriorates, the AI ​​will analyze the situation and instruct appropriate action. The notification unit can also share emergency situations and respond quickly through communication with users and caregivers. This allows the notification unit to respond quickly to emergencies and ensure the user's safety. Furthermore, the notification unit can accumulate data on emergency situations and use it to improve future responses. This allows the reporting department to respond quickly and appropriately to emergency situations, ensuring user safety and thereby improving the quality of care support.

[0035] The management department can manage finances. For example, the management department can record user expenses. The management department can also manage user budgets. For example, the management department can automate payments. For example, the management department can use AI to record user expenses and manage budgets. By automating financial management, the burden on users can be reduced, and the efficiency of care support can be improved.

[0036] The integration unit can integrate with designated driver services. For example, the integration unit can support the user's transportation. The integration unit can also automate the use of designated driver services. For example, the integration unit can use AI to integrate with designated driver services. This automates the integration with designated driver services, thereby supporting the user's transportation and improving the efficiency of care support.

[0037] The Hygiene Management Department can manage the hygiene of living spaces. For example, the Hygiene Management Department can clean the user's living space. For example, the Hygiene Management Department can also perform disinfection. For example, the Hygiene Management Department can use AI to manage the hygiene of living spaces. By automating the hygiene management of living spaces, it is possible to provide a comfortable environment for users and improve the efficiency of care support.

[0038] The service provider can provide entertainment. For example, the service provider can provide users with television viewing. For example, the service provider can provide users with games. For example, the service provider can provide users with reading material. For example, the service provider can use AI to provide users with the most suitable entertainment. By automating the provision of entertainment, it is possible to improve the quality of life for users and increase the efficiency of care support.

[0039] The reporting unit can provide families with reports on the care situation. For example, the reporting unit can report the user's health status to the family. The reporting unit can also report to the family on the progress of the user's care plan. The reporting unit can also use AI to report on the care situation. By automating the reporting of the care situation, information can be provided to families more smoothly, and the efficiency of care support can be improved.

[0040] The proxy unit can answer questions about unclear points and act as a conversational partner. For example, the proxy unit can answer questions about the user's medical care. For example, the proxy unit can also answer questions about the user's daily life. For example, the proxy unit can act as a conversational partner for the user. For example, the proxy unit can use AI to answer questions and act as a conversational partner for the user. By automating questioning and conversational support, it is possible to reduce user anxiety and improve the efficiency of care support.

[0041] The planning department can analyze the results of past care plan implementations and develop the most effective care plan. For example, based on the results of past care plan implementations, the planning department can develop a care plan that prioritizes activities that users enjoyed. For example, based on the results of past care plan implementations, the planning department can also develop a care plan that includes many activities that were highly effective. For example, based on the results of past care plan implementations, the planning department can develop a care plan that avoids activities that were prone to causing problems. In this way, by analyzing the results of past care plan implementations, it is possible to provide more effective care plans.

[0042] The planning department can customize care plans based on the user's living environment and family structure. For example, if the user lives alone, the planning department can create a care plan that increases the frequency of home care visits. If the user lives with family, the planning department can also create a care plan that assumes cooperation with family members. For example, the planning department can create a care plan that includes a balanced mix of indoor and outdoor activities, tailored to the user's living environment. This allows for the provision of more individualized care plans by considering the user's living environment and family structure.

[0043] The planning unit can create the most effective care plan based on the user's geographical location information. For example, if the user lives in an urban area, the planning unit can create a care plan that utilizes nearby facilities. If the user lives in a suburban area, the planning unit can also create a care plan that takes transportation into consideration. If the user lives in a remote area, the planning unit can also create a care plan that includes online activities. This allows for the provision of more appropriate care plans by considering the user's geographical location information.

[0044] The planning department can analyze a user's social media activity when creating a care plan and develop a relevant care plan. For example, the planning department can develop a care plan that includes activities the user has shown interest in on social media. The planning department can also develop a care plan that includes activities the user engages in with friends on social media. For example, the planning department can develop a care plan based on health information the user shares on social media. This allows for the provision of more appropriate care plans by analyzing the user's social media activity.

[0045] The monitoring unit can detect abnormal values ​​in health data in real time and respond quickly. For example, if a user's body temperature rises rapidly, the monitoring unit can immediately contact a medical institution. For example, if a user's blood pressure shows an abnormal value, the monitoring unit can quickly instruct medical treatment. For example, if a user's heart rate shows an abnormal value, the monitoring unit can immediately notify a caregiver. This enables rapid response by detecting abnormal values ​​in health data in real time.

[0046] The monitoring unit can predict changes in a user's health status by referring to their past health data. For example, the monitoring unit can predict seasonal changes in health status from past health data. For example, the monitoring unit can also predict changes in health status after a specific activity from past health data. For example, the monitoring unit can also predict long-term trends in health status from past health data. This allows for the prediction of changes in health status and appropriate responses by referring to past health data.

[0047] The monitoring unit can set the most effective monitoring timing based on the user's lifestyle when monitoring their health status. For example, the monitoring unit can monitor the user's health status according to their wake-up time. For example, the monitoring unit can monitor the user's health status according to their meal times. For example, the monitoring unit can monitor the user's health status according to their bedtime. This allows for more appropriate health monitoring by taking the user's lifestyle into consideration.

[0048] The monitoring unit can customize its monitoring content when monitoring the user's health status, taking into account the user's diet and exercise habits. For example, the monitoring unit can monitor blood glucose levels based on the user's diet. For example, the monitoring unit can also monitor heart rate based on the user's exercise habits. For example, the monitoring unit can perform comprehensive health monitoring, taking into account the balance between the user's diet and exercise. This allows for more appropriate health monitoring by considering the user's diet and exercise habits.

[0049] The instruction unit can refer to the user's past medical history to provide optimal instructions when issuing medical treatment orders. For example, the instruction unit can recommend appropriate medical treatment based on the user's past allergy information. For example, the instruction unit can recommend the optimal treatment method based on the user's past medical history. For example, the instruction unit can recommend appropriate medications based on the user's past medication use history. This allows for the provision of more appropriate medical treatment by referring to the user's past medical history.

[0050] The instruction unit can reflect the user's current health status in real time when issuing medical treatment instructions. For example, the instruction unit can instruct appropriate medical treatment based on the user's current body temperature. For example, the instruction unit can instruct the optimal treatment method based on the user's current blood pressure. For example, the instruction unit can instruct appropriate medication based on the user's current heart rate. This allows for the provision of more appropriate medical treatment by reflecting the user's current health status in real time.

[0051] The instruction unit can provide the most effective instructions for medical treatment based on the user's living environment. For example, if the user lives alone, the instruction unit may instruct an increase in the frequency of home visits. If the user lives with family, the instruction unit can also instruct medical treatment that requires cooperation with family members. The instruction unit can also provide instructions for a balanced mix of indoor and outdoor medical treatment, tailored to the user's living environment. This allows for the provision of more appropriate medical treatment by considering the user's living environment.

[0052] The instruction unit can strengthen collaboration with the user's family and caregivers when issuing instructions for medical procedures. For example, the instruction unit can explain the details of the medical procedure to the user's family. For example, the instruction unit can also provide specific instructions on the steps of the medical procedure to the user's caregivers. For example, the instruction unit can strengthen collaboration with the user's family and caregivers and maximize the effectiveness of the medical procedure. This strengthens collaboration with the user's family and caregivers, enabling the provision of more appropriate medical procedures.

[0053] The implementation monitoring unit can monitor the status of care plan implementation in real time and respond quickly if problems occur. For example, the implementation monitoring unit can monitor the status of care plan implementation in real time and immediately notify caregivers if an abnormality occurs. For example, the implementation monitoring unit can monitor the status of care plan implementation in real time and quickly contact medical institutions if problems occur. For example, the implementation monitoring unit can monitor the status of care plan implementation in real time and quickly notify families if problems occur. This enables a rapid response by monitoring the status of care plan implementation in real time.

[0054] The implementation monitoring unit can identify areas for improvement by comparing the implementation status of care plans with past data. For example, the implementation monitoring unit can identify activities that were highly effective by comparing the implementation status of care plans with past data. For example, the implementation monitoring unit can also identify activities that were prone to problems by comparing the implementation status of care plans with past data. For example, the implementation monitoring unit can identify areas for improvement by comparing the implementation status of care plans with past data and reflect them in the next care plan. In this way, by comparing the implementation status of care plans with past data, areas for improvement can be identified and reflected in the next care plan.

[0055] The execution monitoring unit can set the most effective monitoring timing based on the user's daily rhythm when monitoring the implementation status of the care plan. For example, the execution monitoring unit can monitor the implementation status of the care plan in accordance with the user's wake-up time. For example, the execution monitoring unit can also monitor the implementation status of the care plan in accordance with the user's meal times. For example, the execution monitoring unit can also monitor the implementation status of the care plan in accordance with the user's bedtime. This allows for more appropriate care plan implementation monitoring by taking the user's daily rhythm into consideration.

[0056] The implementation monitoring unit can strengthen collaboration with the user's family and caregivers when monitoring the implementation status of the care plan. For example, the implementation monitoring unit can report the implementation status of the care plan in detail to the user's family. For example, the implementation monitoring unit can also give specific instructions to the user's caregivers regarding the implementation status of the care plan. For example, the implementation monitoring unit can also optimize the implementation status of the care plan by strengthening collaboration with the user's family and caregivers. This strengthens collaboration with the user's family and caregivers, enabling more appropriate monitoring of the care plan's implementation.

