System

Abstract

An object of the system according to the embodiment is to propose an individual nursing care plan based on the health condition and life needs of a care receiver and to check the progress in real time.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and a confirmation unit. The collection unit collects data from a sensor or a medical device. The analysis unit analyzes the data collected by the collection unit. The proposal part proposes an individual nursing care plan on the basis of the analysis result obtained by the analysis part. The confirmer confirms the progress of the care plan proposed by the proposer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of making it difficult to efficiently propose individual care plans based on the health condition and lifestyle needs of the person requiring care, and to check progress in real time.

[0005] The system according to the embodiment aims to propose an individual nursing care plan based on the health condition and lifestyle needs of a person requiring nursing care, and to check the progress in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a confirmation unit. The collection unit collects data from sensors or medical devices. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an individual nursing care plan based on the analysis results obtained by the analysis unit. The confirmation unit confirms the progress of the care plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment proposes an individual nursing care plan based on the health condition and lifestyle needs of the person requiring care, and can check progress in real time. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A care plan proposal system according to an embodiment of the present invention uses AI to analyze the health condition and lifestyle needs of a care recipient and propose an individual care plan. The system integrates data from sensors and medical devices, and AI sensitively detects changes in the care recipient's daily activities. The AI ​​then analyzes this data and proposes an individual care plan based on the care recipient's health condition and lifestyle needs. Caregivers and family members can check the progress of the plan in real time through a dedicated app and take action as needed. For example, the care plan proposal system collects data such as the care recipient's body temperature, heart rate, blood pressure, and activity level. For example, this data is collected in real time from a wearable device worn by the care recipient. The care plan proposal system then analyzes the collected data using AI to analyze the care recipient's health condition and lifestyle needs. For example, if the care recipient's body temperature rises, the AI ​​identifies the cause and proposes appropriate countermeasures. Furthermore, if the care recipient's activity level decreases, the AI ​​can analyze the cause and propose a rehabilitation plan. Furthermore, the care plan proposal system allows users to check the progress of the care plan in real time through a dedicated app. For example, if the health condition of a care recipient changes, a notification is sent to the app, allowing caregivers and family members to respond quickly. It is also possible to check the progress of the care plan through the app and revise the plan as necessary. This allows the care recipient and their supporter to help build a better life together. The care plan proposal system can propose an individual care plan based on the health condition and lifestyle needs of the care recipient and check the progress. For example, the care recipient can receive care tailored to their health condition and lifestyle needs, and caregivers and family members can understand the situation in real time through the dedicated app and take appropriate action. This improves the quality of life of the care recipient and reduces the burden on caregivers and family members.

[0029] A care plan proposal system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a confirmation unit. The collection unit collects data from a sensor or a medical device. Examples of the collected data include, but are not limited to, the body temperature, heart rate, blood pressure, and activity level of the care recipient. The collection unit collects data in real time using, for example, a wearable device. The collection unit can also integrate data from medical devices to detect daily changes in the care recipient. For example, the collection unit measures the body temperature of the care recipient and collects data. The collection unit can also measure the heart rate and collect data. The collection unit can also measure the blood pressure and collect data. The analysis unit analyzes the data collected by the collection unit using AI. For example, the analysis unit analyzes the data using a machine learning algorithm to identify the health condition and lifestyle needs of the care recipient. The analysis unit can also analyze the data using deep learning technology. The analysis unit can also analyze the data using natural language processing technology. For example, the analysis unit analyzes the collected body temperature data to identify the health condition of the care recipient. The analysis unit can also analyze the collected heart rate data to identify the health condition of the care recipient. The analysis unit can also analyze the collected blood pressure data to identify the health condition of the care recipient. The proposal unit proposes an individual care plan based on the analysis results obtained by the analysis unit. The proposal unit proposes an appropriate care plan, for example, based on the health condition and lifestyle needs of the care recipient. The proposal unit can also identify the cause of an increase in the body temperature of the care recipient and propose appropriate countermeasures. The proposal unit can also analyze the cause of a decrease in the activity level of the care recipient and propose a rehabilitation plan. For example, the proposal unit can propose cooling measures if the body temperature of the care recipient increases. The proposal unit can also propose a rehabilitation exercise plan if the activity level of the care recipient decreases. The proposal unit can also propose a plan to improve the diet of the care recipient. The confirmation unit checks the progress of the care plan proposed by the proposal unit. The confirmation unit checks the progress of the care plan in real time, for example, through a dedicated app.The confirmation unit can also send a notification to the app if the health condition of the care recipient changes. The confirmation unit can also check the progress of the care plan and revise the plan as necessary. For example, the confirmation unit can send a notification to the app if the body temperature of the care recipient rises. The confirmation unit can also send a notification to the app if the activity level of the care recipient decreases. The confirmation unit can also check the progress of the care plan and revise the plan as necessary. As a result, the care plan proposal system according to the embodiment can propose an individual care plan based on the health condition and lifestyle needs of the care recipient and check the progress. For example, the care recipient can receive care tailored to their health condition and lifestyle needs, and caregivers and family members can understand the situation in real time through a dedicated app and take appropriate action. This improves the quality of life of the care recipient and reduces the burden on caregivers and family members.

[0030] The collection unit can collect the body temperature, heart rate, blood pressure, activity level, and other data of the care recipient. For example, the collection unit measures the body temperature of the care recipient and collects the data. The collected data includes, but is not limited to, body temperature, heart rate, blood pressure, and activity level. The collection unit collects data in real time using, for example, a wearable device. The collection unit can also integrate data from medical devices to detect daily changes in the care recipient. For example, the collection unit measures the body temperature of the care recipient and collects the data. The collection unit can also measure the heart rate and collect the data. The collection unit can also measure the blood pressure and collect the data. This allows for a detailed understanding of the health condition of the care recipient. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the body temperature data of the care recipient to the generation AI and have the generation AI analyze the body temperature data.

[0031] The analysis unit can analyze the collected data using AI to identify the health condition and lifestyle needs of the care recipient. The analysis unit can analyze the data using, for example, a machine learning algorithm to identify the health condition and lifestyle needs of the care recipient. The analysis unit can also analyze the data using, for example, deep learning technology. The analysis unit can also analyze the data using natural language processing technology. For example, the analysis unit can analyze collected body temperature data to identify the health condition of the care recipient. The analysis unit can also analyze collected heart rate data to identify the health condition of the care recipient. The analysis unit can also analyze collected blood pressure data to identify the health condition of the care recipient. This makes it possible to accurately identify the health condition and lifestyle needs of the care recipient. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and cause the generation AI to identify the health condition and lifestyle needs.

[0032] The proposal unit can propose an individual nursing care plan based on the identified health condition and lifestyle needs. The proposal unit proposes an appropriate care plan based on, for example, the health condition and lifestyle needs of the care recipient. For example, if the body temperature of the care recipient rises, the proposal unit can identify the cause and propose appropriate countermeasures. Furthermore, if the activity level of the care recipient decreases, the proposal unit can analyze the cause and propose a rehabilitation plan. For example, if the body temperature of the care recipient rises, the proposal unit can propose cooling measures. Furthermore, if the activity level of the care recipient decreases, the proposal unit can propose a rehabilitation exercise plan. Furthermore, the proposal unit can propose a plan to improve the diet of the care recipient. This makes it possible to provide an optimal nursing care plan for the care recipient. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the identified health condition and lifestyle needs into a generation AI and cause the generation AI to propose a nursing care plan.

[0033] The confirmation unit can check the progress of the care plan in real time through a dedicated app. The confirmation unit, for example, checks the progress of the care plan in real time through a dedicated app. The confirmation unit can also send a notification to the app if the health condition of the care recipient changes, for example. The confirmation unit can also check the progress of the care plan and revise the plan as necessary. For example, the confirmation unit can send a notification to the app if the body temperature of the care recipient rises. The confirmation unit can also send a notification to the app if the activity level of the care recipient decreases. The confirmation unit can also check the progress of the care plan and revise the plan as necessary. This allows caregivers and family members to check the progress of the care plan in real time. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input progress data of the care plan to the generation AI and cause the generation AI to check the progress.

[0034] The confirmation unit can revise the plan as necessary. For example, the confirmation unit checks the progress of the care plan and revise the plan as necessary. For example, the confirmation unit can revise the plan when the health condition of the care recipient changes. The confirmation unit can also revise the plan when the life needs of the care recipient change. For example, the confirmation unit revise the plan when the body temperature of the care recipient rises. The confirmation unit can also revise the plan when the activity level of the care recipient decreases. The confirmation unit can also revise the plan when the dietary content of the care recipient changes. This allows the care plan to be flexibly adjusted. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input progress data of the care plan to the generation AI and cause the generation AI to revise the plan.

