system

Abstract

The system according to this embodiment aims to analyze the health status and lifestyle needs of care recipients and propose and monitor an optimal care plan. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects data from sensors and medical devices. The analysis unit analyzes the data collected by the data collection unit to determine the health status and living needs of the care recipient. The proposal unit proposes an optimal care plan based on the analysis results obtained by the analysis unit. The monitoring unit monitors the progress of the care plan proposed by the proposal unit in real time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently carried out to grasp the health status and living needs of care recipients in real time and propose and monitor an optimal care plan, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the health status and living needs of care recipients and propose and monitor an optimal care plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects data from sensors and medical devices. The analysis unit analyzes the data collected by the data collection unit to determine the health status and living needs of the care recipient. The proposal unit proposes an optimal care plan based on the analysis results obtained by the analysis unit. The monitoring unit monitors the progress of the care plan proposed by the proposal unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the health status and lifestyle needs of care recipients and propose and monitor an optimal care plan. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The care support system according to an embodiment of the present invention is a system that uses AI to analyze the health status and lifestyle needs of each care recipient and proposes an individualized care plan. The care support system integrates data from sensors and medical devices, and the AI ​​analyzes the health status and lifestyle needs of the care recipient. Next, the AI ​​proposes an optimal care plan for each care recipient based on the analysis results. This care plan is designed to respond quickly to daily changes. Furthermore, caregivers and family members can monitor the progress of the care plan in real time through a dedicated app and take action as needed. This system enables care recipients and their supporters to build a better quality of life together. For example, the care support system collects data from sensors and medical devices. In this process, it collects detailed data on the health status and lifestyle needs of the care recipient. For example, it collects vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily activities. This allows the system to understand the health status and lifestyle needs of the care recipient. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the collected data and determines the health status and lifestyle needs of the care recipient. For example, the system can detect changes in health status from fluctuations in heart rate and blood pressure, and identify lifestyle needs from daily activity data. This allows for the proposal of an optimal care plan for the care recipient. Furthermore, the proposed care plan is designed to respond quickly to daily changes. For example, the care plan can be modified in response to changes in health status, and necessary support can be provided. This helps maintain the care recipient's health and improve their quality of life. Caregivers and family members can also monitor the progress of the care plan in real time through a dedicated app. For example, they can check the implementation status of the care plan and changes in health status, and take action as needed. This allows caregivers and family members to respond quickly to the care recipient's health status and lifestyle needs. Through this system, care recipients and their caregivers can build a better quality of life together. For example, by maintaining the care recipient's health status and improving their quality of life, the burden on caregivers and family members can be reduced.Furthermore, by monitoring the progress of care plans in real time, prompt responses can be made, thereby maintaining the health status of care recipients and improving their quality of life. This allows the care support system to propose individualized care plans based on the health status and lifestyle needs of care recipients, and to monitor them in real time.

[0029] The care support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects data from sensors and medical devices. The data collection unit collects, for example, vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily living activities. For example, the data collection unit collects heart rate data of the care recipient using a heart rate sensor. The data collection unit can also collect blood pressure data of the care recipient using a medical device that measures blood pressure. Furthermore, the data collection unit can also collect body temperature data of the care recipient using a body temperature sensor. For example, the data collection unit attaches a heart rate sensor to the care recipient's wrist and collects heart rate data in real time. A blood pressure monitor is attached to the care recipient's upper arm and collects blood pressure data periodically. A thermometer is attached to the care recipient's forehead and collects body temperature data. The analysis unit analyzes the data collected by the data collection unit and determines the care recipient's health status and living needs. For example, the analysis unit detects changes in health status from fluctuations in heart rate and blood pressure. The analysis unit, for example, analyzes heart rate data and detects abnormal heart rate fluctuations. The analysis unit can also analyze blood pressure data and detect abnormal blood pressure fluctuations. Furthermore, the analysis unit can analyze body temperature data and detect abnormal body temperature fluctuations. For example, the analysis unit analyzes heart rate data in a time series to detect abnormal heart rate fluctuations. It analyzes blood pressure data for daily fluctuations to detect abnormal blood pressure fluctuations. It analyzes body temperature data for hourly fluctuations to detect abnormal body temperature fluctuations. The proposal unit proposes an optimal care plan based on the analysis results obtained by the analysis unit. The proposal unit proposes an individualized care plan based on the care recipient's health condition and lifestyle needs. The proposal unit modifies the care plan in response to changes in health condition. The proposal unit modifies the care plan in response to heart rate fluctuations. It can also modify the care plan in response to blood pressure fluctuations. Furthermore, it can modify the care plan in response to body temperature fluctuations. For example, the proposal unit proposes a care plan that adjusts the amount of exercise in response to heart rate fluctuations. We propose a care plan that adjusts meal content according to blood pressure fluctuations. We propose a care plan that adjusts rest periods according to body temperature fluctuations.The monitoring unit monitors the progress of the care plan proposed by the proposal unit in real time. The monitoring unit monitors, for example, the implementation status of the care plan and changes in health status. The monitoring unit monitors, for example, the implementation status of the care plan in real time and takes action as needed. The monitoring unit monitors the implementation status of the care plan and issues an alert if an abnormality occurs. The monitoring unit can also monitor changes in health status in real time and issue an alert if an abnormality occurs. Furthermore, the monitoring unit can monitor the progress of the care plan in real time and notify caregivers and family members as needed. For example, the monitoring unit monitors the implementation status of the care plan and issues an alert if an abnormality occurs. It monitors changes in health status in real time and issues an alert if an abnormality occurs. It monitors the progress of the care plan in real time and notifies caregivers and family members as needed. As a result, the care support system according to the embodiment can propose an individualized care plan based on the health status and living needs of the care recipient and monitor it in real time.