[0057] The notification system can determine the most appropriate notification content in emergency situations by referring to the user's past health data. For example, the notification system can notify the appropriate medical institution based on the user's past health data. The notification system can also notify the user of the most appropriate course of action based on the user's past health data. For example, the notification system can determine the content of the notification to ensure a rapid response based on the user's past health data. This allows for more appropriate notifications by referring to the user's past health data.

[0058] The notification system can reflect the user's current health status in real time during an emergency. For example, the notification system can notify the appropriate medical institution based on the user's current body temperature. For example, the notification system can notify the user of the optimal course of action based on the user's current blood pressure. For example, the notification system can determine the content of the notification for a rapid response based on the user's current heart rate. This allows for more appropriate notifications by reflecting the user's current health status in real time.

[0059] The notification system can determine the most effective notification content based on the user's living environment during an emergency. For example, if the user lives alone, the notification system will send a notification that requires a quick response. If the user lives with family, the notification system can also send a notification that requires coordination with family members. The notification system can also send a notification that describes the most appropriate response method tailored to the user's living environment. This allows for more appropriate notifications by considering the user's living environment.

[0060] The notification system can strengthen coordination with the user's family and medical institutions during emergencies. For example, the notification system can provide the user's family with a detailed explanation of the emergency notification. The notification system can also provide the user's medical institutions with notification information to facilitate a rapid response. The notification system can also optimize emergency responses by strengthening coordination with the user's family and medical institutions. This strengthens coordination with the user's family and medical institutions, enabling more appropriate notifications.

[0061] The management department can select the optimal management method by referring to the user's past spending history when managing finances. For example, the management department can set an appropriate budget based on the user's past spending history. For example, the management department can also suggest ways to reduce unnecessary spending based on the user's past spending history. For example, the management department can also formulate an optimal spending plan based on the user's past spending history. In this way, by referring to the user's past spending history, more appropriate financial management can be provided.

[0062] The management department can select the most effective financial management method based on the user's living environment. For example, if the user lives alone, the management department will select a method that emphasizes managing living expenses. If the user lives with family, the management department can also select a management method that assumes cooperation with family. The management department can also, for example, develop an optimal spending plan tailored to the user's living environment. In this way, by considering the user's living environment, it is possible to provide more appropriate financial management.

[0063] The integration unit can select the optimal integration method by referring to the user's past usage history when integrating with a designated driver service. For example, the integration unit can select an appropriate designated driver service based on the user's past usage history. For example, the integration unit can also propose the optimal integration method based on the user's past usage history. For example, the integration unit can select an efficient integration method based on the user's past usage history. This makes it possible to perform more appropriate integration by referring to the user's past usage history.

[0064] The collaboration unit can select the most effective collaboration method based on the user's living environment when collaborating with a designated driver service. For example, if the user lives alone, the collaboration unit will select a collaboration method that requires a quick response. If the user lives with family, the collaboration unit can also select a collaboration method that requires cooperation with family. For example, the collaboration unit can also select the optimal collaboration method to suit the user's living environment. This allows for more appropriate collaboration by considering the user's living environment.

[0065] The hygiene management department can select the optimal management method when managing the hygiene of living spaces by referring to the user's past hygiene management history. For example, the hygiene management department can set an appropriate cleaning schedule based on the user's past hygiene management history. For example, the hygiene management department can also propose ways to reduce unnecessary cleaning based on the user's past hygiene management history. For example, the hygiene management department can formulate an optimal hygiene management plan based on the user's past hygiene management history. In this way, more appropriate hygiene management becomes possible by referring to the user's past hygiene management history.

[0066] The Hygiene Management Department can select the most effective hygiene management method based on the user's living environment when managing hygiene in living spaces. For example, if the user lives alone, the Hygiene Management Department will select a hygiene management method that suits their living environment. If the user lives with family, the Hygiene Management Department can also select a hygiene management method that takes into account cooperation with family members. For example, the Hygiene Management Department can also formulate an optimal hygiene management plan tailored to the user's living environment. This allows for more appropriate hygiene management by considering the user's living environment.

[0067] The service provider can select the optimal service delivery method by referring to the user's past entertainment history when providing entertainment. For example, the service provider can provide appropriate entertainment based on the user's past entertainment history. For example, the service provider can also suggest ways to reduce unnecessary entertainment based on the user's past entertainment history. For example, the service provider can also formulate an optimal entertainment plan based on the user's past entertainment history. This makes it possible to provide more appropriate entertainment by referring to the user's past entertainment history. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The service provider can select the most effective method of providing entertainment based on the user's living environment. For example, if the user lives alone, the service provider can provide entertainment tailored to their living environment. If the user lives with family, the service provider can also provide entertainment that requires cooperation with family members. For example, the service provider can also formulate an optimal entertainment plan tailored to the user's living environment. This makes it possible to provide more appropriate entertainment by considering the user's living environment.

[0069] The reporting department can determine the optimal reporting content by referring to the user's past care history when reporting on the care situation. For example, the reporting department can determine appropriate reporting content based on the user's past care history. For example, the reporting department can also propose methods to reduce unnecessary reporting based on the user's past care history. For example, the reporting department can also develop an optimal reporting plan based on the user's past care history. This makes it possible to provide more appropriate reports by referring to the user's past care history.

[0070] The reporting unit can strengthen collaboration with the user's family and caregivers when reporting on the care situation. For example, the reporting unit can explain the details of the care situation report to the user's family. For example, the reporting unit can also give specific instructions to the user's caregivers regarding the content of the care situation report. For example, the reporting unit can strengthen collaboration with the user's family and caregivers and optimize the reporting of the care situation. This strengthens collaboration with the user's family and caregivers, enabling more appropriate reporting.

[0071] The proxy service can select the optimal proxy method by referring to the user's past question history when asking questions or acting as a conversation partner. For example, the proxy service can provide appropriate answers based on the user's past question history. For example, the proxy service can also suggest ways to reduce unnecessary questions based on the user's past question history. For example, the proxy service can also formulate an optimal proxy plan based on the user's past question history. This allows for more appropriate proxy service by referring to the user's past question history.

[0072] The proxy service can select the most effective proxy method based on the user's living environment when asking questions or acting as a conversation partner. For example, if the user lives alone, the proxy service will ask questions and act as a conversation partner in a way that suits their living environment. If the user lives with family, the proxy service can also ask questions and act as a conversation partner in cooperation with family members. The proxy service can also develop an optimal proxy plan tailored to the user's living environment. This allows for more appropriate proxy services by taking the user's living environment into consideration.

[0073] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0074] The management department can select the optimal management method by referring to the user's past spending history. For example, it can set an appropriate budget based on the user's past spending history. It can also suggest ways to reduce unnecessary spending based on the user's past spending history. Furthermore, it can develop an optimal spending plan based on the user's past spending history. In this way, by referring to the user's past spending history, it is possible to provide more appropriate financial management.

[0075] The integration unit can select the optimal integration method when integrating with designated driver services by referring to the user's past usage history. For example, it can select an appropriate designated driver service based on the user's past usage history. It can also propose the optimal integration method based on the user's past usage history. Furthermore, it can select an efficient integration method based on the user's past usage history. As a result, referencing the user's past usage history enables more appropriate integration.

[0076] The hygiene management department can select the optimal hygiene management method when managing the hygiene of living spaces by referring to the user's past hygiene management history. For example, it can set an appropriate cleaning schedule based on the user's past hygiene management history. It can also suggest ways to reduce unnecessary cleaning based on the user's past hygiene management history. Furthermore, it can formulate an optimal hygiene management plan based on the user's past hygiene management history. In this way, more appropriate hygiene management becomes possible by referring to the user's past hygiene management history.

[0077] The service provider can select the optimal service delivery method by referring to the user's past entertainment history when providing entertainment. For example, it can provide appropriate entertainment based on the user's past entertainment history. It can also suggest ways to reduce unnecessary entertainment based on the user's past entertainment history. Furthermore, it can formulate an optimal entertainment plan based on the user's past entertainment history. In this way, by referring to the user's past entertainment history, it becomes possible to provide more appropriate entertainment.

[0078] The reporting department can determine the optimal reporting content when reporting caregiving situations by referring to the user's past caregiving history. For example, it can determine appropriate reporting content based on the user's past caregiving history. It can also suggest ways to reduce unnecessary reporting based on the user's past caregiving history. Furthermore, it can develop an optimal reporting plan based on the user's past caregiving history. As a result, more appropriate reporting becomes possible by referring to the user's past caregiving history.

[0079] The following briefly describes the processing flow for example form 1.