[0035] The suggestion unit can identify the cause of a rise in the body temperature of the care recipient and propose specific countermeasures. For example, when the body temperature of the care recipient rises, the suggestion unit can identify the cause and propose appropriate countermeasures. For example, when the cause of the rise in body temperature is an infectious disease, the suggestion unit can suggest visiting a medical institution. Furthermore, when the cause of the rise in body temperature is an environmental factor, the suggestion unit can also suggest cooling measures. Furthermore, when the cause of the rise in body temperature is exercise, the suggestion unit can also suggest rest. For example, when the cause of the rise in body temperature is an infectious disease, the suggestion unit can suggest visiting a medical institution. Furthermore, when the cause of the rise in body temperature is an environmental factor, the suggestion unit can also suggest cooling measures. Furthermore, when the cause of the rise in body temperature is exercise, the suggestion unit can also suggest rest. This enables a prompt and appropriate response when the body temperature rises. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input body temperature data into the generation AI and cause the generation AI to identify the cause of the rise in body temperature and propose countermeasures.

[0036] When the activity level of the care recipient decreases, the suggestion unit can analyze the cause and suggest a rehabilitation plan. For example, when the activity level of the care recipient decreases, the suggestion unit can analyze the cause and suggest an appropriate rehabilitation plan. For example, when the activity level of the care recipient decreases, the suggestion unit can suggest exercise therapy. Furthermore, when the activity level of the care recipient decreases, the suggestion unit can suggest joint rehabilitation. Furthermore, when the activity level of the care recipient decreases, the suggestion unit can suggest psychotherapy. For example, when the activity level of the care recipient decreases, the suggestion unit can suggest exercise therapy. Furthermore, when the activity level of the care recipient decreases, the suggestion unit can suggest joint rehabilitation. Furthermore, when the activity level of the care recipient decreases, the suggestion unit can suggest psychotherapy. In this way, an appropriate rehabilitation plan can be provided when the activity level of the care recipient decreases. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the proposal unit can input activity data into the generation AI and have the generation AI analyze the cause of the decrease in activity and propose a rehabilitation plan.

[0037] The collection unit can analyze the care recipient's past health data and select the most appropriate data collection method. The collection unit, for example, analyzes the care recipient's past health data and selects the optimal data collection method. The collection unit, for example, concentrates data collection during a specific time period based on the care recipient's past health data. The collection unit can also prioritize the use of specific sensors or medical devices based on the care recipient's past health data. The collection unit can also analyze the care recipient's past health data and optimize the data collection interval. For example, the collection unit concentrates data collection during a specific time period based on the care recipient's past health data. The collection unit can also prioritize the use of specific sensors or medical devices based on the care recipient's past health data. The collection unit can also analyze the care recipient's past health data and optimize the data collection interval. This allows the optimal data collection method to be selected based on the past health data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past health data of the person requiring care into the generation AI and have the generation AI select the optimal data collection method.

[0038] The collection unit can filter data based on the living environment and activity pattern of the care recipient when collecting data. For example, the collection unit can filter data based on the living environment and activity pattern of the care recipient when collecting data. For example, when the care recipient is outdoors, the collection unit preferentially uses outdoor sensors. Furthermore, when the care recipient is active at night, the collection unit can also apply a nighttime data collection method. Furthermore, the collection unit can filter specific data according to the living environment of the care recipient and collect only necessary information. For example, when the care recipient is outdoors, the collection unit preferentially uses outdoor sensors. Furthermore, when the care recipient is active at night, the collection unit can also apply a nighttime data collection method. Furthermore, the collection unit can filter specific data according to the living environment of the care recipient and collect only necessary information. This enables data collection according to the living environment and activity pattern. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input living environment data of a person requiring care into the generation AI and have the generation AI perform filtering of the data.

[0039] The collection unit can select an optimal collection means according to the input method of the care recipient when collecting data. For example, the collection unit selects an optimal collection means according to the input method of the care recipient when collecting data. For example, if the care recipient prefers voice input, the collection unit can preferentially collect voice data. Furthermore, if the care recipient prefers text input, the collection unit can preferentially collect text data. Furthermore, if the care recipient prefers image input, the collection unit can preferentially collect image data. For example, if the care recipient prefers voice input, the collection unit can preferentially collect voice data. Furthermore, if the care recipient prefers text input, the collection unit can preferentially collect text data. Furthermore, if the care recipient prefers image input, the collection unit can preferentially collect image data. This enables data collection according to the input method of the care recipient. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the input data of the care recipient to a generation AI and cause the generation AI to select an optimal collection means.

[0040] The collection unit can prioritize collecting highly relevant data based on the geographical location information of the care recipient when collecting data. For example, the collection unit prioritizes collecting highly relevant data based on the geographical location information of the care recipient when collecting data. For example, when the care recipient is in a specific area, the collection unit prioritizes collecting health data related to the area. Furthermore, when the care recipient is traveling, the collection unit can prioritize collecting data related to the environment of the destination. Furthermore, when the care recipient is at home, the collection unit can prioritize collecting data related to the home environment. For example, when the care recipient is in a specific area, the collection unit prioritizes collecting health data related to the area. Furthermore, when the care recipient is traveling, the collection unit can prioritize collecting data related to the environment of the destination. Furthermore, when the care recipient is at home, the collection unit can prioritize collecting data related to the home environment. This enables data collection based on geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the person requiring care into the generation AI and cause the generation AI to collect highly relevant data.

[0041] The collection unit can analyze the social media activities of the care recipient and collect related data when collecting data. For example, the collection unit analyzes the social media activities of the care recipient and collects related data when collecting data. For example, the collection unit collects related health data based on the social media activities of the care recipient. The collection unit can also analyze the social media posts of the care recipient and collect related data. The collection unit can also collect related data by referring to the activities of the care recipient's friends on social media. For example, the collection unit collects related health data based on the social media activities of the care recipient. The collection unit can also analyze the social media posts of the care recipient and collect related data. The collection unit can also collect related data by referring to the activities of the care recipient's friends on social media. This makes it possible to collect data based on social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the social media data of the care recipient to the generation AI and cause the generation AI to collect related data.

[0042] The collection unit can customize the collection method by reflecting the care recipient's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the care recipient's past feedback when collecting data. For example, the collection unit adjusts the data collection method based on feedback provided in the past by the care recipient. The collection unit can also prioritize the use of a specific data collection means based on the care recipient's past feedback. The collection unit can also optimize the interval and frequency of data collection by reflecting the care recipient's past feedback. For example, the collection unit adjusts the data collection method based on feedback provided in the past by the care recipient. The collection unit can also prioritize the use of a specific data collection means based on the care recipient's past feedback. The collection unit can also optimize the data collection interval and frequency by reflecting the care recipient's past feedback. This makes it possible to customize the data collection method based on the past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the care recipient's feedback data to the generation AI and cause the generation AI to customize the collection method.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the health data during analysis. For example, the analysis unit can perform a detailed analysis on important health data. The analysis unit can also perform a simplified analysis on less important health data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the health data. For example, the analysis unit can perform a detailed analysis on important health data. The analysis unit can also perform a simplified analysis on less important health data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the health data. This allows the level of detail of the analysis to be adjusted according to the importance of the health data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the health data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a specific analysis algorithm to heart rate data. The analysis unit can also apply a different analysis algorithm to blood pressure data. The analysis unit can also apply a different analysis algorithm to activity amount data. For example, the analysis unit can apply a specific analysis algorithm to heart rate data. The analysis unit can also apply a different analysis algorithm to blood pressure data. The analysis unit can also apply a different analysis algorithm to activity amount data. This makes it possible to apply an analysis algorithm depending on the category of health data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input health data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0045] The analysis unit can improve the accuracy of the analysis by referring to past analysis results of the care recipient during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the care recipient during analysis. For example, the analysis unit can improve the current analysis accuracy based on the past analysis results of the care recipient. The analysis unit can also optimize the analysis algorithm by referring to the past analysis results of the care recipient. The analysis unit can also minimize analysis errors by using the past analysis results of the care recipient. For example, the analysis unit can improve the current analysis accuracy based on the past analysis results of the care recipient. The analysis unit can also optimize the analysis algorithm by referring to the past analysis results of the care recipient. The analysis unit can also minimize analysis errors by using the past analysis results of the care recipient. This makes it possible to improve the current analysis accuracy based on the past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past analysis results of the care recipient to the generation AI and cause the generation AI to improve the analysis accuracy.

[0046] The analysis unit can determine the analysis priority based on when the health data was collected during analysis. For example, the analysis unit determines the analysis priority based on when the health data was collected during analysis. For example, the analysis unit prioritizes analysis of recently collected health data. The analysis unit can also determine the analysis priority by referring to past health data. The analysis unit can also dynamically adjust the order of analysis depending on when the health data was collected. For example, the analysis unit prioritizes analysis of recently collected health data. The analysis unit can also determine the analysis priority by referring to past health data. The analysis unit can also dynamically adjust the order of analysis depending on when the health data was collected. This makes it possible to determine the analysis priority based on when the health data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection date of the health data to the generation AI and cause the generation AI to determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit prioritizes analysis of highly relevant health data. The analysis unit can also postpone analysis of less relevant health data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the health data. For example, the analysis unit prioritizes analysis of highly relevant health data. The analysis unit can also postpone analysis of less relevant health data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the health data. This makes it possible to adjust the order of analysis based on the relevance of the health data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the health data to the generation AI and cause the generation AI to adjust the order of analysis.