[0030] The data collection unit collects data from sensors and medical devices. For example, it collects vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily activities. Specifically, it uses a heart rate sensor to collect heart rate data from care recipients. The heart rate sensor is attached to the care recipient's wrist and collects heart rate data in real time. This allows for continuous monitoring of fluctuations in the care recipient's heart rate. The data collection unit can also collect blood pressure data from care recipients using a medical device that measures blood pressure. The blood pressure monitor is attached to the care recipient's upper arm and collects blood pressure data periodically. This allows for monitoring of fluctuations in the care recipient's blood pressure and early detection of abnormal fluctuations. Furthermore, the data collection unit can also collect body temperature data from care recipients using a body temperature sensor. The thermometer is attached to the care recipient's forehead and collects body temperature data. This allows for real-time monitoring of fluctuations in the care recipient's body temperature and early detection of abnormal temperature fluctuations. The data collection unit centrally manages the data collected from these sensors and medical devices and transmits it to a central database. This allows the data collection unit to continuously monitor the health status of care recipients and respond quickly if any abnormalities occur. Furthermore, the data collection unit can utilize the collected data in cooperation with the analysis, proposal, and monitoring units. For example, the collected data can be stored on a cloud server, which the analysis unit can access and analyze. The proposal unit can also propose care plans based on the collected data, and the monitoring unit can monitor the progress of the care plans based on the collected data. In this way, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The Analysis Department analyzes data collected by the Data Collection Department to determine the health status and lifestyle needs of care recipients. Specifically, it detects changes in health status from fluctuations in heart rate and blood pressure. For example, it analyzes heart rate data to detect abnormal heart rate fluctuations. Heart rate data is analyzed in time series, allowing for early detection of abnormal heart rate fluctuations. It can also analyze blood pressure data to detect abnormal blood pressure fluctuations. Blood pressure data is analyzed in daily fluctuations, allowing for early detection of abnormal blood pressure fluctuations. Furthermore, it can analyze body temperature data to detect abnormal body temperature fluctuations. Body temperature data is analyzed in hourly fluctuations, allowing for early detection of abnormal body temperature fluctuations. Based on this data, the Analysis Department comprehensively evaluates the health status of care recipients and can respond quickly if abnormalities occur. In addition, the Analysis Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical heart rate data, it can predict risk fluctuations in specific time periods or situations and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department proposes the optimal care plan based on the analysis results obtained by the Analysis Department. Specifically, it proposes individualized care plans based on the health condition and lifestyle needs of the care recipient. For example, it modifies the care plan in response to changes in health condition. It proposes care plans that adjust the amount of exercise in response to fluctuations in heart rate. For example, if the heart rate is high, it proposes reducing the amount of exercise, and if the heart rate is low, it proposes increasing the amount of exercise. It also proposes care plans that adjust the diet in response to fluctuations in blood pressure. For example, if blood pressure is high, it proposes a diet with reduced salt intake, and if blood pressure is low, it proposes moderate salt intake. Furthermore, it proposes care plans that adjust rest time in response to fluctuations in body temperature. For example, if body temperature is high, it proposes increasing rest time, and if body temperature is low, it proposes moderate exercise. The Proposal Department provides these proposals to care recipients and caregivers and supports the implementation of the care plan. In addition, the Proposal Department can monitor the implementation status of the care plan and modify it as needed. For example, it regularly evaluates the implementation status of the care plan and modifies it in accordance with the health condition and lifestyle needs of the care recipient. Furthermore, the proposal department can collect feedback from care recipients and caregivers, enabling continuous improvement of the accuracy and effectiveness of care plans. This allows the proposal department to propose optimal care plans based on the health status and lifestyle needs of care recipients and to support the implementation of those plans.

[0033] The monitoring department monitors the progress of care plans proposed by the proposal department in real time. Specifically, it monitors the implementation status of care plans and changes in health status. For example, it monitors the implementation status of care plans in real time and takes action as needed. It monitors the implementation status of care plans and issues alerts if abnormalities occur. It can also monitor changes in health status in real time and issue alerts if abnormalities occur. For example, it can monitor fluctuations in heart rate, blood pressure, and body temperature in real time and respond quickly if abnormalities occur. Furthermore, the monitoring department can monitor the progress of care plans in real time and notify caregivers and family members as needed. For example, it monitors the implementation status of care plans and issues alerts if abnormalities occur. It monitors changes in health status in real time and issues alerts if abnormalities occur. It monitors the progress of care plans in real time and notifies caregivers and family members as needed. Based on these monitoring results, the monitoring department can also propose revisions and improvements to care plans. For example, it provides feedback to the proposal department based on the implementation status of care plans and changes in health status and proposes revisions and improvements to care plans. Furthermore, the monitoring department can collect feedback from care recipients and caregivers, enabling continuous improvement of the accuracy and effectiveness of monitoring. This allows the monitoring department to monitor the progress of care plans in real time and respond quickly if any abnormalities occur. In addition, the monitoring department can propose revisions and improvements to care plans, supporting the implementation of optimal care plans based on the health status and lifestyle needs of care recipients.

[0034] The data collection unit can collect vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily living activities. For example, the data collection unit can collect heart rate data of care recipients using a heart rate sensor. For example, the data collection unit can attach a heart rate sensor to the care recipient's wrist and collect heart rate data in real time. The data collection unit can also collect blood pressure data of care recipients using a medical device that measures blood pressure. For example, the data collection unit can attach a blood pressure monitor to the care recipient's upper arm and collect blood pressure data periodically. Furthermore, the data collection unit can collect body temperature data of care recipients using a body temperature sensor. For example, the data collection unit can attach a thermometer to the care recipient's forehead and collect body temperature data. This allows the data collection unit to understand the care recipient's detailed health status and lifestyle needs. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the heart rate sensor into a generating AI, which can analyze the data to detect fluctuations in heart rate.

[0035] The analysis unit can analyze the collected data to determine the health status and lifestyle needs of care recipients. For example, the analysis unit can detect changes in health status from fluctuations in heart rate and blood pressure. For example, the analysis unit can analyze heart rate data to detect abnormal fluctuations in heart rate. The analysis unit can also analyze blood pressure data to detect abnormal fluctuations in blood pressure. Furthermore, the analysis unit can analyze body temperature data to detect abnormal fluctuations in body temperature. For example, the analysis unit can analyze heart rate data in time series to detect abnormal fluctuations in heart rate. For blood pressure data, it can analyze daily fluctuations to detect abnormal fluctuations in blood pressure. For body temperature data, it can analyze hourly fluctuations to detect abnormal fluctuations in body temperature. This allows the analysis unit to accurately determine the health status and lifestyle needs of care recipients. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can analyze the data to determine the health status and lifestyle needs.

[0036] The proposal unit can propose an optimal care plan based on the analysis results. For example, the proposal unit proposes an individualized care plan based on the care recipient's health condition and lifestyle needs. For example, the proposal unit modifies the care plan in response to changes in health condition. For example, the proposal unit modifies the care plan in response to fluctuations in heart rate. The proposal unit can also modify the care plan in response to fluctuations in blood pressure. Furthermore, the proposal unit can also modify the care plan in response to fluctuations in body temperature. For example, the proposal unit proposes a care plan that adjusts the amount of exercise in response to fluctuations in heart rate. The proposal unit proposes a care plan that adjusts the content of meals in response to fluctuations in blood pressure. The proposal unit proposes a care plan that adjusts rest time in response to fluctuations in body temperature. In this way, the proposal unit can propose an optimal care plan for the care recipient. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the analysis results into a generating AI, and the generating AI can propose an optimal care plan.

[0037] The monitoring unit can monitor the progress of the proposed care plan in real time and take action as needed. For example, the monitoring unit monitors the implementation status of the care plan and changes in health status. For example, the monitoring unit monitors the implementation status of the care plan in real time and takes action as needed. For example, the monitoring unit monitors the implementation status of the care plan and issues an alert if an abnormality occurs. The monitoring unit can also monitor changes in health status in real time and issue an alert if an abnormality occurs. Furthermore, the monitoring unit can monitor the progress of the care plan in real time and notify caregivers and family members as needed. For example, the monitoring unit monitors the implementation status of the care plan and issues an alert if an abnormality occurs. The monitoring unit monitors changes in health status in real time and issues an alert if an abnormality occurs. The monitoring unit monitors the progress of the care plan in real time and notifies caregivers and family members as needed. This allows the monitoring unit to monitor the progress of the care plan in real time and respond quickly. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit inputs progress data from care plans into a generating AI, which then monitors the data in real time and takes action as needed.