[0080] Step 1: The planning department develops a care plan. The planning department creates an optimal care plan based on information such as the user's health condition, living environment, and past care plans. For example, if the user's health condition deteriorates, the planning department can develop a corresponding care plan and instruct necessary medical measures. It is also possible to use AI to analyze the user's health condition and living environment and develop an optimal care plan. Step 2: The monitoring unit monitors the user's health status based on the care plan developed by the planning unit. The monitoring unit monitors the user's health data, such as body temperature, blood pressure, and heart rate, in real time. If an abnormality is detected, it can instruct medical action. It can also use AI to analyze health data and detect abnormalities. Step 3: The instruction unit directs medical actions based on the health status monitored by the monitoring unit. If the user's body temperature rises rapidly, the instruction unit can contact a medical institution and direct appropriate action. It can also use AI to analyze health data and direct appropriate medical actions. Step 4: The implementation monitoring unit monitors the implementation status of the care plan based on the medical measures instructed by the instruction unit. The implementation monitoring unit monitors whether the care plan is being implemented appropriately. If a problem occurs, it can issue a warning or notify external parties. It can also use AI to monitor the implementation status of the care plan and detect problems. Step 5: The notification unit issues emergency alerts and makes external notifications based on the implementation status of the care plan monitored by the execution monitoring unit. If the user is not following the care plan, the notification unit can alert the caregiver and contact family or medical institutions as needed. It can also use AI to analyze the emergency situation and make appropriate notifications.

[0081] (Example of form 2) The care support system according to an embodiment of the present invention is a system for advancing the use of AI in care support. While AI has already been introduced into some areas of care support systems, such as the creation of care plans, human intervention is still required for tasks that connect these processes. By integrating these processes and building a system in which AI automatically makes decisions and repeatedly learns while considering the overall workflow, the aim is to contribute to solving Japan's increasingly serious aging problem. For example, in the care support system, the AI ​​formulates a care plan. Next, the AI ​​monitors the user's health condition and instructs medical measures as needed. Furthermore, the care support system monitors the implementation status of the care plan and issues warnings or notifies external parties in case of emergencies. In addition, the care support system grasps all information related to care and takes the following actions. This includes managing finances, coordinating with designated driver services, managing hygiene in the living space, providing entertainment, reporting to family members about the care situation, and acting as a question answerer or conversational partner. For example, in the care support system, the AI ​​formulates a care plan. In this process, the AI ​​creates an optimal care plan based on information such as the user's health condition, living environment, and past care plans. For example, if a user's health deteriorates, the AI ​​will create a corresponding care plan and instruct necessary medical actions. Next, the care support system uses AI to monitor the user's health. The AI ​​monitors the user's health data, such as body temperature, blood pressure, and heart rate, in real time, and instructs medical actions if an abnormality is detected. For example, if a user's body temperature rises rapidly, the AI ​​will contact a medical institution and instruct appropriate action. Furthermore, the care support system uses AI to monitor the implementation of the care plan. The AI ​​monitors whether the care plan is being properly implemented and issues warnings or reports to external parties if problems occur. For example, if a user is not following the care plan, the AI ​​will alert the caregiver and contact family or medical institutions as needed. The care support system also grasps all information related to care and takes the following actions. This includes managing finances, coordinating with designated driver services, managing the hygiene of the living space, providing entertainment, reporting to family members about the care situation, and acting as a question answerer or conversational partner. For example, the care support system manages the user's finances and automatically makes necessary payments.Furthermore, the care support system will integrate with a designated driver service to assist users with their transportation. In addition, the care support system will manage the hygiene of the living space, providing a comfortable environment for users. In this way, the care support system will improve the efficiency and quality of caregiving operations, contributing to the resolution of Japan's increasingly serious aging population problem. Thus, the care support system will improve the efficiency and quality of caregiving operations.

[0082] The care support system according to this embodiment comprises a planning unit, a monitoring unit, an instruction unit, an execution monitoring unit, and a notification unit. The planning unit plans a care plan. The planning unit creates an optimal care plan based on information such as the user's health condition, living environment, and past care plans. For example, if the user's health condition deteriorates, the planning unit can plan a corresponding care plan and instruct necessary medical measures. The planning unit can also use AI to analyze the user's health condition and living environment and plan an optimal care plan. The monitoring unit monitors the health condition based on the care plan planned by the planning unit. The monitoring unit monitors health data such as the user's body temperature, blood pressure, and heart rate in real time. For example, if an abnormality is detected, the monitoring unit can instruct medical measures. The monitoring unit can also use AI to analyze health data and detect abnormalities. The instruction unit instructs medical measures based on the health condition monitored by the monitoring unit. The instruction unit can, for example, contact a medical institution and instruct appropriate action if the user's body temperature rises rapidly. The instruction unit can also, for example, use AI to analyze health data and instruct appropriate medical measures. The execution monitoring unit monitors the implementation status of the care plan based on the medical measures instructed by the instruction unit. The execution monitoring unit monitors, for example, whether the care plan is being implemented appropriately. The execution monitoring unit can, for example, issue warnings or notify external parties if a problem occurs. The execution monitoring unit can also, for example, use AI to monitor the implementation status of the care plan and detect problems. The notification unit issues warnings or notifies external parties in emergencies based on the implementation status of the care plan monitored by the execution monitoring unit. The notification unit can, for example, alert caregivers if the user is not following the care plan and contact family members or medical institutions as needed. The notification unit can also, for example, use AI to analyze the situation in an emergency and make appropriate notifications. As a result, the care support system according to this embodiment integrates everything from care plan planning to implementation, monitoring, and notification, and the AI ​​automatically makes decisions and repeatedly learns, thereby improving the efficiency and quality of care support.

[0083] The planning department develops care plans. Based on information such as the user's health status, living environment, and past care plans, the planning department creates the optimal care plan. Specifically, data on the user's health status is collected from regular health checkup results and daily health monitoring data. This includes vital signs such as blood pressure, body temperature, heart rate, and blood glucose levels. Information on the living environment includes the user's residence, family structure, and daily activity patterns. Past care plans include data on the content and effectiveness of care provided to date, as well as the user's responses. The planning department comprehensively analyzes this information to assess the user's current condition and future risks. For example, if the user's health status deteriorates, the planning department can develop a corresponding care plan and instruct necessary medical measures. The planning department can also use AI to analyze the user's health status and living environment and develop the optimal care plan. By learning from past data and building pattern recognition and predictive models, the AI ​​can provide customized care plans for each user. For example, the AI ​​can analyze the user's health data and detect specific symptoms or risk factors early. This allows the planning department to quickly develop appropriate care plans tailored to the user's health condition, thereby improving the quality of care support. Furthermore, the planning department can share the contents of the care plan through communication with the user and their family, and reflect the user's intentions and wishes. This can increase user satisfaction and improve the implementation rate of care plans.

[0084] The monitoring unit monitors the user's health status based on the care plan developed by the planning unit. The monitoring unit monitors health data in real time, such as the user's body temperature, blood pressure, and heart rate. Specifically, this data is continuously collected through wearable devices worn by the user and home health monitoring devices. These devices transmit data to a central database via Bluetooth or Wi-Fi, allowing the monitoring unit to access it in real time. The monitoring unit can, for example, instruct medical action if an abnormality is detected. For instance, if a user's body temperature rises sharply, the monitoring unit immediately issues an alert and contacts a medical institution. The monitoring unit can also use AI to analyze health data and detect abnormalities. The AI ​​learns patterns in normal health data and uses algorithms to quickly identify abnormal data. This allows the monitoring unit to constantly monitor the user's health status and respond quickly when an abnormality occurs. Furthermore, the monitoring unit can analyze trends in the user's health status based on past health data and assess long-term risks. For example, it can analyze blood pressure data from the past few months to assess whether the user is at risk of hypertension. This allows the monitoring department to develop preventative care plans and support users in maintaining their health.

[0085] The command unit directs medical actions based on the health status monitored by the monitoring unit. For example, if a user's body temperature rises rapidly, the command unit can contact a medical institution and instruct appropriate action. Specifically, the command unit analyzes the user's health data in real time, and if an abnormality is detected, it instructs action according to a pre-configured protocol. For example, if the body temperature exceeds a certain threshold, it instructs cooling measures and contacts a medical institution as necessary. The command unit can also use AI to analyze health data and instruct appropriate medical actions. Based on past data, the AI ​​can learn the optimal response to specific symptoms and risk factors and issue instructions quickly and accurately. For example, if a user's heart rate is abnormally high, the AI ​​will instruct rest and contact a medical institution as necessary. Furthermore, the command unit can share the details of medical actions through communication with the user and their family, and reflect the user's intentions and wishes. This can increase the user's sense of security and improve the rate of medical action implementation. In addition, the command unit can strengthen cooperation with medical institutions and care facilities to achieve quick and appropriate responses. For example, by sharing data with medical institutions and providing real-time information on the user's health status, medical institutions can respond quickly. This allows the command center to promptly issue appropriate medical instructions based on the user's health condition, thereby improving the quality of care support.

[0086] The Execution Monitoring Unit monitors the implementation status of care plans based on medical procedures instructed by the Instruction Unit. For example, the Execution Monitoring Unit monitors whether the care plan is being implemented appropriately. Specifically, the Execution Monitoring Unit conducts regular checks and monitoring to ensure that users and caregivers are acting in accordance with the care plan. For example, it verifies whether users are taking prescribed medications at the appropriate time and whether they are performing regular exercise and rehabilitation. The Execution Monitoring Unit can also issue warnings and notify external parties if problems occur. For example, if a user forgets to take their medication, it will issue an alert and notify the caregiver. Furthermore, the Execution Monitoring Unit can use AI to monitor the implementation status of care plans and detect problems. The AI ​​analyzes user behavior data and can respond quickly if it detects deviations from the care plan or abnormal behavior. For example, if a user neglects regular exercise, it will issue an alert and notify the caregiver. The Execution Monitoring Unit can also share the status of care plan implementation through communication with users and caregivers and respond quickly if problems occur. This allows the Execution Monitoring Unit to constantly understand the status of care plan implementation and respond quickly when problems arise. Furthermore, the implementation monitoring unit can accumulate data on the implementation status of care plans and use it to improve future care plans. This allows the implementation monitoring unit to monitor the implementation status of care plans and respond quickly when problems arise, thereby improving the quality of care support.