[0048] The analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the care recipient during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the care recipient during analysis. For example, if the care recipient has specialized knowledge, the analysis unit uses detailed technical terms. Furthermore, if the care recipient does not have specialized knowledge, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the expertise level of the care recipient. For example, if the care recipient has specialized knowledge, the analysis unit uses detailed technical terms. Furthermore, if the care recipient does not have specialized knowledge, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the expertise level of the care recipient. This allows the way in which the analysis results are presented to be adjusted according to the expertise level of the care recipient. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the expertise level of the care recipient to the generation AI and cause the generation AI to adjust the use of technical terms.

[0049] The suggestion unit can adjust the level of detail of the proposal based on the importance of the health condition when making a proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the health condition when making a proposal. For example, the suggestion unit makes a detailed proposal for an important health condition. The suggestion unit can also make a simplified proposal for a health condition with a low importance. The suggestion unit can also dynamically adjust the level of detail of the proposal according to the importance of the health condition. For example, the suggestion unit makes a detailed proposal for an important health condition. The suggestion unit can also make a simplified proposal for a health condition with a low importance. The suggestion unit can also dynamically adjust the level of detail of the proposal according to the importance of the health condition. In this way, the level of detail of the proposal can be adjusted according to the importance of the health condition. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the health condition to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0050] The suggestion unit can apply different suggestion algorithms depending on the health condition category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the health condition category when making a suggestion. For example, the suggestion unit can apply a specific suggestion algorithm to heart rate data. The suggestion unit can also apply a different suggestion algorithm to blood pressure data. The suggestion unit can also apply a different suggestion algorithm to activity amount data. For example, the suggestion unit can apply a specific suggestion algorithm to heart rate data. The suggestion unit can also apply a different suggestion algorithm to blood pressure data. The suggestion unit can also apply a different suggestion algorithm to activity amount data. This makes it possible to apply a suggestion algorithm depending on the health condition category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the health condition category to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0051] The suggestion unit can improve the accuracy of the suggestion by referring to past suggestion results for the care recipient when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to past suggestion results for the care recipient when making a suggestion. For example, the suggestion unit can improve the accuracy of the current suggestion based on the past suggestion results for the care recipient. The suggestion unit can also optimize the suggestion algorithm by referring to the past suggestion results for the care recipient. The suggestion unit can also minimize an error in the suggestion by using the past suggestion results for the care recipient. For example, the suggestion unit can improve the accuracy of the current suggestion based on the past suggestion results for the care recipient. The suggestion unit can also optimize the suggestion algorithm by referring to the past suggestion results for the care recipient. The suggestion unit can also minimize an error in the suggestion by using the past suggestion results for the care recipient. This makes it possible to improve the accuracy of the current suggestion based on the past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the past suggestion results for the care recipient into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0052] The suggestion unit can determine the priority of the suggestions based on the time of change in the health condition when making the suggestions. For example, the suggestion unit determines the priority of the suggestions based on the time of change in the health condition when making the suggestions. For example, if the health condition suddenly changes, the suggestion unit prioritizes emergency response suggestions. Furthermore, if the health condition is stable, the suggestion unit can also propose a long-term care plan. Furthermore, the suggestion unit can dynamically adjust the priority of the suggestions depending on the time of change in the health condition. For example, the suggestion unit prioritizes emergency response suggestions when the health condition suddenly changes. Furthermore, the suggestion unit can also propose a long-term care plan when the health condition is stable. Furthermore, the suggestion unit can dynamically adjust the priority of the suggestions depending on the time of change in the health condition. In this way, the priority of the suggestions can be determined based on the time of change in the health condition. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the time of change in the health condition to the generation AI and cause the generation AI to determine the priority of the suggestions.

[0053] The suggestion unit can adjust the order of suggestions based on the relevance of the health conditions when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of the health conditions when making suggestions. For example, the suggestion unit prioritizes suggestions for health conditions with high relevance. The suggestion unit can also postpone suggestions for health conditions with low relevance. The suggestion unit can also dynamically adjust the order of suggestions according to the relevance of the health conditions. For example, the suggestion unit prioritizes suggestions for health conditions with high relevance. The suggestion unit can also postpone suggestions for health conditions with low relevance. The suggestion unit can also dynamically adjust the order of suggestions according to the relevance of the health conditions. This makes it possible to adjust the order of suggestions based on the relevance of the health conditions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of the health conditions to a generation AI and cause the generation AI to adjust the order of suggestions.

[0054] The suggestion unit can adjust the use of technical terminology in the proposal according to the expertise level of the care recipient when making a proposal. For example, the suggestion unit adjusts the use of technical terminology in the proposal according to the expertise level of the care recipient when making a proposal. For example, if the care recipient has specialized knowledge, the suggestion unit uses detailed technical terminology. Furthermore, if the care recipient does not have specialized knowledge, the suggestion unit can make the proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the expertise level of the care recipient. For example, if the care recipient has specialized knowledge, the suggestion unit uses detailed technical terminology. Furthermore, if the care recipient does not have specialized knowledge, the suggestion unit can make the proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the expertise level of the care recipient. In this way, the way the proposal is expressed according to the expertise level of the care recipient can be adjusted. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the expertise level of the care recipient to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0055] The confirmation unit can select an optimal confirmation method by referring to the past progress history of the care recipient at the time of confirmation. For example, the confirmation unit selects an optimal confirmation method by referring to the past progress history of the care recipient at the time of confirmation. For example, the confirmation unit optimizes the current progress confirmation method based on the past progress history of the care recipient. The confirmation unit can also adjust the confirmation method by referring to the past progress history of the care recipient. The confirmation unit can also improve the accuracy of the progress confirmation by using the past progress history of the care recipient. For example, the confirmation unit optimizes the current progress confirmation method based on the past progress history of the care recipient. The confirmation unit can also adjust the confirmation method by referring to the past progress history of the care recipient. The confirmation unit can also improve the accuracy of the progress confirmation by using the past progress history of the care recipient. This makes it possible to select an optimal confirmation method based on the past progress history. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the past progress history of the care recipient to the generation AI and cause the generation AI to select an optimal confirmation method.

[0056] The confirmation unit can customize the confirmation content based on the current health condition of the care recipient at the time of confirmation. For example, the confirmation unit customizes the confirmation content based on the current health condition of the care recipient at the time of confirmation. For example, the confirmation unit adjusts the progress confirmation content based on the current health condition of the care recipient. The confirmation unit can also customize the confirmation content according to the current health condition of the care recipient. The confirmation unit can also optimize the progress confirmation method taking into account the current health condition of the care recipient. For example, the confirmation unit adjusts the progress confirmation content based on the current health condition of the care recipient. The confirmation unit can also customize the confirmation content according to the current health condition of the care recipient. The confirmation unit can also optimize the progress confirmation method taking into account the current health condition of the care recipient. This makes it possible to customize the confirmation content based on the current health condition. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit may input health condition data of the care recipient to the generation AI and cause the generation AI to customize the confirmation content.

[0057] The confirmation unit can improve the confirmation method by reflecting feedback from the care recipient during confirmation. For example, the confirmation unit improves the confirmation method by reflecting feedback from the care recipient during confirmation. For example, the confirmation unit improves the progress confirmation method based on feedback from the care recipient. The confirmation unit can also adjust the confirmation method by referring to feedback from the care recipient. The confirmation unit can also improve the accuracy of progress confirmation by using feedback from the care recipient. For example, the confirmation unit improves the progress confirmation method based on feedback from the care recipient. The confirmation unit can also adjust the confirmation method by referring to feedback from the care recipient. The confirmation unit can also improve the accuracy of progress confirmation by using feedback from the care recipient. This makes it possible to improve the confirmation method based on feedback from the care recipient. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input feedback data from the care recipient to the generation AI and cause the generation AI to improve the confirmation method.

[0058] The confirmation unit can select an optimal confirmation method in consideration of the geographical location information of the care recipient at the time of confirmation. For example, the confirmation unit selects an optimal confirmation method in consideration of the geographical location information of the care recipient at the time of confirmation. For example, if the care recipient is in a specific area, the confirmation unit provides a progress confirmation method related to the area. Furthermore, if the care recipient is traveling, the confirmation unit can also provide a progress confirmation method related to the environment of the destination. Furthermore, if the care recipient is at home, the confirmation unit can also provide a progress confirmation method related to the home environment. For example, if the care recipient is in a specific area, the confirmation unit provides a progress confirmation method related to the area. Furthermore, if the care recipient is traveling, the confirmation unit can also provide a progress confirmation method related to the environment of the destination. Furthermore, if the care recipient is at home, the confirmation unit can also provide a progress confirmation method related to the home environment. This makes it possible to select an optimal confirmation method based on the geographical location information. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the geographical location information of the person requiring care into the generation AI and have the generation AI select the optimal confirmation method.