[0038] The data collection unit can analyze the past health data of care recipients and select the optimal data collection method. For example, the data collection unit can concentrate data collection during specific time periods based on the care recipient's past health data. For example, the data collection unit can intensify data collection during periods of significant fluctuation in specific vital signs based on the care recipient's past health data. For example, the data collection unit can analyze the care recipient's past health data and select the most efficient data collection method. This enables efficient data collection by allowing the data collection unit to select the optimal data collection method based on past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter data based on the care recipient's living environment and activity patterns during data collection. For example, the data collection unit adjusts the timing of data collection according to the care recipient's living environment. For example, the data collection unit filters out unnecessary data based on the care recipient's activity patterns. For example, the data collection unit selects the optimal data collection method considering the care recipient's living environment and activity patterns. As a result, the data collection unit can eliminate unnecessary data by filtering data based on living environment and activity patterns, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on living environment and activity patterns into a generating AI, which can then perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of care recipients during data collection. For example, if a care recipient is in a specific location, the data collection unit will prioritize the collection of data related to that location. For example, the data collection unit will collect the most relevant data based on the geographical location information of care recipients. For example, if a care recipient is on the move, the data collection unit will prioritize the collection of data related to their destination. This enables efficient data collection by prioritizing the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the social media activities of care recipients and collect relevant data during data collection. For example, the data collection unit can collect information related to health status from the social media activities of care recipients. For example, the data collection unit can analyze the social media activities of care recipients and collect data related to their living needs. For example, the data collection unit can select the optimal data collection method based on the social media activities of care recipients. This allows the data collection unit to more accurately understand the living needs of care recipients by collecting relevant data based on their social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI, and the generating AI can collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit performs a detailed analysis on important vital sign data. The analysis unit adjusts the level of detail of the analysis according to the importance of the health data. For example, the analysis unit performs a simplified analysis on less important data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis according to the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, which can then adjust the level of detail of the analysis based on the importance of the data.

[0043] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit may apply a specific analysis algorithm to vital sign data. For example, the analysis unit may apply a different analysis algorithm to daily life activity data. For example, the analysis unit may select the optimal analysis algorithm depending on the category of health data. This enables accurate analysis by applying the optimal analysis algorithm according to the category of health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, which can then apply the optimal analysis algorithm according to the data category.

[0044] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of the most recent health data. The analysis unit may determine the priority of analysis based on when the health data was collected. For example, the analysis unit may perform a simplified analysis on older data. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on when the health data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input health data into a generating AI, which can then determine the priority of analysis based on when the data was collected.

[0045] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit prioritizes the analysis of the most relevant data based on the relevance of the health data. The analysis unit adjusts the order of analysis based on the relevance of the health data. For example, the analysis unit performs a simplified analysis on less relevant data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, which can then adjust the order of analysis based on the relevance of the data.

[0046] The proposal unit can adjust the level of detail in its proposals based on the importance of the care plan. For example, the proposal unit can provide detailed proposals for important care plans. The proposal unit can adjust the level of detail in its proposals according to the importance of the care plan. For example, the proposal unit can provide simplified proposals for less important care plans. This allows the proposal unit to provide efficient proposals by adjusting the level of detail in its proposals according to the importance of the care plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input care plan data into a generating AI, which can then adjust the level of detail in its proposals based on the importance of the plan.

[0047] The proposal unit can apply different proposal algorithms depending on the category of the care plan when making a proposal. For example, the proposal unit applies a specific proposal algorithm to a care plan related to health management. For example, the proposal unit applies a different proposal algorithm to a care plan related to support for daily living. For example, the proposal unit selects the optimal proposal algorithm depending on the category of the care plan. This enables the proposal unit to make accurate proposals by applying the optimal proposal algorithm according to the category of the care plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input care plan data into a generating AI, and the generating AI can apply the optimal proposal algorithm according to the category of the plan.

[0048] The proposal department can determine the priority of proposals based on the submission date of the care plan. For example, the proposal department will prioritize the most recent care plan. The proposal department will determine the priority of proposals based on the submission date of the care plan. For example, the proposal department will make simplified proposals for older care plans. This allows the proposal department to prioritize the most recent plan by determining the priority of proposals based on the submission date of the care plan. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input care plan data into a generating AI, which can then determine the priority of proposals based on the submission date of the plan.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the care plans. For example, the proposal unit prioritizes the most relevant proposals based on the relevance of the care plans. The proposal unit adjusts the order of proposals based on the relevance of the care plans. For example, the proposal unit provides simplified proposals for less relevant care plans. This allows the proposal unit to make efficient proposals by adjusting the order of proposals based on the relevance of the care plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input care plan data into a generating AI, which can then adjust the order of proposals based on the relevance of the plans.

[0050] The monitoring unit can analyze the progress data of the care plan during monitoring and select the optimal monitoring method. For example, the monitoring unit selects the optimal monitoring method based on the progress data of the care plan. For example, the monitoring unit adjusts the frequency of monitoring based on the results of the analysis of the progress data. For example, the monitoring unit adjusts the level of detail of monitoring according to the importance of the progress data. This enables efficient monitoring by allowing the monitoring unit to select the optimal monitoring method based on the results of the analysis of the progress data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input progress data into a generating AI, and the generating AI can select the optimal monitoring method.

[0051] The monitoring unit can apply different monitoring algorithms depending on the category of the care plan during monitoring. For example, the monitoring unit applies a specific monitoring algorithm to a care plan related to health management. For example, the monitoring unit applies a different monitoring algorithm to a care plan related to support for daily living. For example, the monitoring unit selects the optimal monitoring algorithm depending on the category of the care plan. This enables accurate monitoring by applying the optimal monitoring algorithm according to the category of the care plan. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input care plan data into a generating AI, and the generating AI can apply the optimal monitoring algorithm according to the category of the plan.

[0052] The monitoring unit can determine monitoring priorities based on the timing of data collection for the care plan during monitoring. For example, the monitoring unit prioritizes monitoring the most recent progress data. The monitoring unit determines monitoring priorities based on the timing of data collection. For example, the monitoring unit performs simplified monitoring on older progress data. This allows the monitoring unit to prioritize monitoring the most recent data by determining monitoring priorities based on the timing of data collection. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input progress data into a generating AI, which can then determine monitoring priorities based on the timing of data collection.

[0053] The monitoring unit can adjust the order of monitoring based on the relevance of the care plan during monitoring. For example, the monitoring unit prioritizes monitoring the most relevant data based on the relevance of the care plan. The monitoring unit adjusts the order of monitoring based on the relevance of the care plan. For example, the monitoring unit performs simplified monitoring on less relevant data. This enables efficient monitoring by adjusting the order of monitoring based on the relevance of the care plan. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit inputs care plan data into a generating AI, which can then adjust the order of monitoring based on the relevance of the plan.

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

[0055] The care support system can also be equipped with a reminder function. This reminder function provides reminders regarding the care recipient's schedule and health management. For example, it can notify the care recipient of medication times, regular health check times, and exercise and rehabilitation times. This allows the reminder function to support the care recipient's schedule management and promote health management.

[0056] The care support system can also include an education department. This department provides educational content related to caregiving to caregivers and their families. For example, it can provide content to learn appropriate care methods based on the health status and living needs of care recipients. It can also provide resources for caregivers and families to learn the latest information on caregiving. Furthermore, it can offer training programs to help caregivers and families improve their caregiving skills. This allows caregivers and families to enhance their knowledge and skills in caregiving.