[0087] The notification unit issues emergency alerts and makes external notifications based on the implementation status of care plans monitored by the execution monitoring unit. For example, if a user is not following the care plan, the notification unit can alert the caregiver and contact family or medical institutions as needed. Specifically, the notification unit receives alerts from the execution monitoring unit and instructs the user or caregiver to take appropriate action. For example, if a user forgets to take their medication, the notification unit will notify the caregiver and contact family or medical institutions as needed. The notification unit can also use AI to analyze emergency situations and make appropriate notifications. Based on past data, the AI ​​can learn emergency patterns and respond quickly and accurately. For example, if a user's health condition rapidly deteriorates, the AI ​​will analyze the situation and instruct appropriate action. The notification unit can also share emergency situations and respond quickly through communication with users and caregivers. This allows the notification unit to respond quickly to emergencies and ensure the user's safety. Furthermore, the notification unit can accumulate data on emergency situations and use it to improve future responses. This allows the reporting department to respond quickly and appropriately to emergency situations, ensuring user safety and thereby improving the quality of care support.

[0088] The management department can manage finances. For example, the management department can record user expenses. The management department can also manage user budgets. For example, the management department can automate payments. For example, the management department can use AI to record user expenses and manage budgets. By automating financial management, the burden on users can be reduced, and the efficiency of care support can be improved.

[0089] The integration unit can integrate with designated driver services. For example, the integration unit can support the user's transportation. The integration unit can also automate the use of designated driver services. For example, the integration unit can use AI to integrate with designated driver services. This automates the integration with designated driver services, thereby supporting the user's transportation and improving the efficiency of care support.

[0090] The Hygiene Management Department can manage the hygiene of living spaces. For example, the Hygiene Management Department can clean the user's living space. For example, the Hygiene Management Department can also perform disinfection. For example, the Hygiene Management Department can use AI to manage the hygiene of living spaces. By automating the hygiene management of living spaces, it is possible to provide a comfortable environment for users and improve the efficiency of care support.

[0091] The service provider can provide entertainment. For example, the service provider can provide users with television viewing. For example, the service provider can provide users with games. For example, the service provider can provide users with reading material. For example, the service provider can use AI to provide users with the most suitable entertainment. By automating the provision of entertainment, it is possible to improve the quality of life for users and increase the efficiency of care support.

[0092] The reporting unit can provide families with reports on the care situation. For example, the reporting unit can report the user's health status to the family. The reporting unit can also report to the family on the progress of the user's care plan. The reporting unit can also use AI to report on the care situation. By automating the reporting of the care situation, information can be provided to families more smoothly, and the efficiency of care support can be improved.

[0093] The proxy unit can answer questions about unclear points and act as a conversational partner. For example, the proxy unit can answer questions about the user's medical care. For example, the proxy unit can also answer questions about the user's daily life. For example, the proxy unit can act as a conversational partner for the user. For example, the proxy unit can use AI to answer questions and act as a conversational partner for the user. By automating questioning and conversational support, it is possible to reduce user anxiety and improve the efficiency of care support.

[0094] The planning unit can estimate the user's emotions and adjust the content of the care plan based on the estimated emotions. For example, if the user is feeling stressed, the planning unit can create a care plan that includes many relaxing activities. For example, if the user is feeling lonely, the planning unit can also create a care plan that includes many social activities. For example, if the user is tired, the planning unit can also create a care plan that emphasizes rest. In this way, by adjusting the content of the care plan based on the user's emotions, a more appropriate care plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The planning department can analyze the results of past care plan implementations and develop the most effective care plan. For example, based on the results of past care plan implementations, the planning department can develop a care plan that prioritizes activities that users enjoyed. For example, based on the results of past care plan implementations, the planning department can also develop a care plan that includes many activities that were highly effective. For example, based on the results of past care plan implementations, the planning department can develop a care plan that avoids activities that were prone to causing problems. In this way, by analyzing the results of past care plan implementations, it is possible to provide more effective care plans.

[0096] The planning department can customize care plans based on the user's living environment and family structure. For example, if the user lives alone, the planning department can create a care plan that increases the frequency of home care visits. If the user lives with family, the planning department can also create a care plan that assumes cooperation with family members. For example, the planning department can create a care plan that includes a balanced mix of indoor and outdoor activities, tailored to the user's living environment. This allows for the provision of more individualized care plans by considering the user's living environment and family structure.

[0097] The planning unit can estimate the user's emotions and determine the priorities of the care plan based on those estimated emotions. For example, if the user is feeling anxious, the planning unit can create a care plan that prioritizes activities that provide a sense of security. For example, if the user is seeking enjoyment, the planning unit can also create a care plan that prioritizes recreational activities. For example, if the user values ​​health, the planning unit can also create a care plan that prioritizes health maintenance activities. By determining the priorities of the care plan based on the user's emotions, a more appropriate care plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The planning unit can create the most effective care plan based on the user's geographical location information. For example, if the user lives in an urban area, the planning unit can create a care plan that utilizes nearby facilities. If the user lives in a suburban area, the planning unit can also create a care plan that takes transportation into consideration. If the user lives in a remote area, the planning unit can also create a care plan that includes online activities. This allows for the provision of more appropriate care plans by considering the user's geographical location information.

[0099] The planning department can analyze a user's social media activity when creating a care plan and develop a relevant care plan. For example, the planning department can develop a care plan that includes activities the user has shown interest in on social media. The planning department can also develop a care plan that includes activities the user engages in with friends on social media. For example, the planning department can develop a care plan based on health information the user shares on social media. This allows for the provision of more appropriate care plans by analyzing the user's social media activity.

[0100] The monitoring unit can estimate the user's emotions and adjust the health monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit can perform health monitoring in a relaxing environment. For example, if the user is anxious, the monitoring unit can perform health monitoring in a way that provides reassurance. For example, if the user is relaxed, the monitoring unit can perform monitoring that collects detailed health data. This allows for more appropriate health monitoring by adjusting the health monitoring method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The monitoring unit can detect abnormal values ​​in health data in real time and respond quickly. For example, if a user's body temperature rises rapidly, the monitoring unit can immediately contact a medical institution. For example, if a user's blood pressure shows an abnormal value, the monitoring unit can quickly instruct medical treatment. For example, if a user's heart rate shows an abnormal value, the monitoring unit can immediately notify a caregiver. This enables rapid response by detecting abnormal values ​​in health data in real time.

[0102] The monitoring unit can predict changes in a user's health status by referring to their past health data. For example, the monitoring unit can predict seasonal changes in health status from past health data. For example, the monitoring unit can also predict changes in health status after a specific activity from past health data. For example, the monitoring unit can also predict long-term trends in health status from past health data. This allows for the prediction of changes in health status and appropriate responses by referring to past health data.

[0103] The monitoring unit can estimate the user's emotions and adjust the frequency of health monitoring based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit will monitor their health more frequently. For example, if the user is relaxed, the monitoring unit can also monitor their health regularly. For example, if the user is feeling stressed, the monitoring unit can implement monitoring methods to reduce stress. This allows for more appropriate health monitoring by adjusting the frequency of health monitoring based on the user'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.

[0104] The monitoring unit can set the most effective monitoring timing based on the user's lifestyle when monitoring their health status. For example, the monitoring unit can monitor the user's health status according to their wake-up time. For example, the monitoring unit can monitor the user's health status according to their meal times. For example, the monitoring unit can monitor the user's health status according to their bedtime. This allows for more appropriate health monitoring by taking the user's lifestyle into consideration.

[0105] The monitoring unit can customize its monitoring content when monitoring the user's health status, taking into account the user's diet and exercise habits. For example, the monitoring unit can monitor blood glucose levels based on the user's diet. For example, the monitoring unit can also monitor heart rate based on the user's exercise habits. For example, the monitoring unit can perform comprehensive health monitoring, taking into account the balance between the user's diet and exercise. This allows for more appropriate health monitoring by considering the user's diet and exercise habits.

[0106] The instruction unit can estimate the user's emotions and adjust the content of medical treatment instructions based on the estimated emotions. For example, if the user is feeling anxious, the instruction unit may instruct medical treatment that provides reassurance. For example, if the user is relaxed, the instruction unit may instruct medical treatment that includes detailed explanations. For example, if the user is stressed, the instruction unit may instruct medical treatment to reduce stress. This allows for the provision of more appropriate medical treatment by adjusting the content of medical treatment instructions based on the user'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.

[0107] The instruction unit can refer to the user's past medical history to provide optimal instructions when issuing medical treatment orders. For example, the instruction unit can recommend appropriate medical treatment based on the user's past allergy information. For example, the instruction unit can recommend the optimal treatment method based on the user's past medical history. For example, the instruction unit can recommend appropriate medications based on the user's past medication use history. This allows for the provision of more appropriate medical treatment by referring to the user's past medical history.