[0059] The confirmation unit can analyze the social media activities of the care recipient and suggest confirmation content during confirmation. For example, the confirmation unit analyzes the social media activities of the care recipient and suggests confirmation content during confirmation. For example, the confirmation unit can suggest related progress confirmation content based on the social media activities of the care recipient. The confirmation unit can also analyze the social media posts of the care recipient and suggest related progress confirmation content. The confirmation unit can also suggest related progress confirmation content by referring to the activities of the care recipient's friends on social media. For example, the confirmation unit can suggest related progress confirmation content based on the social media activities of the care recipient. The confirmation unit can also analyze the social media posts of the care recipient and suggest related progress confirmation content. The confirmation unit can also suggest related progress confirmation content by referring to the activities of the care recipient's friends on social media. This makes it possible to suggest confirmation content based on social media activities. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the social media data of the care recipient to a generation AI and cause the generation AI to suggest confirmation content.

[0060] The confirmation unit can customize the confirmation method by reflecting the care recipient's past feedback during confirmation. For example, the confirmation unit customizes the confirmation method by reflecting the care recipient's past feedback during confirmation. For example, the confirmation unit adjusts the progress confirmation method based on the care recipient's past feedback. The confirmation unit can also prioritize the use of a specific confirmation means based on the care recipient's past feedback. The confirmation unit can also optimize the interval and frequency of progress confirmation by reflecting the care recipient's past feedback. For example, the confirmation unit adjusts the progress confirmation method based on the care recipient's past feedback. The confirmation unit can also prioritize the use of a specific confirmation means based on the care recipient's past feedback. The confirmation unit can also optimize the interval and frequency of progress confirmation by reflecting the care recipient's past feedback. This makes it possible to customize the confirmation method based on past feedback. Some or all of the above-described processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the care recipient's feedback data into the generation AI and cause the generation AI to customize the confirmation method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The collection unit can analyze the past health data of the care recipient and select the most appropriate data collection method. For example, the collection unit can concentrate data collection during a specific time period based on the past health data of the care recipient. The collection unit can also prioritize the use of specific sensors or medical devices based on the past health data of the care recipient. Furthermore, the collection unit can analyze the past health data of the care recipient and optimize the intervals between data collections. This allows the optimal data collection method to be selected based on the past health data.

[0063] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, a detailed analysis can be performed on important health data. A simplified analysis can also be performed on less important health data. Furthermore, the level of detail of the analysis can be dynamically adjusted according to the importance of the health data. This allows for the provision of a more effective care plan by adjusting the level of detail of the analysis according to the importance of the health data.

[0064] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the health condition. For example, a detailed proposal can be made for an important health condition. A simplified proposal can also be made for a less important health condition. Furthermore, the level of detail of the proposal can be dynamically adjusted according to the importance of the health condition. This allows for the provision of a more effective care plan by adjusting the level of detail of the proposal according to the importance of the health condition.

[0065] When checking, the checking unit can select the optimal checking method by referring to the past progress history of the care recipient. For example, the current progress checking method can be optimized based on the past progress history of the care recipient. The checking unit can also adjust the checking method by referring to the past progress history of the care recipient. Furthermore, the accuracy of progress checking can be improved by using the past progress history of the care recipient. This allows for more effective management of care plans by selecting the optimal checking method based on the past progress history.

[0066] When collecting data, the collection unit can filter the data based on the living environment and activity patterns of the care recipient. For example, if the care recipient is outdoors, an outdoor sensor can be used preferentially. Also, if the care recipient is active at night, a nighttime data collection method can be applied. Furthermore, specific data can be filtered according to the living environment of the care recipient, and only necessary information can be collected. This makes it possible to collect data according to the living environment and activity patterns.

[0067] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of changes in the health condition. For example, if the health condition suddenly changes, priority is given to proposals for emergency responses. Also, if the health condition is stable, a long-term care plan can be proposed. Furthermore, the priority of the proposal can be dynamically adjusted depending on the timing of changes in the health condition. In this way, by determining the priority of the proposal based on the timing of changes in the health condition, a more effective care plan can be provided.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects data from sensors or medical devices. The collected data includes, for example, the body temperature, heart rate, blood pressure, and activity level of the care recipient. The collection unit collects data in real time using a wearable device. The collection unit can also integrate data from medical devices to detect daily changes in the care recipient. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit. The analysis unit uses machine learning algorithms, deep learning technology, and natural language processing technology to analyze the data and identify the health condition and lifestyle needs of the person requiring care. Step 3: The proposal unit proposes an individual nursing care plan based on the analysis results obtained by the analysis unit. The proposal unit proposes an appropriate care plan based on the health condition and lifestyle needs of the person requiring care, such as cooling measures if body temperature rises or a rehabilitation exercise plan if activity levels decrease. Step 4: The confirmation unit checks the progress of the care plan proposed by the proposal unit. The confirmation unit checks the progress of the care plan in real time through a dedicated app and sends a notification to the app if there is a change in the health condition of the person requiring care. The confirmation unit can also check the progress of the care plan and revise the plan as necessary.

[0070] (Example 2) A care plan proposal system according to an embodiment of the present invention uses AI to analyze the health condition and lifestyle needs of a care recipient and propose an individual care plan. The system integrates data from sensors and medical devices, and AI sensitively detects changes in the care recipient's daily activities. The AI ​​then analyzes this data and proposes an individual care plan based on the care recipient's health condition and lifestyle needs. Caregivers and family members can check the progress of the plan in real time through a dedicated app and take action as needed. For example, the care plan proposal system collects data such as the care recipient's body temperature, heart rate, blood pressure, and activity level. For example, this data is collected in real time from a wearable device worn by the care recipient. The care plan proposal system then analyzes the collected data using AI to analyze the care recipient's health condition and lifestyle needs. For example, if the care recipient's body temperature rises, the AI ​​identifies the cause and proposes appropriate countermeasures. Furthermore, if the care recipient's activity level decreases, the AI ​​can analyze the cause and propose a rehabilitation plan. Furthermore, the care plan proposal system allows users to check the progress of the care plan in real time through a dedicated app. For example, if the health condition of a care recipient changes, a notification is sent to the app, allowing caregivers and family members to respond quickly. It is also possible to check the progress of the care plan through the app and revise the plan as necessary. This allows the care recipient and their supporter to help build a better life together. The care plan proposal system can propose an individual care plan based on the health condition and lifestyle needs of the care recipient and check the progress. For example, the care recipient can receive care tailored to their health condition and lifestyle needs, and caregivers and family members can understand the situation in real time through the dedicated app and take appropriate action. This improves the quality of life of the care recipient and reduces the burden on caregivers and family members.

[0071] A care plan proposal system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a confirmation unit. The collection unit collects data from a sensor or a medical device. Examples of the collected data include, but are not limited to, the body temperature, heart rate, blood pressure, and activity level of the care recipient. The collection unit collects data in real time using, for example, a wearable device. The collection unit can also integrate data from medical devices to detect daily changes in the care recipient. For example, the collection unit measures the body temperature of the care recipient and collects data. The collection unit can also measure the heart rate and collect data. The collection unit can also measure the blood pressure and collect data. The analysis unit analyzes the data collected by the collection unit using AI. For example, the analysis unit analyzes the data using a machine learning algorithm to identify the health condition and lifestyle needs of the care recipient. The analysis unit can also analyze the data using deep learning technology. The analysis unit can also analyze the data using natural language processing technology. For example, the analysis unit analyzes the collected body temperature data to identify the health condition of the care recipient. The analysis unit can also analyze the collected heart rate data to identify the health condition of the care recipient. The analysis unit can also analyze the collected blood pressure data to identify the health condition of the care recipient. The proposal unit proposes an individual care plan based on the analysis results obtained by the analysis unit. The proposal unit proposes an appropriate care plan, for example, based on the health condition and lifestyle needs of the care recipient. The proposal unit can also identify the cause of an increase in the body temperature of the care recipient and propose appropriate countermeasures. The proposal unit can also analyze the cause of a decrease in the activity level of the care recipient and propose a rehabilitation plan. For example, the proposal unit can propose cooling measures if the body temperature of the care recipient increases. The proposal unit can also propose a rehabilitation exercise plan if the activity level of the care recipient decreases. The proposal unit can also propose a plan to improve the diet of the care recipient. The confirmation unit checks the progress of the care plan proposed by the proposal unit. The confirmation unit checks the progress of the care plan in real time, for example, through a dedicated app.The confirmation unit can also send a notification to the app if the health condition of the care recipient changes. The confirmation unit can also check the progress of the care plan and revise the plan as necessary. For example, the confirmation unit can send a notification to the app if the body temperature of the care recipient rises. The confirmation unit can also send a notification to the app if the activity level of the care recipient decreases. The confirmation unit can also check the progress of the care plan and revise the plan as necessary. As a result, the care plan proposal system according to the embodiment can propose an individual care plan based on the health condition and lifestyle needs of the care recipient and check the progress. For example, the care recipient can receive care tailored to their health condition and lifestyle needs, and caregivers and family members can understand the situation in real time through a dedicated app and take appropriate action. This improves the quality of life of the care recipient and reduces the burden on caregivers and family members.