[0057] The care support system can also be equipped with a monitoring unit. The monitoring unit continuously monitors the health status and living needs of care recipients and issues alerts if abnormalities are detected. For example, the monitoring unit can monitor the care recipient's vital signs and activity data in real time and detect abnormal fluctuations. It can also monitor the care recipient's living environment and activity patterns and detect abnormal behavior. Furthermore, the monitoring unit can propose appropriate responses based on the care recipient's health status and living needs. As a result, the monitoring unit can continuously monitor the care recipient's health status and living needs and respond quickly.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data acquisition unit collects data from sensors and medical devices. Specifically, it collects vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily living activities. For example, a heart rate sensor is attached to the care recipient's wrist to collect heart rate data in real time. A blood pressure monitor is attached to the care recipient's upper arm to collect blood pressure data periodically. A thermometer is attached to the care recipient's forehead to collect body temperature data. Step 2: The analysis unit analyzes the data collected by the data collection unit to determine the health status and lifestyle needs of care recipients. Specifically, it detects changes in health status from fluctuations in heart rate, blood pressure, and body temperature. For example, it analyzes heart rate data in a time series to detect abnormal heart rate fluctuations. It analyzes blood pressure data for daily fluctuations to detect abnormal blood pressure fluctuations. It analyzes body temperature data for hourly fluctuations to detect abnormal body temperature fluctuations. Step 3: The proposal department proposes the optimal care plan based on the analysis results obtained by the analysis department. Specifically, it proposes an individualized care plan based on the care recipient's health condition and lifestyle needs, and modifies the care plan according to changes in their health condition. For example, it proposes a care plan that adjusts the amount of exercise according to fluctuations in heart rate, a care plan that adjusts the content of meals according to fluctuations in blood pressure, and a care plan that adjusts rest time according to fluctuations in body temperature. Step 4: The monitoring department monitors the progress of the care plan proposed by the proposal department in real time. Specifically, it monitors the implementation status of the care plan and changes in health status, and issues alerts if any abnormalities occur. Furthermore, it monitors the progress of the care plan in real time and notifies caregivers and family members as needed.

[0060] (Example of form 2) The care support system according to an embodiment of the present invention is a system that uses AI to analyze the health status and lifestyle needs of each care recipient and proposes an individualized care plan. The care support system integrates data from sensors and medical devices, and the AI ​​analyzes the health status and lifestyle needs of the care recipient. Next, the AI ​​proposes an optimal care plan for each care recipient based on the analysis results. This care plan is designed to respond quickly to daily changes. Furthermore, caregivers and family members can monitor the progress of the care plan in real time through a dedicated app and take action as needed. This system enables care recipients and their supporters to build a better quality of life together. For example, the care support system collects data from sensors and medical devices. In this process, it collects detailed data on the health status and lifestyle needs of the care recipient. For example, it collects vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily activities. This allows the system to understand the health status and lifestyle needs of the care recipient. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the collected data and determines the health status and lifestyle needs of the care recipient. For example, the system can detect changes in health status from fluctuations in heart rate and blood pressure, and identify lifestyle needs from daily activity data. This allows for the proposal of an optimal care plan for the care recipient. Furthermore, the proposed care plan is designed to respond quickly to daily changes. For example, the care plan can be modified in response to changes in health status, and necessary support can be provided. This helps maintain the care recipient's health and improve their quality of life. Caregivers and family members can also monitor the progress of the care plan in real time through a dedicated app. For example, they can check the implementation status of the care plan and changes in health status, and take action as needed. This allows caregivers and family members to respond quickly to the care recipient's health status and lifestyle needs. Through this system, care recipients and their caregivers can build a better quality of life together. For example, by maintaining the care recipient's health status and improving their quality of life, the burden on caregivers and family members can be reduced.Furthermore, by monitoring the progress of care plans in real time, prompt responses can be made, thereby maintaining the health status of care recipients and improving their quality of life. This allows the care support system to propose individualized care plans based on the health status and lifestyle needs of care recipients, and to monitor them in real time.

[0061] The care support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects data from sensors and medical devices. The data collection unit collects, for example, vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily living activities. For example, the data collection unit collects heart rate data of the care recipient using a heart rate sensor. The data collection unit can also collect blood pressure data of the care recipient using a medical device that measures blood pressure. Furthermore, the data collection unit can also collect body temperature data of the care recipient using a body temperature sensor. For example, the data collection unit attaches a heart rate sensor to the care recipient's wrist and collects heart rate data in real time. A blood pressure monitor is attached to the care recipient's upper arm and collects blood pressure data periodically. A thermometer is attached to the care recipient's forehead and collects body temperature data. The analysis unit analyzes the data collected by the data collection unit and determines the care recipient's health status and living needs. For example, the analysis unit detects changes in health status from fluctuations in heart rate and blood pressure. The analysis unit, for example, analyzes heart rate data and detects abnormal heart rate fluctuations. The analysis unit can also analyze blood pressure data and detect abnormal blood pressure fluctuations. Furthermore, the analysis unit can analyze body temperature data and detect abnormal body temperature fluctuations. For example, the analysis unit analyzes heart rate data in a time series to detect abnormal heart rate fluctuations. It analyzes blood pressure data for daily fluctuations to detect abnormal blood pressure fluctuations. It analyzes body temperature data for hourly fluctuations to detect abnormal body temperature fluctuations. The proposal unit proposes an optimal care plan based on the analysis results obtained by the analysis unit. The proposal unit proposes an individualized care plan based on the care recipient's health condition and lifestyle needs. The proposal unit modifies the care plan in response to changes in health condition. The proposal unit modifies the care plan in response to heart rate fluctuations. It can also modify the care plan in response to blood pressure fluctuations. Furthermore, it can modify the care plan in response to body temperature fluctuations. For example, the proposal unit proposes a care plan that adjusts the amount of exercise in response to heart rate fluctuations. We propose a care plan that adjusts meal content according to blood pressure fluctuations. We propose a care plan that adjusts rest periods according to body temperature fluctuations.The monitoring unit monitors the progress of the care plan proposed by the proposal unit in real time. The monitoring unit monitors, for example, the implementation status of the care plan and changes in health status. The monitoring unit monitors, for example, the implementation status of the care plan in real time and takes action as needed. The monitoring unit monitors the implementation status of the care plan and issues an alert if an abnormality occurs. The monitoring unit can also monitor changes in health status in real time and issue an alert if an abnormality occurs. Furthermore, the monitoring unit can monitor the progress of the care plan in real time and notify caregivers and family members as needed. For example, the monitoring unit monitors the implementation status of the care plan and issues an alert if an abnormality occurs. It monitors changes in health status in real time and issues an alert if an abnormality occurs. It monitors the progress of the care plan in real time and notifies caregivers and family members as needed. As a result, the care support system according to the embodiment can propose an individualized care plan based on the health status and living needs of the care recipient and monitor it in real time.