[0108] The instruction unit can reflect the user's current health status in real time when issuing medical treatment instructions. For example, the instruction unit can instruct appropriate medical treatment based on the user's current body temperature. For example, the instruction unit can instruct the optimal treatment method based on the user's current blood pressure. For example, the instruction unit can instruct appropriate medication based on the user's current heart rate. This allows for the provision of more appropriate medical treatment by reflecting the user's current health status in real time.

[0109] The instruction unit can estimate the user's emotions and adjust the timing of medical treatment instructions based on the estimated emotions. For example, if the user is feeling anxious, the instruction unit may instruct medical treatment at a time that provides reassurance. For example, if the user is relaxed, the instruction unit may instruct medical treatment at a time that includes detailed explanations. For example, if the user is feeling stressed, the instruction unit may instruct medical treatment at a time that helps reduce stress. This allows for the provision of more appropriate medical treatment by adjusting the timing of medical treatment instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The instruction unit can provide the most effective instructions for medical treatment based on the user's living environment. For example, if the user lives alone, the instruction unit may instruct an increase in the frequency of home visits. If the user lives with family, the instruction unit can also instruct medical treatment that requires cooperation with family members. The instruction unit can also provide instructions for a balanced mix of indoor and outdoor medical treatment, tailored to the user's living environment. This allows for the provision of more appropriate medical treatment by considering the user's living environment.

[0111] The instruction unit can strengthen collaboration with the user's family and caregivers when issuing instructions for medical procedures. For example, the instruction unit can explain the details of the medical procedure to the user's family. For example, the instruction unit can also provide specific instructions on the steps of the medical procedure to the user's caregivers. For example, the instruction unit can strengthen collaboration with the user's family and caregivers and maximize the effectiveness of the medical procedure. This strengthens collaboration with the user's family and caregivers, enabling the provision of more appropriate medical procedures.

[0112] The execution monitoring unit can estimate the user's emotions and adjust the method of monitoring the care plan's implementation status based on the estimated user emotions. For example, if the user is feeling stressed, the execution monitoring unit will monitor the care plan's implementation status in a relaxing environment. For example, if the user is feeling anxious, the execution monitoring unit can also monitor the care plan's implementation status in a way that provides reassurance. For example, if the user is relaxed, the execution monitoring unit can also perform monitoring to collect detailed implementation status. This allows for more appropriate care plan implementation monitoring by adjusting the method of monitoring the care plan's implementation status based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The implementation monitoring unit can monitor the status of care plan implementation in real time and respond quickly if problems occur. For example, the implementation monitoring unit can monitor the status of care plan implementation in real time and immediately notify caregivers if an abnormality occurs. For example, the implementation monitoring unit can monitor the status of care plan implementation in real time and quickly contact medical institutions if problems occur. For example, the implementation monitoring unit can monitor the status of care plan implementation in real time and quickly notify families if problems occur. This enables a rapid response by monitoring the status of care plan implementation in real time.

[0114] The implementation monitoring unit can identify areas for improvement by comparing the implementation status of care plans with past data. For example, the implementation monitoring unit can identify activities that were highly effective by comparing the implementation status of care plans with past data. For example, the implementation monitoring unit can also identify activities that were prone to problems by comparing the implementation status of care plans with past data. For example, the implementation monitoring unit can identify areas for improvement by comparing the implementation status of care plans with past data and reflect them in the next care plan. In this way, by comparing the implementation status of care plans with past data, areas for improvement can be identified and reflected in the next care plan.

[0115] The execution monitoring unit can estimate the user's emotions and adjust the frequency of monitoring the care plan's implementation status based on the estimated emotions. For example, if the user is feeling anxious, the execution monitoring unit will monitor the care plan's implementation status more frequently. For example, if the user is relaxed, the execution monitoring unit can also monitor the care plan's implementation status periodically. For example, if the user is feeling stressed, the execution monitoring unit can implement monitoring methods to reduce stress. By adjusting the frequency of monitoring the care plan's implementation status based on the user's emotions, more appropriate care plan implementation monitoring can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The execution monitoring unit can set the most effective monitoring timing based on the user's daily rhythm when monitoring the implementation status of the care plan. For example, the execution monitoring unit can monitor the implementation status of the care plan in accordance with the user's wake-up time. For example, the execution monitoring unit can also monitor the implementation status of the care plan in accordance with the user's meal times. For example, the execution monitoring unit can also monitor the implementation status of the care plan in accordance with the user's bedtime. This allows for more appropriate care plan implementation monitoring by taking the user's daily rhythm into consideration.

[0117] The implementation monitoring unit can strengthen collaboration with the user's family and caregivers when monitoring the implementation status of the care plan. For example, the implementation monitoring unit can report the implementation status of the care plan in detail to the user's family. For example, the implementation monitoring unit can also give specific instructions to the user's caregivers regarding the implementation status of the care plan. For example, the implementation monitoring unit can also optimize the implementation status of the care plan by strengthening collaboration with the user's family and caregivers. This strengthens collaboration with the user's family and caregivers, enabling more appropriate monitoring of the care plan's implementation.

[0118] The notification unit can estimate the user's emotions and adjust the content of emergency notifications based on those emotions. For example, if the user is feeling anxious, the notification unit will send a notification that provides reassurance. If the user is relaxed, the notification unit may also send a notification that includes detailed explanations. If the user is stressed, the notification unit may also send a notification that helps reduce stress. By adjusting the content of emergency notifications based on the user's emotions, more appropriate notifications can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The notification system can determine the most appropriate notification content in emergency situations by referring to the user's past health data. For example, the notification system can notify the appropriate medical institution based on the user's past health data. The notification system can also notify the user of the most appropriate course of action based on the user's past health data. For example, the notification system can determine the content of the notification to ensure a rapid response based on the user's past health data. This allows for more appropriate notifications by referring to the user's past health data.

[0120] The notification system can reflect the user's current health status in real time during an emergency. For example, the notification system can notify the appropriate medical institution based on the user's current body temperature. For example, the notification system can notify the user of the optimal course of action based on the user's current blood pressure. For example, the notification system can determine the content of the notification for a rapid response based on the user's current heart rate. This allows for more appropriate notifications by reflecting the user's current health status in real time.

[0121] The notification unit can estimate the user's emotions and adjust the timing of emergency notifications based on those emotions. For example, if the user is feeling anxious, the notification unit can make a notification at a time that provides reassurance. For example, if the user is relaxed, the notification unit can make a notification at a time that includes detailed explanations. For example, if the user is stressed, the notification unit can make a notification at a time that helps reduce stress. By adjusting the timing of emergency notifications based on the user's emotions, more appropriate notifications can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The notification system can determine the most effective notification content based on the user's living environment during an emergency. For example, if the user lives alone, the notification system will send a notification that requires a quick response. If the user lives with family, the notification system can also send a notification that requires coordination with family members. The notification system can also send a notification that describes the most appropriate response method tailored to the user's living environment. This allows for more appropriate notifications by considering the user's living environment.

[0123] The notification system can strengthen coordination with the user's family and medical institutions during emergencies. For example, the notification system can provide the user's family with a detailed explanation of the emergency notification. The notification system can also provide the user's medical institutions with notification information to facilitate a rapid response. The notification system can also optimize emergency responses by strengthening coordination with the user's family and medical institutions. This strengthens coordination with the user's family and medical institutions, enabling more appropriate notifications.

[0124] The management unit can estimate the user's emotions and adjust the method of financial management based on the estimated emotions. For example, if the user is feeling anxious, the management unit will manage the user's money in a way that provides reassurance. For example, if the user is relaxed, the management unit may manage the user's money in a way that includes detailed explanations. For example, if the user is stressed, the management unit may manage the user's money in a way that reduces stress. This allows for more appropriate financial management by adjusting the method of financial management based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The management department can select the optimal management method by referring to the user's past spending history when managing finances. For example, the management department can set an appropriate budget based on the user's past spending history. For example, the management department can also suggest ways to reduce unnecessary spending based on the user's past spending history. For example, the management department can also formulate an optimal spending plan based on the user's past spending history. In this way, by referring to the user's past spending history, more appropriate financial management can be provided.

[0126] The management unit can estimate the user's emotions and determine the priority of financial management based on the estimated emotions. For example, if the user is feeling anxious, the management unit will prioritize financial management based on providing reassurance. If the user is relaxed, the management unit may also prioritize financial management based on providing detailed explanations. If the user is stressed, the management unit may also prioritize financial management based on stress reduction. This allows for more appropriate financial management by determining the priority of financial management based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The management department can select the most effective financial management method based on the user's living environment. For example, if the user lives alone, the management department will select a method that emphasizes managing living expenses. If the user lives with family, the management department can also select a management method that assumes cooperation with family. The management department can also, for example, develop an optimal spending plan tailored to the user's living environment. In this way, by considering the user's living environment, it is possible to provide more appropriate financial management.

[0128] The integration unit can estimate the user's emotions and adjust the method of integrating with the designated driver service based on the estimated emotions. For example, if the user is feeling anxious, the integration unit will integrate with the designated driver service in a way that provides reassurance. For example, if the user is relaxed, the integration unit may integrate with the designated driver service in a way that includes detailed explanations. For example, if the user is feeling stressed, the integration unit may integrate with the designated driver service in a way that reduces stress. By adjusting the method of integrating with the designated driver service based on the user's emotions, more appropriate integration becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The integration unit can select the optimal integration method by referring to the user's past usage history when integrating with a designated driver service. For example, the integration unit can select an appropriate designated driver service based on the user's past usage history. For example, the integration unit can also propose the optimal integration method based on the user's past usage history. For example, the integration unit can select an efficient integration method based on the user's past usage history. This makes it possible to perform more appropriate integration by referring to the user's past usage history.