[0072] The collection unit can collect the body temperature, heart rate, blood pressure, activity level, and other data of the care recipient. For example, the collection unit measures the body temperature of the care recipient and collects the data. The collected data includes, but is not limited to, body temperature, heart rate, blood pressure, and activity level. The collection unit collects data in real time using, for example, a wearable device. The collection unit can also integrate data from medical devices to detect daily changes in the care recipient. For example, the collection unit measures the body temperature of the care recipient and collects the data. The collection unit can also measure the heart rate and collect the data. The collection unit can also measure the blood pressure and collect the data. This allows for a detailed understanding of the health condition of the care recipient. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the body temperature data of the care recipient to the generation AI and have the generation AI analyze the body temperature data.

[0073] The analysis unit can analyze the collected data using AI to identify the health condition and lifestyle needs of the care recipient. The analysis unit can analyze the data using, for example, a machine learning algorithm to identify the health condition and lifestyle needs of the care recipient. The analysis unit can also analyze the data using, for example, deep learning technology. The analysis unit can also analyze the data using natural language processing technology. For example, the analysis unit can analyze collected body temperature data to identify the health condition of the care recipient. The analysis unit can also analyze collected heart rate data to identify the health condition of the care recipient. The analysis unit can also analyze collected blood pressure data to identify the health condition of the care recipient. This makes it possible to accurately identify the health condition and lifestyle needs of the care recipient. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and cause the generation AI to identify the health condition and lifestyle needs.

[0074] The proposal unit can propose an individual nursing care plan based on the identified health condition and lifestyle needs. The proposal unit proposes an appropriate care plan based on, for example, the health condition and lifestyle needs of the care recipient. For example, if the body temperature of the care recipient rises, the proposal unit can identify the cause and propose appropriate countermeasures. Furthermore, if the activity level of the care recipient decreases, the proposal unit can analyze the cause and propose a rehabilitation plan. For example, if the body temperature of the care recipient rises, the proposal unit can propose cooling measures. Furthermore, if the activity level of the care recipient decreases, the proposal unit can propose a rehabilitation exercise plan. Furthermore, the proposal unit can propose a plan to improve the diet of the care recipient. This makes it possible to provide an optimal nursing care plan for the care recipient. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the identified health condition and lifestyle needs into a generation AI and cause the generation AI to propose a nursing care plan.

[0075] The confirmation unit can check the progress of the care plan in real time through a dedicated app. The confirmation unit, for example, checks the progress of the care plan in real time through a dedicated app. The confirmation unit can also send a notification to the app if the health condition of the care recipient changes, for example. The confirmation unit can also check the progress of the care plan and revise the plan as necessary. For example, the confirmation unit can send a notification to the app if the body temperature of the care recipient rises. The confirmation unit can also send a notification to the app if the activity level of the care recipient decreases. The confirmation unit can also check the progress of the care plan and revise the plan as necessary. This allows caregivers and family members to check the progress of the care plan in real time. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input progress data of the care plan to the generation AI and cause the generation AI to check the progress.

[0076] The confirmation unit can revise the plan as necessary. For example, the confirmation unit checks the progress of the care plan and revise the plan as necessary. For example, the confirmation unit can revise the plan when the health condition of the care recipient changes. The confirmation unit can also revise the plan when the life needs of the care recipient change. For example, the confirmation unit revise the plan when the body temperature of the care recipient rises. The confirmation unit can also revise the plan when the activity level of the care recipient decreases. The confirmation unit can also revise the plan when the dietary content of the care recipient changes. This allows the care plan to be flexibly adjusted. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input progress data of the care plan to the generation AI and cause the generation AI to revise the plan.

[0077] The suggestion unit can identify the cause of a rise in the body temperature of the care recipient and propose specific countermeasures. For example, when the body temperature of the care recipient rises, the suggestion unit can identify the cause and propose appropriate countermeasures. For example, when the cause of the rise in body temperature is an infectious disease, the suggestion unit can suggest visiting a medical institution. Furthermore, when the cause of the rise in body temperature is an environmental factor, the suggestion unit can also suggest cooling measures. Furthermore, when the cause of the rise in body temperature is exercise, the suggestion unit can also suggest rest. For example, when the cause of the rise in body temperature is an infectious disease, the suggestion unit can suggest visiting a medical institution. Furthermore, when the cause of the rise in body temperature is an environmental factor, the suggestion unit can also suggest cooling measures. Furthermore, when the cause of the rise in body temperature is exercise, the suggestion unit can also suggest rest. This enables a prompt and appropriate response when the body temperature rises. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input body temperature data into the generation AI and cause the generation AI to identify the cause of the rise in body temperature and propose countermeasures.

[0078] When the activity level of the care recipient decreases, the suggestion unit can analyze the cause and suggest a rehabilitation plan. For example, when the activity level of the care recipient decreases, the suggestion unit can analyze the cause and suggest an appropriate rehabilitation plan. For example, when the activity level of the care recipient decreases, the suggestion unit can suggest exercise therapy. Furthermore, when the activity level of the care recipient decreases, the suggestion unit can suggest joint rehabilitation. Furthermore, when the activity level of the care recipient decreases, the suggestion unit can suggest psychotherapy. For example, when the activity level of the care recipient decreases, the suggestion unit can suggest exercise therapy. Furthermore, when the activity level of the care recipient decreases, the suggestion unit can suggest joint rehabilitation. Furthermore, when the activity level of the care recipient decreases, the suggestion unit can suggest psychotherapy. In this way, an appropriate rehabilitation plan can be provided when the activity level of the care recipient decreases. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the proposal unit can input activity data into the generation AI and have the generation AI analyze the cause of the decrease in activity and propose a rehabilitation plan.

[0079] The collection unit can estimate the emotion of the care recipient and adjust the frequency of data collection based on the estimated emotion of the care recipient. For example, the collection unit can estimate the emotion of the care recipient and adjust the frequency of data collection based on the estimated emotion. For example, if the care recipient is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden. Furthermore, if the care recipient is relaxed, the collection unit can increase the frequency of data collection to understand a detailed health condition. Furthermore, if the care recipient is feeling anxious, the collection unit can appropriately adjust the frequency of data collection to provide a sense of security. For example, if the care recipient is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden. Furthermore, if the care recipient is relaxed, the collection unit can increase the frequency of data collection to understand a detailed health condition. Furthermore, if the care recipient is feeling anxious, the collection unit can appropriately adjust the frequency of data collection to provide a sense of security. In this way, by adjusting the frequency of data collection according to the emotion of the care recipient, the burden can be reduced. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input emotional data of the person requiring care into the generation AI and cause the generation AI to adjust the frequency of data collection.

[0080] The collection unit can analyze the care recipient's past health data and select the most appropriate data collection method. The collection unit, for example, analyzes the care recipient's past health data and selects the optimal data collection method. The collection unit, for example, concentrates data collection during a specific time period based on the care recipient's past health data. The collection unit can also prioritize the use of specific sensors or medical devices based on the care recipient's past health data. The collection unit can also analyze the care recipient's past health data and optimize the data collection interval. For example, the collection unit concentrates data collection during a specific time period based on the care recipient's past health data. The collection unit can also prioritize the use of specific sensors or medical devices based on the care recipient's past health data. The collection unit can also analyze the care recipient's past health data and optimize the data collection interval. This allows the optimal data collection method to be selected based on the past health data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past health data of the person requiring care into the generation AI and have the generation AI select the optimal data collection method.

[0081] The collection unit can filter data based on the living environment and activity pattern of the care recipient when collecting data. For example, the collection unit can filter data based on the living environment and activity pattern of the care recipient when collecting data. For example, when the care recipient is outdoors, the collection unit preferentially uses outdoor sensors. Furthermore, when the care recipient is active at night, the collection unit can also apply a nighttime data collection method. Furthermore, the collection unit can filter specific data according to the living environment of the care recipient and collect only necessary information. For example, when the care recipient is outdoors, the collection unit preferentially uses outdoor sensors. Furthermore, when the care recipient is active at night, the collection unit can also apply a nighttime data collection method. Furthermore, the collection unit can filter specific data according to the living environment of the care recipient and collect only necessary information. This enables data collection according to the living environment and activity pattern. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input living environment data of a person requiring care into the generation AI and have the generation AI perform filtering of the data.