[0062] The data collection unit collects data from sensors and medical devices. For example, it collects vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily activities. Specifically, it uses a heart rate sensor to collect heart rate data from care recipients. The heart rate sensor is attached to the care recipient's wrist and collects heart rate data in real time. This allows for continuous monitoring of fluctuations in the care recipient's heart rate. The data collection unit can also collect blood pressure data from care recipients using a medical device that measures blood pressure. The blood pressure monitor is attached to the care recipient's upper arm and collects blood pressure data periodically. This allows for monitoring of fluctuations in the care recipient's blood pressure and early detection of abnormal fluctuations. Furthermore, the data collection unit can also collect body temperature data from care recipients using a body temperature sensor. The thermometer is attached to the care recipient's forehead and collects body temperature data. This allows for real-time monitoring of fluctuations in the care recipient's body temperature and early detection of abnormal temperature fluctuations. The data collection unit centrally manages the data collected from these sensors and medical devices and transmits it to a central database. This allows the data collection unit to continuously monitor the health status of care recipients and respond quickly if any abnormalities occur. Furthermore, the data collection unit can utilize the collected data in cooperation with the analysis, proposal, and monitoring units. For example, the collected data can be stored on a cloud server, which the analysis unit can access and analyze. The proposal unit can also propose care plans based on the collected data, and the monitoring unit can monitor the progress of the care plans based on the collected data. In this way, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0063] The Analysis Department analyzes data collected by the Data Collection Department to determine the health status and lifestyle needs of care recipients. Specifically, it detects changes in health status from fluctuations in heart rate and blood pressure. For example, it analyzes heart rate data to detect abnormal heart rate fluctuations. Heart rate data is analyzed in time series, allowing for early detection of abnormal heart rate fluctuations. It can also analyze blood pressure data to detect abnormal blood pressure fluctuations. Blood pressure data is analyzed in daily fluctuations, allowing for early detection of abnormal blood pressure fluctuations. Furthermore, it can analyze body temperature data to detect abnormal body temperature fluctuations. Body temperature data is analyzed in hourly fluctuations, allowing for early detection of abnormal body temperature fluctuations. Based on this data, the Analysis Department comprehensively evaluates the health status of care recipients and can respond quickly if abnormalities occur. In addition, the Analysis Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical heart rate data, it can predict risk fluctuations in specific time periods or situations and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0064] The Proposal Department proposes the optimal care plan based on the analysis results obtained by the Analysis Department. Specifically, it proposes individualized care plans based on the health condition and lifestyle needs of the care recipient. For example, it modifies the care plan in response to changes in health condition. It proposes care plans that adjust the amount of exercise in response to fluctuations in heart rate. For example, if the heart rate is high, it proposes reducing the amount of exercise, and if the heart rate is low, it proposes increasing the amount of exercise. It also proposes care plans that adjust the diet in response to fluctuations in blood pressure. For example, if blood pressure is high, it proposes a diet with reduced salt intake, and if blood pressure is low, it proposes moderate salt intake. Furthermore, it proposes care plans that adjust rest time in response to fluctuations in body temperature. For example, if body temperature is high, it proposes increasing rest time, and if body temperature is low, it proposes moderate exercise. The Proposal Department provides these proposals to care recipients and caregivers and supports the implementation of the care plan. In addition, the Proposal Department can monitor the implementation status of the care plan and modify it as needed. For example, it regularly evaluates the implementation status of the care plan and modifies it in accordance with the health condition and lifestyle needs of the care recipient. Furthermore, the proposal department can collect feedback from care recipients and caregivers, enabling continuous improvement of the accuracy and effectiveness of care plans. This allows the proposal department to propose optimal care plans based on the health status and lifestyle needs of care recipients and to support the implementation of those plans.

[0065] The monitoring department monitors the progress of care plans proposed by the proposal department in real time. Specifically, it monitors the implementation status of care plans and changes in health status. For example, it monitors the implementation status of care plans in real time and takes action as needed. It monitors the implementation status of care plans and issues alerts if abnormalities occur. It can also monitor changes in health status in real time and issue alerts if abnormalities occur. For example, it can monitor fluctuations in heart rate, blood pressure, and body temperature in real time and respond quickly if abnormalities occur. Furthermore, the monitoring department can monitor the progress of care plans in real time and notify caregivers and family members as needed. For example, it monitors the implementation status of care plans and issues alerts if abnormalities occur. It monitors changes in health status in real time and issues alerts if abnormalities occur. It monitors the progress of care plans in real time and notifies caregivers and family members as needed. Based on these monitoring results, the monitoring department can also propose revisions and improvements to care plans. For example, it provides feedback to the proposal department based on the implementation status of care plans and changes in health status and proposes revisions and improvements to care plans. Furthermore, the monitoring department can collect feedback from care recipients and caregivers, enabling continuous improvement of the accuracy and effectiveness of monitoring. This allows the monitoring department to monitor the progress of care plans in real time and respond quickly if any abnormalities occur. In addition, the monitoring department can propose revisions and improvements to care plans, supporting the implementation of optimal care plans based on the health status and lifestyle needs of care recipients.

[0066] The data collection unit can collect vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily living activities. For example, the data collection unit can collect heart rate data of care recipients using a heart rate sensor. For example, the data collection unit can attach a heart rate sensor to the care recipient's wrist and collect heart rate data in real time. The data collection unit can also collect blood pressure data of care recipients using a medical device that measures blood pressure. For example, the data collection unit can attach a blood pressure monitor to the care recipient's upper arm and collect blood pressure data periodically. Furthermore, the data collection unit can collect body temperature data of care recipients using a body temperature sensor. For example, the data collection unit can attach a thermometer to the care recipient's forehead and collect body temperature data. This allows the data collection unit to understand the care recipient's detailed health status and lifestyle needs. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the heart rate sensor into a generating AI, which can analyze the data to detect fluctuations in heart rate.

[0067] The analysis unit can analyze the collected data to determine the health status and lifestyle needs of care recipients. For example, the analysis unit can detect changes in health status from fluctuations in heart rate and blood pressure. For example, the analysis unit can analyze heart rate data to detect abnormal fluctuations in heart rate. The analysis unit can also analyze blood pressure data to detect abnormal fluctuations in blood pressure. Furthermore, the analysis unit can analyze body temperature data to detect abnormal fluctuations in body temperature. For example, the analysis unit can analyze heart rate data in time series to detect abnormal fluctuations in heart rate. For blood pressure data, it can analyze daily fluctuations to detect abnormal fluctuations in blood pressure. For body temperature data, it can analyze hourly fluctuations to detect abnormal fluctuations in body temperature. This allows the analysis unit to accurately determine the health status and lifestyle needs of care recipients. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can analyze the data to determine the health status and lifestyle needs.

[0068] The proposal unit can propose an optimal care plan based on the analysis results. For example, the proposal unit proposes an individualized care plan based on the care recipient's health condition and lifestyle needs. For example, the proposal unit modifies the care plan in response to changes in health condition. For example, the proposal unit modifies the care plan in response to fluctuations in heart rate. The proposal unit can also modify the care plan in response to fluctuations in blood pressure. Furthermore, the proposal unit can also modify the care plan in response to fluctuations in body temperature. For example, the proposal unit proposes a care plan that adjusts the amount of exercise in response to fluctuations in heart rate. The proposal unit proposes a care plan that adjusts the content of meals in response to fluctuations in blood pressure. The proposal unit proposes a care plan that adjusts rest time in response to fluctuations in body temperature. In this way, the proposal unit can propose an optimal care plan for the care recipient. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the analysis results into a generating AI, and the generating AI can propose an optimal care plan.

[0069] The monitoring unit can monitor the progress of the proposed care plan in real time and take action as needed. For example, the monitoring unit monitors the implementation status of the care plan and changes in health status. For example, the monitoring unit monitors the implementation status of the care plan in real time and takes action as needed. For example, the monitoring unit monitors the implementation status of the care plan and issues an alert if an abnormality occurs. The monitoring unit can also monitor changes in health status in real time and issue an alert if an abnormality occurs. Furthermore, the monitoring unit can monitor the progress of the care plan in real time and notify caregivers and family members as needed. For example, the monitoring unit monitors the implementation status of the care plan and issues an alert if an abnormality occurs. The monitoring unit monitors changes in health status in real time and issues an alert if an abnormality occurs. The monitoring unit monitors the progress of the care plan in real time and notifies caregivers and family members as needed. This allows the monitoring unit to monitor the progress of the care plan in real time and respond quickly. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit inputs progress data from care plans into a generating AI, which then monitors the data in real time and takes action as needed.