[0130] The coordination unit can estimate the user's emotions and adjust the frequency of coordination with the designated driver service based on the estimated emotions. For example, if the user is feeling anxious, the coordination unit will coordinate the designated driver service more frequently. For example, if the user is relaxed, the coordination unit can coordinate the designated driver service regularly. For example, if the user is stressed, the coordination unit can coordinate the designated driver service at a frequency that helps reduce stress. By adjusting the frequency of coordination with the designated driver service based on the user's emotions, more appropriate coordination becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0131] The collaboration unit can select the most effective collaboration method based on the user's living environment when collaborating with a designated driver service. For example, if the user lives alone, the collaboration unit will select a collaboration method that requires a quick response. If the user lives with family, the collaboration unit can also select a collaboration method that requires cooperation with family. For example, the collaboration unit can also select the optimal collaboration method to suit the user's living environment. This allows for more appropriate collaboration by considering the user's living environment.

[0132] The hygiene management department can estimate the user's emotions and adjust the hygiene management methods for the living space based on the estimated emotions. For example, if the user is feeling anxious, the hygiene management department can manage the hygiene of the living space in a way that provides a sense of security. For example, if the user is relaxed, the hygiene management department can manage the hygiene of the living space in a way that includes detailed explanations. For example, if the user is feeling stressed, the hygiene management department can manage the hygiene of the living space in a way that reduces stress. This allows for more appropriate hygiene management by adjusting the hygiene management methods for the living space based on the user'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.

[0133] The hygiene management department can select the optimal management method when managing the hygiene of living spaces by referring to the user's past hygiene management history. For example, the hygiene management department can set an appropriate cleaning schedule based on the user's past hygiene management history. For example, the hygiene management department can also propose ways to reduce unnecessary cleaning based on the user's past hygiene management history. For example, the hygiene management department can formulate an optimal hygiene management plan based on the user's past hygiene management history. In this way, more appropriate hygiene management becomes possible by referring to the user's past hygiene management history.

[0134] The hygiene management department can estimate the user's emotions and determine the priority of hygiene management in the living space based on the estimated emotions. For example, if the user is feeling anxious, the hygiene management department will prioritize hygiene management in the living space that provides a sense of security. For example, if the user is relaxed, the hygiene management department may also prioritize hygiene management in the living space that includes detailed explanations. For example, if the user is stressed, the hygiene management department may also prioritize hygiene management in the living space that reduces stress. This allows for more appropriate hygiene management by determining the priority of hygiene management in the living space based on the user'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.

[0135] The Hygiene Management Department can select the most effective hygiene management method based on the user's living environment when managing hygiene in living spaces. For example, if the user lives alone, the Hygiene Management Department will select a hygiene management method that suits their living environment. If the user lives with family, the Hygiene Management Department can also select a hygiene management method that takes into account cooperation with family members. For example, the Hygiene Management Department can also formulate an optimal hygiene management plan tailored to the user's living environment. This allows for more appropriate hygiene management by considering the user's living environment.

[0136] The service provider can estimate the user's emotions and adjust the way entertainment is delivered based on those estimated emotions. For example, if the user is feeling anxious, the service provider can provide entertainment that provides a sense of security. For example, if the user is relaxed, the service provider can also provide entertainment that includes detailed explanations. For example, if the user is stressed, the service provider can also provide entertainment that helps reduce stress. This allows for the delivery of more appropriate entertainment by adjusting the way entertainment is delivered based on the user'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.

[0137] The service provider can select the optimal service delivery method by referring to the user's past entertainment history when providing entertainment. For example, the service provider can provide appropriate entertainment based on the user's past entertainment history. For example, the service provider can also suggest ways to reduce unnecessary entertainment based on the user's past entertainment history. For example, the service provider can also formulate an optimal entertainment plan based on the user's past entertainment history. This makes it possible to provide more appropriate entertainment by referring to the user's past entertainment history. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0138] The service provider can estimate the user's emotions and adjust the frequency of entertainment delivery based on the estimated emotions. For example, if the user is feeling anxious, the service provider will deliver entertainment more frequently. For example, if the user is relaxed, the service provider may deliver entertainment regularly. For example, if the user is stressed, the service provider may deliver entertainment at a frequency that helps reduce stress. By adjusting the frequency of entertainment delivery based on the user's emotions, it becomes possible to deliver more appropriate entertainment. 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.

[0139] The service provider can select the most effective method of providing entertainment based on the user's living environment. For example, if the user lives alone, the service provider can provide entertainment tailored to their living environment. If the user lives with family, the service provider can also provide entertainment that requires cooperation with family members. For example, the service provider can also formulate an optimal entertainment plan tailored to the user's living environment. This makes it possible to provide more appropriate entertainment by considering the user's living environment.

[0140] The reporting unit can estimate the user's emotions and adjust the content of the care situation report based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit will report the care situation in a way that provides reassurance. For example, if the user is relaxed, the reporting unit may also report the care situation with detailed explanations. For example, if the user is stressed, the reporting unit may also report the care situation with content that helps reduce stress. By adjusting the content of the care situation report based on the user's emotions, more appropriate reporting becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0141] The reporting department can determine the optimal reporting content by referring to the user's past care history when reporting on the care situation. For example, the reporting department can determine appropriate reporting content based on the user's past care history. For example, the reporting department can also propose methods to reduce unnecessary reporting based on the user's past care history. For example, the reporting department can also develop an optimal reporting plan based on the user's past care history. This makes it possible to provide more appropriate reports by referring to the user's past care history.

[0142] The reporting unit can estimate the user's emotions and adjust the frequency of care status reports based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit will report care status more frequently. For example, if the user is relaxed, the reporting unit may report care status regularly. For example, if the user is stressed, the reporting unit may report care status at a frequency that helps reduce stress. By adjusting the frequency of care status reports based on the user's emotions, more appropriate reporting becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0143] The reporting unit can strengthen collaboration with the user's family and caregivers when reporting on the care situation. For example, the reporting unit can explain the details of the care situation report to the user's family. For example, the reporting unit can also give specific instructions to the user's caregivers regarding the content of the care situation report. For example, the reporting unit can strengthen collaboration with the user's family and caregivers and optimize the reporting of the care situation. This strengthens collaboration with the user's family and caregivers, enabling more appropriate reporting.

[0144] The proxy unit can estimate the user's emotions and adjust its questioning and conversational approach based on the estimated emotions. For example, if the user is feeling anxious, the proxy unit will ask questions and engage in conversation in a way that provides reassurance. If the user is relaxed, the proxy unit may also ask questions and engage in conversation in a way that includes detailed explanations. If the user is stressed, the proxy unit may also ask questions and engage in conversation in a way that reduces stress. By adjusting the questioning and conversational approach based on the user's emotions, more appropriate proxy service becomes possible. 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.

[0145] The proxy service can select the optimal proxy method by referring to the user's past question history when asking questions or acting as a conversation partner. For example, the proxy service can provide appropriate answers based on the user's past question history. For example, the proxy service can also suggest ways to reduce unnecessary questions based on the user's past question history. For example, the proxy service can also formulate an optimal proxy plan based on the user's past question history. This allows for more appropriate proxy service by referring to the user's past question history.

[0146] The proxy unit can estimate the user's emotions and adjust the frequency of asking questions and acting as a conversational partner based on the estimated emotions. For example, if the user is feeling anxious, the proxy unit will frequently ask questions and act as a conversational partner. For example, if the user is relaxed, the proxy unit can also ask questions and act as a conversational partner regularly. For example, if the user is feeling stressed, the proxy unit can also ask questions and act as a conversational partner at a frequency that helps reduce stress. This allows for more appropriate proxy service by adjusting the frequency of questions and conversational partner actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0147] The proxy service can select the most effective proxy method based on the user's living environment when asking questions or acting as a conversation partner. For example, if the user lives alone, the proxy service will ask questions and act as a conversation partner in a way that suits their living environment. If the user lives with family, the proxy service can also ask questions and act as a conversation partner in cooperation with family members. The proxy service can also develop an optimal proxy plan tailored to the user's living environment. This allows for more appropriate proxy services by taking the user's living environment into consideration.

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

[0149] The planning department can estimate the user's emotions and adjust the content of the care plan based on those estimates. For example, if the user is feeling stressed, a care plan can be developed that includes many relaxing activities. Similarly, if the user is feeling lonely, a care plan can be developed that includes many social activities. Furthermore, if the user is tired, a care plan that emphasizes rest can be developed. By adjusting the content of the care plan based on the user's emotions, a more appropriate care plan can be provided.

[0150] The monitoring unit can estimate the user's emotions and adjust the health monitoring method based on those emotions. For example, if the user is stressed, health monitoring can be performed in a relaxing environment. If the user is anxious, health monitoring can be performed in a way that provides reassurance. Furthermore, if the user is relaxed, monitoring can be performed to collect detailed health data. In this way, by adjusting the health monitoring method based on the user's emotions, more appropriate health monitoring can be provided.