[0082] The collection unit can select an optimal collection means according to the input method of the care recipient when collecting data. For example, the collection unit selects an optimal collection means according to the input method of the care recipient when collecting data. For example, if the care recipient prefers voice input, the collection unit can preferentially collect voice data. Furthermore, if the care recipient prefers text input, the collection unit can preferentially collect text data. Furthermore, if the care recipient prefers image input, the collection unit can preferentially collect image data. For example, if the care recipient prefers voice input, the collection unit can preferentially collect voice data. Furthermore, if the care recipient prefers text input, the collection unit can preferentially collect text data. Furthermore, if the care recipient prefers image input, the collection unit can preferentially collect image data. This enables data collection according to the input method of the care recipient. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the input data of the care recipient to a generation AI and cause the generation AI to select an optimal collection means.

[0083] The collection unit can estimate the emotion of the care recipient and determine the priority of data to be collected based on the estimated emotion of the care recipient. For example, the collection unit estimates the emotion of the care recipient and determines the priority of data to be collected based on the estimated emotion. For example, if the care recipient is feeling stressed, the collection unit can prioritize collecting stress-related data. Furthermore, if the care recipient is relaxed, the collection unit can prioritize collecting relaxation-related data. Furthermore, if the care recipient is feeling anxious, the collection unit can prioritize collecting anxiety-related data. For example, if the care recipient is feeling stressed, the collection unit can prioritize collecting stress-related data. Furthermore, if the care recipient is relaxed, the collection unit can prioritize collecting relaxation-related data. Furthermore, if the care recipient is feeling anxious, the collection unit can prioritize collecting anxiety-related data. This makes it possible to determine the priority of data collection according to the emotion of the care recipient. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input emotion data of the care recipient to the generation AI and have the generation AI determine the priority of the data.

[0084] The collection unit can prioritize collecting highly relevant data based on the geographical location information of the care recipient when collecting data. For example, the collection unit prioritizes collecting highly relevant data based on the geographical location information of the care recipient when collecting data. For example, when the care recipient is in a specific area, the collection unit prioritizes collecting health data related to the area. Furthermore, when the care recipient is traveling, the collection unit can prioritize collecting data related to the environment of the destination. Furthermore, when the care recipient is at home, the collection unit can prioritize collecting data related to the home environment. For example, when the care recipient is in a specific area, the collection unit prioritizes collecting health data related to the area. Furthermore, when the care recipient is traveling, the collection unit can prioritize collecting data related to the environment of the destination. Furthermore, when the care recipient is at home, the collection unit can prioritize collecting data related to the home environment. This enables data collection based on geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the person requiring care into the generation AI and cause the generation AI to collect highly relevant data.

[0085] The collection unit can analyze the social media activities of the care recipient and collect related data when collecting data. For example, the collection unit analyzes the social media activities of the care recipient and collects related data when collecting data. For example, the collection unit collects related health data based on the social media activities of the care recipient. The collection unit can also analyze the social media posts of the care recipient and collect related data. The collection unit can also collect related data by referring to the activities of the care recipient's friends on social media. For example, the collection unit collects related health data based on the social media activities of the care recipient. The collection unit can also analyze the social media posts of the care recipient and collect related data. The collection unit can also collect related data by referring to the activities of the care recipient's friends on social media. This makes it possible to collect data based on social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the social media data of the care recipient to the generation AI and cause the generation AI to collect related data.

[0086] The collection unit can customize the collection method by reflecting the care recipient's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the care recipient's past feedback when collecting data. For example, the collection unit adjusts the data collection method based on feedback provided in the past by the care recipient. The collection unit can also prioritize the use of a specific data collection means based on the care recipient's past feedback. The collection unit can also optimize the interval and frequency of data collection by reflecting the care recipient's past feedback. For example, the collection unit adjusts the data collection method based on feedback provided in the past by the care recipient. The collection unit can also prioritize the use of a specific data collection means based on the care recipient's past feedback. The collection unit can also optimize the data collection interval and frequency by reflecting the care recipient's past feedback. This makes it possible to customize the data collection method based on the past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the care recipient's feedback data to the generation AI and cause the generation AI to customize the collection method.

[0087] The analysis unit can estimate the emotion of the care recipient and adjust the method of expression of the analysis based on the estimated emotion of the care recipient. For example, the analysis unit estimates the emotion of the care recipient and adjusts the method of expression of the analysis based on the estimated emotion. For example, if the care recipient is feeling stressed, the analysis unit provides a simple and highly visible analysis result. Furthermore, if the care recipient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the care recipient is feeling anxious, the analysis unit can provide an analysis result that gives a sense of security. For example, if the care recipient is feeling stressed, the analysis unit provides a simple and highly visible analysis result. Furthermore, if the care recipient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the care recipient is feeling anxious, the analysis unit can provide an analysis result that gives a sense of security. This makes it possible to adjust the method of expression of the analysis result according to the emotion of the care recipient. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input emotional data of the care recipient to the generation AI and cause the generation AI to adjust the way the analysis results are expressed.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the health data during analysis. For example, the analysis unit can perform a detailed analysis on important health data. The analysis unit can also perform a simplified analysis on less important health data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the health data. For example, the analysis unit can perform a detailed analysis on important health data. The analysis unit can also perform a simplified analysis on less important health data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the health data. This allows the level of detail of the analysis to be adjusted according to the importance of the health data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the health data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a specific analysis algorithm to heart rate data. The analysis unit can also apply a different analysis algorithm to blood pressure data. The analysis unit can also apply a different analysis algorithm to activity amount data. For example, the analysis unit can apply a specific analysis algorithm to heart rate data. The analysis unit can also apply a different analysis algorithm to blood pressure data. The analysis unit can also apply a different analysis algorithm to activity amount data. This makes it possible to apply an analysis algorithm depending on the category of health data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input health data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0090] The analysis unit can improve the accuracy of the analysis by referring to past analysis results of the care recipient during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the care recipient during analysis. For example, the analysis unit can improve the current analysis accuracy based on the past analysis results of the care recipient. The analysis unit can also optimize the analysis algorithm by referring to the past analysis results of the care recipient. The analysis unit can also minimize analysis errors by using the past analysis results of the care recipient. For example, the analysis unit can improve the current analysis accuracy based on the past analysis results of the care recipient. The analysis unit can also optimize the analysis algorithm by referring to the past analysis results of the care recipient. The analysis unit can also minimize analysis errors by using the past analysis results of the care recipient. This makes it possible to improve the current analysis accuracy based on the past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past analysis results of the care recipient to the generation AI and cause the generation AI to improve the analysis accuracy.

[0091] The analysis unit can estimate the emotion of the care recipient and adjust the length of the analysis based on the estimated emotion of the care recipient. For example, the analysis unit estimates the emotion of the care recipient and adjusts the length of the analysis based on the estimated emotion. For example, if the care recipient is feeling stressed, the analysis unit provides a short and concise analysis result. Furthermore, if the care recipient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the care recipient is feeling anxious, the analysis unit can provide an analysis result that gives a sense of security. For example, if the care recipient is feeling stressed, the analysis unit provides a short and concise analysis result. Furthermore, if the care recipient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the care recipient is feeling anxious, the analysis unit can provide an analysis result that gives a sense of security. This makes it possible to adjust the length of the analysis according to the emotion of the care recipient. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input emotion data of the care recipient to the generation AI and cause the generation AI to adjust the length of the analysis.

[0092] The analysis unit can determine the analysis priority based on when the health data was collected during analysis. For example, the analysis unit determines the analysis priority based on when the health data was collected during analysis. For example, the analysis unit prioritizes analysis of recently collected health data. The analysis unit can also determine the analysis priority by referring to past health data. The analysis unit can also dynamically adjust the order of analysis depending on when the health data was collected. For example, the analysis unit prioritizes analysis of recently collected health data. The analysis unit can also determine the analysis priority by referring to past health data. The analysis unit can also dynamically adjust the order of analysis depending on when the health data was collected. This makes it possible to determine the analysis priority based on when the health data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection date of the health data to the generation AI and cause the generation AI to determine the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit prioritizes analysis of highly relevant health data. The analysis unit can also postpone analysis of less relevant health data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the health data. For example, the analysis unit prioritizes analysis of highly relevant health data. The analysis unit can also postpone analysis of less relevant health data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the health data. This makes it possible to adjust the order of analysis based on the relevance of the health data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the health data to the generation AI and cause the generation AI to adjust the order of analysis.

[0094] The analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the care recipient during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the care recipient during analysis. For example, if the care recipient has specialized knowledge, the analysis unit uses detailed technical terms. Furthermore, if the care recipient does not have specialized knowledge, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the expertise level of the care recipient. For example, if the care recipient has specialized knowledge, the analysis unit uses detailed technical terms. Furthermore, if the care recipient does not have specialized knowledge, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the expertise level of the care recipient. This allows the way in which the analysis results are presented to be adjusted according to the expertise level of the care recipient. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the expertise level of the care recipient to the generation AI and cause the generation AI to adjust the use of technical terms.