[0070] The data collection unit can estimate the emotions of the care recipient and adjust the frequency of data collection based on the estimated emotions. For example, if the care recipient is stressed, the data collection unit reduces the frequency of data collection to alleviate the burden. For example, if the care recipient is relaxed, the data collection unit increases the frequency of data collection to collect more detailed data. For example, if the care recipient is in a hurry, the data collection unit minimizes the frequency of data collection to quickly obtain the necessary data. In this way, the data collection unit can reduce its burden by adjusting the frequency of data collection according to the emotions of the care recipient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the care recipient's emotion data into the generative AI, which can estimate the emotions and adjust the frequency of data collection.

[0071] The data collection unit can analyze the past health data of care recipients and select the optimal data collection method. For example, the data collection unit can concentrate data collection during specific time periods based on the care recipient's past health data. For example, the data collection unit can intensify data collection during periods of significant fluctuation in specific vital signs based on the care recipient's past health data. For example, the data collection unit can analyze the care recipient's past health data and select the most efficient data collection method. This enables efficient data collection by allowing the data collection unit to select the optimal data collection method based on past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI, which can then select the optimal data collection method.

[0072] The data collection unit can filter data based on the care recipient's living environment and activity patterns during data collection. For example, the data collection unit adjusts the timing of data collection according to the care recipient's living environment. For example, the data collection unit filters out unnecessary data based on the care recipient's activity patterns. For example, the data collection unit selects the optimal data collection method considering the care recipient's living environment and activity patterns. As a result, the data collection unit can eliminate unnecessary data by filtering data based on living environment and activity patterns, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on living environment and activity patterns into a generating AI, which can then perform the filtering.

[0073] The data collection unit can estimate the emotions of the care recipient and determine the priority of data to collect based on the estimated emotions. For example, if the care recipient is stressed, the data collection unit will prioritize collecting important vital sign data. For example, if the care recipient is relaxed, the data collection unit will prioritize collecting detailed activity data. For example, if the care recipient is in a hurry, the data collection unit will prioritize collecting only the most important data. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data to collect according to the emotions of the care recipient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the care recipient's emotion data into a generative AI, which can estimate emotions and determine the priority of data to collect.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of care recipients during data collection. For example, if a care recipient is in a specific location, the data collection unit will prioritize the collection of data related to that location. For example, the data collection unit will collect the most relevant data based on the geographical location information of care recipients. For example, if a care recipient is on the move, the data collection unit will prioritize the collection of data related to their destination. This enables efficient data collection by prioritizing the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0075] The data collection unit can analyze the social media activities of care recipients and collect relevant data during data collection. For example, the data collection unit can collect information related to health status from the social media activities of care recipients. For example, the data collection unit can analyze the social media activities of care recipients and collect data related to their living needs. For example, the data collection unit can select the optimal data collection method based on the social media activities of care recipients. This allows the data collection unit to more accurately understand the living needs of care recipients by collecting relevant data based on their social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI, and the generating AI can collect relevant data.

[0076] The analysis unit can estimate the emotions of care recipients and adjust the presentation of the analysis based on the estimated emotions. For example, if the care recipient is stressed, the analysis unit provides simple and easy-to-understand analysis results. For example, if the care recipient is relaxed, the analysis unit provides detailed analysis results. For example, if the care recipient is in a hurry, the analysis unit provides concise analysis results. In this way, the analysis unit can provide easy-to-understand analysis results by adjusting the presentation of the analysis according to the emotions of the care recipient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the care recipient's emotion data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit performs a detailed analysis on important vital sign data. The analysis unit adjusts the level of detail of the analysis according to the importance of the health data. For example, the analysis unit performs a simplified analysis on less important data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis according to the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, which can then adjust the level of detail of the analysis based on the importance of the data.

[0078] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit may apply a specific analysis algorithm to vital sign data. For example, the analysis unit may apply a different analysis algorithm to daily life activity data. For example, the analysis unit may select the optimal analysis algorithm depending on the category of health data. This enables accurate analysis by applying the optimal analysis algorithm according to the category of health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, which can then apply the optimal analysis algorithm according to the data category.

[0079] The analysis unit can estimate the emotions of the care recipient and adjust the length of the analysis based on the estimated emotions. For example, if the care recipient is stressed, the analysis unit will provide a short, concise analysis. For example, if the care recipient is relaxed, the analysis unit will provide a detailed analysis. For example, if the care recipient is in a hurry, the analysis unit will provide a rapid analysis. In this way, the analysis unit can provide rapid and appropriate analysis results by adjusting the length of the analysis according to the care recipient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the care recipient's emotion data into a generative AI, which can estimate the emotions and adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of the most recent health data. The analysis unit may determine the priority of analysis based on when the health data was collected. For example, the analysis unit may perform a simplified analysis on older data. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on when the health data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input health data into a generating AI, which can then determine the priority of analysis based on when the data was collected.

[0081] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit prioritizes the analysis of the most relevant data based on the relevance of the health data. The analysis unit adjusts the order of analysis based on the relevance of the health data. For example, the analysis unit performs a simplified analysis on less relevant data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, which can then adjust the order of analysis based on the relevance of the data.

[0082] The suggestion unit can estimate the emotions of the care recipient and adjust the way the suggestion is presented based on the estimated emotions. For example, if the care recipient is stressed, the suggestion unit will provide a simple and easily understandable suggestion. For example, if the care recipient is relaxed, the suggestion unit will provide a detailed suggestion. For example, if the care recipient is in a hurry, the suggestion unit will provide a concise suggestion. In this way, the suggestion unit can provide easily understandable suggestions by adjusting the way the suggestion is presented according to the care recipient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the care recipient's emotion data into a generative AI, which can estimate the emotions and adjust the way the suggestion is presented.

[0083] The proposal unit can adjust the level of detail in its proposals based on the importance of the care plan. For example, the proposal unit can provide detailed proposals for important care plans. The proposal unit can adjust the level of detail in its proposals according to the importance of the care plan. For example, the proposal unit can provide simplified proposals for less important care plans. This allows the proposal unit to provide efficient proposals by adjusting the level of detail in its proposals according to the importance of the care plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input care plan data into a generating AI, which can then adjust the level of detail in its proposals based on the importance of the plan.

[0084] The proposal unit can apply different proposal algorithms depending on the category of the care plan when making a proposal. For example, the proposal unit applies a specific proposal algorithm to a care plan related to health management. For example, the proposal unit applies a different proposal algorithm to a care plan related to support for daily living. For example, the proposal unit selects the optimal proposal algorithm depending on the category of the care plan. This enables the proposal unit to make accurate proposals by applying the optimal proposal algorithm according to the category of the care plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input care plan data into a generating AI, and the generating AI can apply the optimal proposal algorithm according to the category of the plan.

[0085] The suggestion unit can estimate the emotions of the care recipient and adjust the length of the suggestion based on the estimated emotions. For example, if the care recipient is stressed, the suggestion unit will provide a short, concise suggestion. For example, if the care recipient is relaxed, the suggestion unit will provide a detailed suggestion. For example, if the care recipient is in a hurry, the suggestion unit will provide a quick suggestion. In this way, the suggestion unit can provide quick and appropriate suggestions by adjusting the length of the suggestion according to the care recipient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the care recipient's emotion data into a generative AI, which can estimate the emotions and adjust the length of the suggestion.