[0151] The instruction unit can estimate the user's emotions and adjust the content of medical treatment instructions based on those emotions. For example, if the user is feeling anxious, it can instruct medical treatment that provides a sense of security. If the user is relaxed, it can instruct medical treatment that includes detailed explanations. Furthermore, if the user is feeling stressed, it can instruct medical treatment to reduce stress. In this way, by adjusting the content of medical treatment instructions based on the user's emotions, more appropriate medical treatment can be provided.

[0152] The execution monitoring unit can estimate the user's emotions and adjust the method of monitoring the care plan's implementation status based on those estimated emotions. For example, if the user is feeling stressed, the system can monitor the care plan's implementation status in a relaxing environment. If the user is feeling anxious, the system can monitor the care plan's implementation status in a way that provides reassurance. Furthermore, if the user is relaxed, the system can implement a monitoring method that collects detailed information about the implementation status. By adjusting the care plan's implementation status monitoring method based on the user's emotions, the system can provide more appropriate care plan implementation monitoring.

[0153] The notification system can estimate the user's emotions and adjust the content of emergency notifications based on those estimates. For example, if the user is feeling anxious, the notification can be reassuring. If the user is relaxed, the notification can include detailed explanations. Furthermore, if the user is stressed, the notification can be designed to alleviate stress. By adjusting the content of emergency notifications based on the user's emotions, more appropriate notifications can be made.

[0154] The management department can select the optimal management method by referring to the user's past spending history. For example, it can set an appropriate budget based on the user's past spending history. It can also suggest ways to reduce unnecessary spending based on the user's past spending history. Furthermore, it can develop an optimal spending plan based on the user's past spending history. In this way, by referring to the user's past spending history, it is possible to provide more appropriate financial management.

[0155] The integration unit can select the optimal integration method when integrating with designated driver services by referring to the user's past usage history. For example, it can select an appropriate designated driver service based on the user's past usage history. It can also propose the optimal integration method based on the user's past usage history. Furthermore, it can select an efficient integration method based on the user's past usage history. As a result, referencing the user's past usage history enables more appropriate integration.

[0156] The hygiene management department can select the optimal hygiene management method when managing the hygiene of living spaces by referring to the user's past hygiene management history. For example, it can set an appropriate cleaning schedule based on the user's past hygiene management history. It can also suggest ways to reduce unnecessary cleaning based on the user's past hygiene management history. Furthermore, it can formulate an optimal hygiene management plan based on the user's past hygiene management history. In this way, more appropriate hygiene management becomes possible by referring to the user's past hygiene management history.

[0157] The service provider can select the optimal service delivery method by referring to the user's past entertainment history when providing entertainment. For example, it can provide appropriate entertainment based on the user's past entertainment history. It can also suggest ways to reduce unnecessary entertainment based on the user's past entertainment history. Furthermore, it can formulate an optimal entertainment plan based on the user's past entertainment history. In this way, by referring to the user's past entertainment history, it becomes possible to provide more appropriate entertainment.

[0158] The reporting department can determine the optimal reporting content when reporting caregiving situations by referring to the user's past caregiving history. For example, it can determine appropriate reporting content based on the user's past caregiving history. It can also suggest ways to reduce unnecessary reporting based on the user's past caregiving history. Furthermore, it can develop an optimal reporting plan based on the user's past caregiving history. As a result, more appropriate reporting becomes possible by referring to the user's past caregiving history.

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

[0160] Step 1: The planning department develops a care plan. The planning department creates an optimal care plan based on information such as the user's health condition, living environment, and past care plans. For example, if the user's health condition deteriorates, the planning department can develop a corresponding care plan and instruct necessary medical measures. It is also possible to use AI to analyze the user's health condition and living environment and develop an optimal care plan. Step 2: The monitoring unit monitors the user's health status based on the care plan developed by the planning unit. The monitoring unit monitors the user's health data, such as body temperature, blood pressure, and heart rate, in real time. If an abnormality is detected, it can instruct medical action. It can also use AI to analyze health data and detect abnormalities. Step 3: The instruction unit directs medical actions based on the health status monitored by the monitoring unit. If the user's body temperature rises rapidly, the instruction unit can contact a medical institution and direct appropriate action. It can also use AI to analyze health data and direct appropriate medical actions. Step 4: The implementation monitoring unit monitors the implementation status of the care plan based on the medical measures instructed by the instruction unit. The implementation monitoring unit monitors whether the care plan is being implemented appropriately. If a problem occurs, it can issue a warning or notify external parties. It can also use AI to monitor the implementation status of the care plan and detect problems. Step 5: The notification unit issues emergency alerts and makes external notifications based on the implementation status of the care plan monitored by the execution monitoring unit. If the user is not following the care plan, the notification unit can alert the caregiver and contact family or medical institutions as needed. It can also use AI to analyze the emergency situation and make appropriate notifications.

[0161] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0163] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements mentioned above, including the planning unit, monitoring unit, instruction unit, execution monitoring unit, notification unit, management unit, coordination unit, hygiene management unit, provision unit, reporting unit, and proxy unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the smart device 14, which analyzes the user's health condition and living environment and formulates an optimal care plan. The monitoring unit monitors health data in real time using, for example, the camera 42 and sensors of the smart device 14. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which instructs medical measures based on health data. The execution monitoring unit monitors the implementation status of the care plan by, for example, the control unit 46A of the smart device 14. The notification unit makes emergency notifications by, for example, the specific processing unit 290 of the data processing unit 12. The management unit manages finances by, for example, the specific processing unit 290 of the data processing unit 12. The collaboration unit, for example, collaborates with the designated driver service via the control unit 46A of the smart device 14. The hygiene management unit, for example, manages the hygiene of the living space via the control unit 46A of the smart device 14. The provision unit, for example, provides entertainment via the control unit 46A of the smart device 14. The reporting unit, for example, reports the care situation to the family via the specific processing unit 290 of the data processing device 12. The substitute unit, for example, answers the user's questions and acts as a conversational partner via the control unit 46A of the smart device 14. 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.

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

[0166] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements mentioned above, including the planning unit, monitoring unit, instruction unit, execution monitoring unit, notification unit, management unit, coordination unit, hygiene management unit, provision unit, reporting unit, and proxy unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the user's health condition and living environment and formulates an optimal care plan. The monitoring unit monitors health data in real time using, for example, the camera 42 and sensors of the smart glasses 214. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which instructs medical measures based on health data. The execution monitoring unit monitors the implementation status of the care plan by, for example, the control unit 46A of the smart glasses 214. The notification unit makes emergency notifications by, for example, the specific processing unit 290 of the data processing unit 12. The management unit manages finances by, for example, the specific processing unit 290 of the data processing unit 12. The coordination unit, for example, coordinates with the designated driver service via the control unit 46A of the smart glasses 214. The hygiene management unit, for example, manages the hygiene of the living space via the control unit 46A of the smart glasses 214. The provision unit, for example, provides entertainment via the control unit 46A of the smart glasses 214. The reporting unit, for example, reports the care situation to the family via the specific processing unit 290 of the data processing device 12. The substitute unit, for example, answers the user's questions and acts as a conversational partner via the control unit 46A of the smart glasses 214. 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.

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

[0182] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0183] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0184] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0185] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0187] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0188] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0189] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0190] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0191] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0192] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0193] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0194] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0195] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0196] Each of the multiple elements described above, including the planning unit, monitoring unit, instruction unit, execution monitoring unit, notification unit, management unit, coordination unit, hygiene management unit, provision unit, reporting unit, and proxy unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the user's health condition and living environment and formulates an optimal care plan. The monitoring unit monitors health data in real time using, for example, the camera 42 and sensors of the headset terminal 314. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which instructs medical measures based on health data. The execution monitoring unit monitors the implementation status of the care plan by, for example, the control unit 46A of the headset terminal 314. The notification unit makes emergency notifications by, for example, the specific processing unit 290 of the data processing unit 12. The management unit manages finances by, for example, the specific processing unit 290 of the data processing unit 12. The coordination unit, for example, coordinates with the designated driver service via the control unit 46A of the headset terminal 314. The hygiene management unit, for example, manages the hygiene of the living space via the control unit 46A of the headset terminal 314. The provision unit, for example, provides entertainment via the control unit 46A of the headset terminal 314. The reporting unit, for example, reports the care situation to the family via the specific processing unit 290 of the data processing device 12. The substitute unit, for example, answers the user's questions and acts as a conversational partner via the control unit 46A of the headset terminal 314. 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.

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

[0198] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0199] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0200] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0201] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0202] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0203] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0204] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0205] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0206] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0207] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0208] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0209] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0210] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0211] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0212] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0213] Each of the multiple elements mentioned above, including the planning unit, monitoring unit, instruction unit, execution monitoring unit, notification unit, management unit, coordination unit, hygiene management unit, provision unit, reporting unit, and proxy unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the robot 414, which analyzes the user's health condition and living environment and formulates an optimal care plan. The monitoring unit monitors health data in real time using the camera 42 and sensors of the robot 414. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which instructs medical measures based on health data. The execution monitoring unit monitors the implementation status of the care plan by, for example, the control unit 46A of the robot 414. The notification unit makes emergency notifications by, for example, the specific processing unit 290 of the data processing unit 12. The management unit manages finances by, for example, the specific processing unit 290 of the data processing unit 12. The coordination unit, for example, coordinates with the driving assistance service via the control unit 46A of the robot 414. The hygiene management unit, for example, manages the hygiene of the living space via the control unit 46A of the robot 414. The provision unit, for example, provides entertainment via the control unit 46A of the robot 414. The reporting unit, for example, reports the care situation to the family via the specific processing unit 290 of the data processing device 12. The substitute unit, for example, answers the user's questions and acts as a conversational partner via 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.