[0095] The suggestion unit can estimate the emotion of the care recipient and adjust the way in which the suggestion is expressed based on the estimated emotion of the care recipient. For example, the suggestion unit estimates the emotion of the care recipient and adjusts the way in which the suggestion is expressed based on the estimated emotion. For example, if the care recipient is feeling stressed, the suggestion unit makes a simple and highly visible suggestion. Furthermore, if the care recipient is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the care recipient is feeling anxious, the suggestion unit can make a suggestion that gives a sense of security. For example, if the care recipient is feeling stressed, the suggestion unit makes a simple and highly visible suggestion. Furthermore, if the care recipient is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the care recipient is feeling anxious, the suggestion unit can make a suggestion that gives a sense of security. This makes it possible to adjust the way in which the suggestion is expressed based on the emotion of the care recipient. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input emotion data of the care recipient to the generation AI and cause the generation AI to adjust the way the suggestion is expressed.

[0096] The suggestion unit can adjust the level of detail of the proposal based on the importance of the health condition when making a proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the health condition when making a proposal. For example, the suggestion unit makes a detailed proposal for an important health condition. The suggestion unit can also make a simplified proposal for a health condition with a low importance. The suggestion unit can also dynamically adjust the level of detail of the proposal according to the importance of the health condition. For example, the suggestion unit makes a detailed proposal for an important health condition. The suggestion unit can also make a simplified proposal for a health condition with a low importance. The suggestion unit can also dynamically adjust the level of detail of the proposal according to the importance of the health condition. In this way, the level of detail of the proposal can be adjusted according to the importance of the health condition. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the health condition to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0097] The suggestion unit can apply different suggestion algorithms depending on the health condition category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the health condition category when making a suggestion. For example, the suggestion unit can apply a specific suggestion algorithm to heart rate data. The suggestion unit can also apply a different suggestion algorithm to blood pressure data. The suggestion unit can also apply a different suggestion algorithm to activity amount data. For example, the suggestion unit can apply a specific suggestion algorithm to heart rate data. The suggestion unit can also apply a different suggestion algorithm to blood pressure data. The suggestion unit can also apply a different suggestion algorithm to activity amount data. This makes it possible to apply a suggestion algorithm depending on the health condition category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the health condition category to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0098] The suggestion unit can improve the accuracy of the suggestion by referring to past suggestion results for the care recipient when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to past suggestion results for the care recipient when making a suggestion. For example, the suggestion unit can improve the accuracy of the current suggestion based on the past suggestion results for the care recipient. The suggestion unit can also optimize the suggestion algorithm by referring to the past suggestion results for the care recipient. The suggestion unit can also minimize an error in the suggestion by using the past suggestion results for the care recipient. For example, the suggestion unit can improve the accuracy of the current suggestion based on the past suggestion results for the care recipient. The suggestion unit can also optimize the suggestion algorithm by referring to the past suggestion results for the care recipient. The suggestion unit can also minimize an error in the suggestion by using the past suggestion results for the care recipient. This makes it possible to improve the accuracy of the current suggestion based on the past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the past suggestion results for the care recipient into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0099] The suggestion unit can estimate the emotion of the care recipient and adjust the length of the suggestion based on the estimated emotion of the care recipient. For example, the suggestion unit estimates the emotion of the care recipient and adjusts the length of the suggestion based on the estimated emotion. For example, if the care recipient is feeling stressed, the suggestion unit makes a short and to-the-point suggestion. Furthermore, if the care recipient is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the care recipient is feeling anxious, the suggestion unit can make a suggestion that gives a sense of security. For example, if the care recipient is feeling stressed, the suggestion unit makes a short and to-the-point suggestion. Furthermore, if the care recipient is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the care recipient is feeling anxious, the suggestion unit can make a suggestion that gives a sense of security. In this way, the length of the suggestion can be adjusted according to the emotion of the care recipient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input emotion data of the care recipient to the generation AI and cause the generation AI to adjust the length of the suggestion.

[0100] The suggestion unit can determine the priority of the suggestions based on the time of change in the health condition when making the suggestions. For example, the suggestion unit determines the priority of the suggestions based on the time of change in the health condition when making the suggestions. For example, if the health condition suddenly changes, the suggestion unit prioritizes emergency response suggestions. Furthermore, if the health condition is stable, the suggestion unit can also propose a long-term care plan. Furthermore, the suggestion unit can dynamically adjust the priority of the suggestions depending on the time of change in the health condition. For example, the suggestion unit prioritizes emergency response suggestions when the health condition suddenly changes. Furthermore, the suggestion unit can also propose a long-term care plan when the health condition is stable. Furthermore, the suggestion unit can dynamically adjust the priority of the suggestions depending on the time of change in the health condition. In this way, the priority of the suggestions can be determined based on the time of change in the health condition. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the time of change in the health condition to the generation AI and cause the generation AI to determine the priority of the suggestions.

[0101] The suggestion unit can adjust the order of suggestions based on the relevance of the health conditions when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of the health conditions when making suggestions. For example, the suggestion unit prioritizes suggestions for health conditions with high relevance. The suggestion unit can also postpone suggestions for health conditions with low relevance. The suggestion unit can also dynamically adjust the order of suggestions according to the relevance of the health conditions. For example, the suggestion unit prioritizes suggestions for health conditions with high relevance. The suggestion unit can also postpone suggestions for health conditions with low relevance. The suggestion unit can also dynamically adjust the order of suggestions according to the relevance of the health conditions. This makes it possible to adjust the order of suggestions based on the relevance of the health conditions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of the health conditions to a generation AI and cause the generation AI to adjust the order of suggestions.

[0102] The suggestion unit can adjust the use of technical terminology in the proposal according to the expertise level of the care recipient when making a proposal. For example, the suggestion unit adjusts the use of technical terminology in the proposal according to the expertise level of the care recipient when making a proposal. For example, if the care recipient has specialized knowledge, the suggestion unit uses detailed technical terminology. Furthermore, if the care recipient does not have specialized knowledge, the suggestion unit can make the proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the expertise level of the care recipient. For example, if the care recipient has specialized knowledge, the suggestion unit uses detailed technical terminology. Furthermore, if the care recipient does not have specialized knowledge, the suggestion unit can make the proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the expertise level of the care recipient. In this way, the way the proposal is expressed according to the expertise level of the care recipient can be adjusted. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the expertise level of the care recipient to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0103] The confirmation unit can estimate the emotion of the care recipient and adjust the progress check method based on the estimated emotion of the care recipient. For example, the confirmation unit estimates the emotion of the care recipient and adjusts the progress check method based on the estimated emotion. For example, if the care recipient is feeling stressed, the confirmation unit can provide a simple and highly visible progress check method. Furthermore, if the care recipient is relaxed, the confirmation unit can provide a detailed progress check method. Furthermore, if the care recipient is feeling anxious, the confirmation unit can provide a progress check method that gives a sense of security. For example, if the care recipient is feeling stressed, the confirmation unit can provide a simple and highly visible progress check method. Furthermore, if the care recipient is relaxed, the confirmation unit can provide a detailed progress check method. Furthermore, if the care recipient is feeling anxious, the confirmation unit can provide a progress check method that gives a sense of security. This makes it possible to adjust the progress check method according to the emotion of the care recipient. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input emotion data of the care recipient to the generation AI and cause the generation AI to adjust the progress confirmation method.

[0104] The confirmation unit can select an optimal confirmation method by referring to the past progress history of the care recipient at the time of confirmation. For example, the confirmation unit selects an optimal confirmation method by referring to the past progress history of the care recipient at the time of confirmation. For example, the confirmation unit optimizes the current progress confirmation method based on the past progress history of the care recipient. The confirmation unit can also adjust the confirmation method by referring to the past progress history of the care recipient. The confirmation unit can also improve the accuracy of the progress confirmation by using the past progress history of the care recipient. For example, the confirmation unit optimizes the current progress confirmation method based on the past progress history of the care recipient. The confirmation unit can also adjust the confirmation method by referring to the past progress history of the care recipient. The confirmation unit can also improve the accuracy of the progress confirmation by using the past progress history of the care recipient. This makes it possible to select an optimal confirmation method based on the past progress history. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the past progress history of the care recipient to the generation AI and cause the generation AI to select an optimal confirmation method.