[0086] The proposal department can determine the priority of proposals based on the submission date of the care plan. For example, the proposal department will prioritize the most recent care plan. The proposal department will determine the priority of proposals based on the submission date of the care plan. For example, the proposal department will make simplified proposals for older care plans. This allows the proposal department to prioritize the most recent plan by determining the priority of proposals based on the submission date of the care plan. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input care plan data into a generating AI, which can then determine the priority of proposals based on the submission date of the plan.

[0087] The proposal unit can adjust the order of proposals based on the relevance of the care plans. For example, the proposal unit prioritizes the most relevant proposals based on the relevance of the care plans. The proposal unit adjusts the order of proposals based on the relevance of the care plans. For example, the proposal unit provides simplified proposals for less relevant care plans. This allows the proposal unit to make efficient proposals by adjusting the order of proposals based on the relevance of the care plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input care plan data into a generating AI, which can then adjust the order of proposals based on the relevance of the plans.

[0088] The monitoring unit can estimate the emotions of the care recipient and adjust the display method of monitoring based on the estimated emotions. For example, if the care recipient is stressed, the monitoring unit provides a simple and highly visible display method. For example, if the care recipient is relaxed, the monitoring unit provides a display method that includes detailed information. For example, if the care recipient is in a hurry, the monitoring unit provides a display method that gets straight to the point. In this way, the monitoring unit can provide highly visible monitoring results by adjusting the display method of monitoring according to the emotions of the care recipient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the care recipient's emotion data into a generative AI, which can estimate the emotions and adjust the display method of monitoring.

[0089] The monitoring unit can analyze the progress data of the care plan during monitoring and select the optimal monitoring method. For example, the monitoring unit selects the optimal monitoring method based on the progress data of the care plan. For example, the monitoring unit adjusts the frequency of monitoring based on the results of the analysis of the progress data. For example, the monitoring unit adjusts the level of detail of monitoring according to the importance of the progress data. This enables efficient monitoring by allowing the monitoring unit to select the optimal monitoring method based on the results of the analysis of the progress data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input progress data into a generating AI, and the generating AI can select the optimal monitoring method.

[0090] The monitoring unit can apply different monitoring algorithms depending on the category of the care plan during monitoring. For example, the monitoring unit applies a specific monitoring algorithm to a care plan related to health management. For example, the monitoring unit applies a different monitoring algorithm to a care plan related to support for daily living. For example, the monitoring unit selects the optimal monitoring algorithm depending on the category of the care plan. This enables accurate monitoring by applying the optimal monitoring algorithm according to the category of the care plan. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input care plan data into a generating AI, and the generating AI can apply the optimal monitoring algorithm according to the category of the plan.

[0091] The monitoring unit can estimate the emotions of care recipients and determine monitoring priorities based on the estimated emotions. For example, if the care recipient is stressed, the monitoring unit will prioritize monitoring important data. For example, if the care recipient is relaxed, the monitoring unit will prioritize monitoring detailed data. For example, if the care recipient is in a hurry, the monitoring unit will prioritize monitoring only the most important data. In this way, the monitoring unit can prioritize monitoring important data by determining monitoring priorities according to the care recipient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input the care recipient's emotion data into a generative AI, which can estimate the emotions and determine monitoring priorities.

[0092] The monitoring unit can determine monitoring priorities based on the timing of data collection for the care plan during monitoring. For example, the monitoring unit prioritizes monitoring the most recent progress data. The monitoring unit determines monitoring priorities based on the timing of data collection. For example, the monitoring unit performs simplified monitoring on older progress data. This allows the monitoring unit to prioritize monitoring the most recent data by determining monitoring priorities based on the timing of data collection. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input progress data into a generating AI, which can then determine monitoring priorities based on the timing of data collection.

[0093] The monitoring unit can adjust the order of monitoring based on the relevance of the care plan during monitoring. For example, the monitoring unit prioritizes monitoring the most relevant data based on the relevance of the care plan. The monitoring unit adjusts the order of monitoring based on the relevance of the care plan. For example, the monitoring unit performs simplified monitoring on less relevant data. This enables efficient monitoring by adjusting the order of monitoring based on the relevance of the care plan. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit inputs care plan data into a generating AI, which can then adjust the order of monitoring based on the relevance of the plan.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The care support system can also include a communication unit. This unit provides functions to facilitate communication between care recipients, caregivers, and family members. For example, the communication unit can estimate the care recipient's emotions and generate appropriate messages based on those estimates. If the care recipient is feeling stressed, it can send encouraging messages. If the care recipient is relaxed, it can provide advice to help them maintain that relaxation. Furthermore, if the care recipient is in a hurry, it can send instructions for a quick response. In this way, the communication unit can provide appropriate communication tailored to the care recipient's emotions.

[0096] The care support system can also be equipped with a reminder function. This reminder function provides reminders regarding the care recipient's schedule and health management. For example, it can notify the care recipient of medication times, regular health check times, and exercise and rehabilitation times. This allows the reminder function to support the care recipient's schedule management and promote health management.

[0097] The care support system can also include an entertainment section. This section provides entertainment content to support the mental health of care recipients. For example, the entertainment section can estimate the care recipient's emotions and recommend appropriate music or videos based on those emotions. If the care recipient is stressed, it can play relaxing music. If the care recipient is relaxed, it can recommend enjoyable videos. Furthermore, if the care recipient is in a hurry, it can provide content that can be enjoyed in a short amount of time. In this way, the entertainment section can provide entertainment tailored to the care recipient's emotions.

[0098] The care support system can also include a feedback unit. This unit collects feedback from care recipients, caregivers, and families, and uses it to improve the system. For example, the feedback unit can estimate the care recipient's emotions and analyze the content of the feedback based on those estimated emotions. If the care recipient is stressed, the unit can identify the cause of the stress and suggest solutions. If the care recipient is relaxed, the unit can collect feedback to help maintain that relaxation. Furthermore, if the care recipient is in a hurry, the unit can collect feedback to help provide a quick response. In this way, the feedback unit can collect appropriate feedback tailored to the care recipient's emotions and use it to improve the system.

[0099] The care support system can also include an education department. This department provides educational content related to caregiving to caregivers and their families. For example, it can provide content to learn appropriate care methods based on the health status and living needs of care recipients. It can also provide resources for caregivers and families to learn the latest information on caregiving. Furthermore, it can offer training programs to help caregivers and families improve their caregiving skills. This allows caregivers and families to enhance their knowledge and skills in caregiving.

[0100] The care support system can also include a relaxation section. This section provides functions to support the relaxation of care recipients. For example, it can estimate the care recipient's emotions and suggest relaxation methods based on those emotions. If the care recipient is stressed, the relaxation section can suggest deep breathing or meditation techniques. If the care recipient is relaxed, the relaxation section can suggest activities to maintain that relaxation. Furthermore, if the care recipient is in a hurry, it can suggest methods for relaxation in a short amount of time. In this way, the relaxation section can provide relaxation methods tailored to the care recipient's emotions.

[0101] The care support system can also be equipped with a monitoring unit. The monitoring unit continuously monitors the health status and living needs of care recipients and issues alerts if abnormalities are detected. For example, the monitoring unit can monitor the care recipient's vital signs and activity data in real time and detect abnormal fluctuations. It can also monitor the care recipient's living environment and activity patterns and detect abnormal behavior. Furthermore, the monitoring unit can propose appropriate responses based on the care recipient's health status and living needs. As a result, the monitoring unit can continuously monitor the care recipient's health status and living needs and respond quickly.