[0214] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0215] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0216] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0217] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0218] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0219] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0220] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0221] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0222] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0223] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0224] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0225] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0226] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0227] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0228] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0229] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0230] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0231] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0232] (Note 1) The planning department is responsible for developing care plans, A monitoring unit monitors the health status based on the care plan formulated by the aforementioned planning unit, An instruction unit that issues instructions for medical treatment based on the health status monitored by the aforementioned monitoring unit, An execution monitoring unit that monitors the status of the care plan's implementation based on the medical measures instructed by the aforementioned instruction unit, The system includes a notification unit that issues emergency alerts and notifies external parties based on the implementation status of the care plan monitored by the aforementioned implementation monitoring unit. A system characterized by the following features. (Note 2) It has a management department to handle financial matters. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a liaison department that coordinates with designated driver services. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a hygiene management department that handles hygiene management of living spaces. The system described in Appendix 1, characterized by the features described herein. (Note 5) Equipped with a section that provides entertainment. The system described in Appendix 1, characterized by the features described herein. (Note 6) It has a reporting department that provides information to families about the care situation. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a proxy service to answer questions about unclear points and act as a conversation partner. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned planning department, The system estimates the user's emotions and adjusts the care plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned planning department, Analyze the results of past care plan implementations and develop the most effective care plan. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned planning department, Customize care plans based on the user's living environment and family structure. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned planning department, The system estimates the user's emotions and prioritizes care plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned planning department, When creating a care plan, the most effective care plan is developed based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned planning department, When developing a care plan, analyze the user's social media activity and create a relevant care plan. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the health monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned monitoring unit, Detect abnormal values ​​in health data in real time and respond quickly. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned monitoring unit, By referring to the user's past health data, we predict changes in their health status. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the frequency of health monitoring based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned monitoring unit, When monitoring health status, the most effective monitoring timing is set based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, When monitoring health status, the monitoring content is customized to take into account the user's diet and exercise habits. The system described in Appendix 1, characterized by the features described herein. (Note 20) The indicator unit is, The system estimates the user's emotions and adjusts the instructions for medical treatment based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The indicator unit is, When giving instructions for medical procedures, the system refers to the user's past medical history to provide the most appropriate instructions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The indicator unit is, When issuing medical treatment instructions, the system reflects the user's current health status in real time. The system described in Appendix 1, characterized by the features described herein. (Note 23) The indicator unit is, The system estimates the user's emotions and adjusts the timing of medical treatment instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The indicator unit is, When giving instructions for medical treatment, provide the most effective instructions based on the user's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The indicator unit is, Strengthening collaboration with the user's family and caregivers when issuing medical treatment instructions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned execution monitoring unit, The system estimates the user's emotions and adjusts the monitoring method for care plan implementation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned execution monitoring unit, We monitor the implementation status of care plans in real time and respond quickly if problems arise. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned execution monitoring unit, Compare the implementation status of the care plan with past data to identify areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned execution monitoring unit, The system estimates the user's emotions and adjusts the frequency of monitoring the care plan's implementation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned execution monitoring unit, When monitoring the implementation of the care plan, set the most effective monitoring timing based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned execution monitoring unit, Strengthen collaboration with the user's family and caregivers when monitoring the implementation status of care plans. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reporting unit, The system estimates the user's emotions and adjusts emergency notification content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reporting unit, In emergency situations, the system uses the user's past health data to determine the most appropriate message to send. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned reporting unit, When reporting in an emergency, it reflects the user's current health status in real time. The system according to Appendix 1, characterized in that. (Appendix 35) The reporting unit Estimates the user's emotions and adjusts the emergency reporting timing based on the estimated user emotions. The system according to Appendix 1, characterized in that. (Appendix 36) The reporting unit When reporting in an emergency, determines the most effective reporting content based on the user's living environment. The system according to Appendix 1, characterized in that. (Appendix 37) The reporting unit When reporting in an emergency, strengthens the cooperation between the user's family and medical institutions. The system according to Appendix 1, characterized in that. (Appendix 38) The management unit Estimates the user's emotions and adjusts the money management method based on the estimated user emotions. The system according to Appendix 2, characterized in that. (Appendix 39) The management unit When managing money, selects the optimal management method by referring to the user's past expenditure history. The system according to Appendix 2, characterized in that. (Appendix 40) The management unit Estimates the user's emotions and determines the priority of money management based on the estimated user emotions. The system according to Appendix 2, characterized in that. (Appendix 4) The management unit When managing money, selects the most effective management method based on the user's living environment. The system according to Appendix 2, characterized in that. (Appendix 42) The cooperation unit The system estimates the user's emotions and adjusts the method of coordinating with the designated driver service based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned linkage unit is, When integrating with a designated driver service, the system selects the optimal integration method by referring to the user's past usage history. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned linkage unit is, The system estimates the user's emotions and adjusts the frequency of the designated driver service's interaction based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned linkage unit is, When integrating with designated driver services, the most effective integration method is selected based on the user's living environment. The system described in Appendix 3, characterized by the features described herein. (Note 46) The aforementioned hygiene management department, The system estimates the user's emotions and adjusts the hygiene management methods for the living space based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned hygiene management department, When managing hygiene in living spaces, the system selects the optimal management method by referring to the user's past hygiene management history. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned hygiene management department, It estimates the user's emotions and determines the priority of hygiene management in the living space based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 49) The aforementioned hygiene management department, When managing hygiene in living spaces, select the most effective management method based on the user's living environment. The system described in Appendix 4, characterized by the features described herein. (Note 50) The providing unit estimates the user's emotion and adjusts the method of providing entertainment based on the estimated user emotion The system according to supplementary note 5, characterized by the above (Supplementary note 51) The providing unit selects an optimal providing method by referring to the user's past entertainment history when providing entertainment The system according to supplementary note 5, characterized by the above (Supplementary note 52) The providing unit estimates the user's emotion and adjusts the frequency of providing entertainment based on the estimated user emotion The system according to supplementary note 5, characterized by the above (Supplementary note 53) The providing unit selects the most effective providing method based on the user's living environment when providing entertainment The system according to supplementary note 5, characterized by the above (Supplementary note 54) The reporting unit estimates the user's emotion and adjusts the content of the report on the care situation based on the estimated user emotion The system according to supplementary note 6, characterized by the above (Supplementary note 55) The reporting unit determines the optimal report content by referring to the user's past care history when reporting the care situation The system according to supplementary note 6, characterized by the above (Supplementary note 56) The reporting unit estimates the user's emotion and adjusts the frequency of reporting the care situation based on the estimated user emotion The system according to supplementary note 6, characterized by the above (Supplementary note 57) The reporting unit strengthens the cooperation with the user's family and caregivers when reporting the care situation The system according to supplementary note 6, characterized by the above (Supplementary note 58) The acting unit It estimates the user's emotions and adjusts the questions and conversational approach based on those estimated emotions. The system described in Appendix 7, characterized by the features described herein. (Note 59) The aforementioned agency unit, When asking questions or acting as a conversational partner, the system refers to the user's past question history to select the most appropriate method of assistance. The system described in Appendix 7, characterized by the features described herein. (Note 60) The aforementioned agency unit, It estimates the user's emotions and adjusts the frequency of questions and conversational interactions based on those estimated emotions. The system described in Appendix 7, characterized by the features described herein. (Note 61) The aforementioned agency unit, When asking questions or acting as a conversational partner, the system selects the most effective method of assistance based on the user's living environment. The system described in Appendix 7, characterized by the features described herein. [Explanation of symbols]

[0233] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The planning department is responsible for developing care plans, A monitoring unit monitors the health status based on the care plan formulated by the aforementioned planning unit, An instruction unit that issues instructions for medical treatment based on the health status monitored by the aforementioned monitoring unit, An execution monitoring unit that monitors the status of the care plan's implementation based on the medical measures instructed by the aforementioned instruction unit, The system includes a notification unit that issues emergency alerts and notifies external parties based on the implementation status of the care plan monitored by the aforementioned implementation monitoring unit. A system characterized by the following features.

2. It has a management department to handle financial matters. The system according to feature 1.

3. It includes a liaison department that coordinates with designated driver services. The system according to feature 1.

4. It has a hygiene management department that handles hygiene management of living spaces. The system according to feature 1.

5. Equipped with a section that provides entertainment. The system according to feature 1.

6. It has a reporting department that provides information to families about the care situation. The system according to feature 1.

7. It includes a proxy service to answer questions about unclear points and act as a conversation partner. The system according to feature 1.

8. The aforementioned planning department, The system estimates the user's emotions and adjusts the care plan based on those emotions. The system according to feature 1.

9. The aforementioned planning department, Analyze the results of past care plan implementations and develop the most effective care plan. The system according to feature 1.

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