[0102] The care support system can also be equipped with a prediction unit. This unit predicts future changes in the care recipient's health status and living needs, and proposes an appropriate care plan. For example, the prediction unit can analyze the care recipient's past health data and predict future changes in their health status. It can also predict changes in the care recipient's living needs and propose an appropriate care plan. Furthermore, the prediction unit can estimate the care recipient's emotions and predict future emotional changes based on these estimates. This allows the prediction unit to predict future changes in the care recipient's health status and living needs and propose an appropriate care plan.

[0103] The care support system can also be equipped with an alert unit. The alert unit issues alerts to caregivers and family members when abnormalities occur in the health status or living needs of the care recipient. For example, the alert unit can notify caregivers and family members when abnormalities are detected in the care recipient's vital signs. The alert unit can also issue alerts when abnormalities are detected in the care recipient's activity data. Furthermore, the alert unit can estimate the care recipient's emotions and detect abnormalities based on those estimated emotions. This allows the alert unit to respond quickly when abnormalities occur in the care recipient's health status or living needs.

[0104] The care support system can also include a reporting function. This function generates reports on the health status and living needs of care recipients and provides them to caregivers and families. For example, the reporting function can generate regular reports based on the care recipient's vital signs and activity data. It can also provide detailed reports based on the care recipient's living environment and activity patterns. Furthermore, the reporting function can estimate the care recipient's emotions and adjust the report content based on these estimated emotions. This allows the reporting function to provide accurate information on the care recipient's health status and living needs.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The data acquisition unit collects data from sensors and medical devices. Specifically, it collects vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily living activities. For example, a heart rate sensor is attached to the care recipient's wrist to collect heart rate data in real time. A blood pressure monitor is attached to the care recipient's upper arm to collect blood pressure data periodically. A thermometer is attached to the care recipient's forehead to collect body temperature data. Step 2: The analysis unit analyzes the data collected by the data collection unit to determine the health status and lifestyle needs of care recipients. Specifically, it detects changes in health status from fluctuations in heart rate, blood pressure, and body temperature. For example, it analyzes heart rate data in a time series to detect abnormal heart rate fluctuations. It analyzes blood pressure data for daily fluctuations to detect abnormal blood pressure fluctuations. It analyzes body temperature data for hourly fluctuations to detect abnormal body temperature fluctuations. Step 3: The proposal department proposes the optimal care plan based on the analysis results obtained by the analysis department. Specifically, it proposes an individualized care plan based on the care recipient's health condition and lifestyle needs, and modifies the care plan according to changes in their health condition. For example, it proposes a care plan that adjusts the amount of exercise according to fluctuations in heart rate, a care plan that adjusts the content of meals according to fluctuations in blood pressure, and a care plan that adjusts rest time according to fluctuations in body temperature. Step 4: The monitoring department monitors the progress of the care plan proposed by the proposal department in real time. Specifically, it monitors the implementation status of the care plan and changes in health status, and issues alerts if any abnormalities occur. Furthermore, it monitors the progress of the care plan in real time and notifies caregivers and family members as needed.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses sensors and medical devices of the smart device 14 to collect vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily living activities of the care recipient. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and determine the health status and living needs of the care recipient. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose an optimal care plan based on the analysis results. The monitoring unit is implemented in the control unit 46A of the smart device 14, for example, to monitor the progress of the proposed care plan in real time and notify caregivers and family members as needed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses sensors in the smart glasses 214 and medical devices to collect vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily living activities of the care recipient. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and determine the care recipient's health status and living needs. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose an optimal care plan based on the analysis results. The monitoring unit is implemented in the control unit 46A of the smart glasses 214, for example, to monitor the progress of the proposed care plan in real time and notify caregivers and family members as needed. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses sensors and medical devices in the headset terminal 314 to collect vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily living activities of the care recipient. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and determine the care recipient's health status and living needs. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose an optimal care plan based on the analysis results. The monitoring unit is implemented in the control unit 46A of the headset terminal 314, for example, to monitor the progress of the proposed care plan in real time and notify caregivers and family members as needed. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and monitoring unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit uses sensors and medical devices on the robot 414 to collect vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily living activities of the care recipient. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to determine the care recipient's health status and living needs. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes an optimal care plan based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414, which monitors the progress of the proposed care plan in real time and notifies the caregiver or family as needed. The correspondence between each unit and the devices or control units is not limited to the example described above, and various changes are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A data acquisition unit that collects data from sensors and medical devices, The data collected by the aforementioned data collection unit is analyzed by an analysis unit to determine the health status and lifestyle needs of care recipients, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal care plan, The system includes a monitoring unit that monitors the progress of the care plan proposed by the proposal unit in real time. A system characterized by the following features. (Note 2) The aforementioned data acquisition unit is It collects vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily activities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to determine the health status and lifestyle needs of care recipients. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose the optimal care plan. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, The progress of the proposed care plan will be monitored in real time, and action will be taken as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data acquisition unit is The system estimates the emotions of care recipients and adjusts the frequency of data collection based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data acquisition unit is Analyze past health data of care recipients and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data acquisition unit is During data collection, filtering is performed based on the living environment and activity patterns of care recipients. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data acquisition unit is The system estimates the emotions of care recipients and prioritizes the data to be collected based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data acquisition unit is When collecting data, the geographical location information of care recipients is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data acquisition unit is During data collection, analyze the social media activity of care recipients and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate the emotions of care recipients and adjust the way the analysis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is The system estimates the emotions of care recipients and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the emotions of care recipients and adjust the way proposals are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the care plan. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the care plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, The system estimates the emotions of care recipients and adjusts the length of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the care plan will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance in the care plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, The system estimates the emotions of care recipients and adjusts the display method of monitoring based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, During monitoring, the progress data of the care plan is analyzed to select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, During monitoring, different monitoring algorithms are applied depending on the category of the care plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, The system estimates the emotions of care recipients and determines monitoring priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, During monitoring, prioritize monitoring based on when care plan progress data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, During monitoring, adjust the order of monitoring based on its relevance to the care plan. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data acquisition unit that collects data from sensors and medical devices, The data collected by the aforementioned data collection unit is analyzed by an analysis unit to determine the health status and lifestyle needs of care recipients, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal care plan, The system includes a monitoring unit that monitors the progress of the care plan proposed by the proposal unit in real time. A system characterized by the following features.

2. The aforementioned data acquisition unit, It collects vital signs such as heart rate, blood pressure, and body temperature, as well as data on daily activities. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to determine the health status and lifestyle needs of care recipients. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose the optimal care plan. The system according to feature 1.

5. The aforementioned monitoring unit, The progress of the proposed care plan will be monitored in real time, and action will be taken as needed. The system according to feature 1.

6. The aforementioned data acquisition unit, The system estimates the emotions of care recipients and adjusts the frequency of data collection based on these estimated emotions. The system according to feature 1.

7. The aforementioned data acquisition unit, Analyze past health data of care recipients and select the optimal data collection method. The system according to feature 1.

8. The aforementioned data acquisition unit, During data collection, filtering is performed based on the living environment and activity patterns of care recipients. The system according to feature 1.

9. The aforementioned data acquisition unit, The system estimates the emotions of care recipients and prioritizes the data to be collected based on these estimated emotions. The system according to feature 1.

10. The aforementioned data acquisition unit, When collecting data, the geographical location information of care recipients is taken into consideration to prioritize the collection of highly relevant data. The system according to feature 1.

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