[0105] The confirmation unit can customize the confirmation content based on the current health condition of the care recipient at the time of confirmation. For example, the confirmation unit customizes the confirmation content based on the current health condition of the care recipient at the time of confirmation. For example, the confirmation unit adjusts the progress confirmation content based on the current health condition of the care recipient. The confirmation unit can also customize the confirmation content according to the current health condition of the care recipient. The confirmation unit can also optimize the progress confirmation method taking into account the current health condition of the care recipient. For example, the confirmation unit adjusts the progress confirmation content based on the current health condition of the care recipient. The confirmation unit can also customize the confirmation content according to the current health condition of the care recipient. The confirmation unit can also optimize the progress confirmation method taking into account the current health condition of the care recipient. This makes it possible to customize the confirmation content based on the current health condition. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit may input health condition data of the care recipient to the generation AI and cause the generation AI to customize the confirmation content.

[0106] The confirmation unit can improve the confirmation method by reflecting feedback from the care recipient during confirmation. For example, the confirmation unit improves the confirmation method by reflecting feedback from the care recipient during confirmation. For example, the confirmation unit improves the progress confirmation method based on feedback from the care recipient. The confirmation unit can also adjust the confirmation method by referring to feedback from the care recipient. The confirmation unit can also improve the accuracy of progress confirmation by using feedback from the care recipient. For example, the confirmation unit improves the progress confirmation method based on feedback from the care recipient. The confirmation unit can also adjust the confirmation method by referring to feedback from the care recipient. The confirmation unit can also improve the accuracy of progress confirmation by using feedback from the care recipient. This makes it possible to improve the confirmation method based on feedback from the care recipient. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input feedback data from the care recipient to the generation AI and cause the generation AI to improve the confirmation method.

[0107] The confirmation unit can estimate the emotion of the care recipient and determine the priority of progress checks based on the estimated emotion of the care recipient. For example, the confirmation unit estimates the emotion of the care recipient and determines the priority of progress checks based on the estimated emotion. For example, if the care recipient is feeling stressed, the confirmation unit prioritizes stress-related progress checks. Furthermore, if the care recipient is relaxed, the confirmation unit can prioritize relaxation-related progress checks. Furthermore, if the care recipient is feeling anxious, the confirmation unit can prioritize anxiety-related progress checks. For example, if the care recipient is feeling stressed, the confirmation unit prioritizes stress-related progress checks. Furthermore, if the care recipient is relaxed, the confirmation unit can prioritize relaxation-related progress checks. Furthermore, if the care recipient is feeling anxious, the confirmation unit can prioritize anxiety-related progress checks. In this way, the priority of progress checks can be determined according to the emotion of the care recipient. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input emotion data of the care recipient to the generation AI and cause the generation AI to determine the priority of progress confirmation.

[0108] The confirmation unit can select an optimal confirmation method in consideration of the geographical location information of the care recipient at the time of confirmation. For example, the confirmation unit selects an optimal confirmation method in consideration of the geographical location information of the care recipient at the time of confirmation. For example, if the care recipient is in a specific area, the confirmation unit provides a progress confirmation method related to the area. Furthermore, if the care recipient is traveling, the confirmation unit can also provide a progress confirmation method related to the environment of the destination. Furthermore, if the care recipient is at home, the confirmation unit can also provide a progress confirmation method related to the home environment. For example, if the care recipient is in a specific area, the confirmation unit provides a progress confirmation method related to the area. Furthermore, if the care recipient is traveling, the confirmation unit can also provide a progress confirmation method related to the environment of the destination. Furthermore, if the care recipient is at home, the confirmation unit can also provide a progress confirmation method related to the home environment. This makes it possible to select an optimal confirmation method based on the geographical location information. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the geographical location information of the person requiring care into the generation AI and have the generation AI select the optimal confirmation method.

[0109] The confirmation unit can analyze the social media activities of the care recipient and suggest confirmation content during confirmation. For example, the confirmation unit analyzes the social media activities of the care recipient and suggests confirmation content during confirmation. For example, the confirmation unit can suggest related progress confirmation content based on the social media activities of the care recipient. The confirmation unit can also analyze the social media posts of the care recipient and suggest related progress confirmation content. The confirmation unit can also suggest related progress confirmation content by referring to the activities of the care recipient's friends on social media. For example, the confirmation unit can suggest related progress confirmation content based on the social media activities of the care recipient. The confirmation unit can also analyze the social media posts of the care recipient and suggest related progress confirmation content. The confirmation unit can also suggest related progress confirmation content by referring to the activities of the care recipient's friends on social media. This makes it possible to suggest confirmation content based on social media activities. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the social media data of the care recipient to a generation AI and cause the generation AI to suggest confirmation content.

[0110] The confirmation unit can customize the confirmation method by reflecting the care recipient's past feedback during confirmation. For example, the confirmation unit customizes the confirmation method by reflecting the care recipient's past feedback during confirmation. For example, the confirmation unit adjusts the progress confirmation method based on the care recipient's past feedback. The confirmation unit can also prioritize the use of a specific confirmation means based on the care recipient's past feedback. The confirmation unit can also optimize the interval and frequency of progress confirmation by reflecting the care recipient's past feedback. For example, the confirmation unit adjusts the progress confirmation method based on the care recipient's past feedback. The confirmation unit can also prioritize the use of a specific confirmation means based on the care recipient's past feedback. The confirmation unit can also optimize the interval and frequency of progress confirmation by reflecting the care recipient's past feedback. This makes it possible to customize the confirmation method based on past feedback. Some or all of the above-described processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the care recipient's feedback data into the generation AI and cause the generation AI to customize the confirmation method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and confirmation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data such as the body temperature, heart rate, blood pressure, and activity level of the care recipient using the camera 42 and microphone 38B of the smart device 14. The collection unit is also implemented by the specific processing unit 290 of the data processing device 12 and integrates data from sensors and medical devices. The analysis unit is also implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The proposal unit is also implemented by the specific processing unit 290 of the data processing device 12 and proposes an individual nursing care plan based on the health condition and lifestyle needs of the care recipient. The confirmation unit is also implemented by the control unit 46A of the smart device 14 and checks the progress of the care plan in real time via a dedicated app. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and confirmation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data such as the body temperature, heart rate, blood pressure, and activity level of the care recipient using the camera 42 and microphone 238 of the smart glasses 214. The collection unit is also realized by the specific processing unit 290 of the data processing device 12 and integrates data from sensors and medical devices. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an individual nursing care plan based on the health condition and lifestyle needs of the care recipient. The confirmation unit is realized, for example, by the control unit 46A of the smart glasses 214 and checks the progress of the care plan in real time via a dedicated app. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and confirmation unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data such as the body temperature, heart rate, blood pressure, and activity level of the care recipient using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit is also implemented by the specific processing unit 290 of the data processing device 12 and integrates data from sensors and medical devices. The analysis unit is also implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The proposal unit is also implemented by the specific processing unit 290 of the data processing device 12 and proposes an individual nursing care plan based on the health condition and lifestyle needs of the care recipient. The confirmation unit is also implemented by the control unit 46A of the headset-type terminal 314 and checks the progress of the care plan in real time via a dedicated app. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and confirmation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data such as the body temperature, heart rate, blood pressure, and activity level of the care recipient using the camera 42 and microphone 238 of the robot 414. The collection unit is also realized by the specific processing unit 290 of the data processing device 12 and integrates data from sensors and medical devices. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an individual nursing care plan based on the health condition and lifestyle needs of the care recipient. The confirmation unit is realized, for example, by the control unit 46A of the robot 414 and checks the progress of the care plan in real time via a dedicated app.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The analysis unit can estimate the emotions of the care recipient and determine the priority of analysis based on the estimated emotions. For example, if the care recipient is feeling stressed, stress-related data can be analyzed with priority. Also, if the care recipient is feeling relaxed, relaxation-related data can be analyzed with priority. Furthermore, if the care recipient is feeling anxious, anxiety-related data can be analyzed with priority. In this way, by determining the priority of analysis according to the emotions of the care recipient, a more appropriate care plan can be provided.

[0113] The collection unit can analyze the past health data of the care recipient and select the most appropriate data collection method. For example, the collection unit can concentrate data collection during a specific time period based on the past health data of the care recipient. The collection unit can also prioritize the use of specific sensors or medical devices based on the past health data of the care recipient. Furthermore, the collection unit can analyze the past health data of the care recipient and optimize the intervals between data collections. This allows the optimal data collection method to be selected based on the past health data.

[0114] The suggestion unit can estimate the emotions of the care recipient and adjust the way in which suggestions are expressed based on the estimated emotions. For example, if the care recipient is feeling stressed, a simple, highly visible suggestion can be made. If the care recipient is relaxed, a detailed suggestion can be made. Furthermore, if the care recipient is feeling anxious, a suggestion that gives a sense of security can be made. In this way, by adjusting the way in which suggestions are expressed according to the emotions of the care recipient, a more effective care plan can be provided.

[0115] The confirmation unit can estimate the emotions of the care recipient and adjust the progress confirmation method based on the estimated emotions. For example, if the care recipient is feeling stressed, a simple and highly visible progress confirmation method can be provided. If the care recipient is relaxed, a detailed progress confirmation method can be provided. Furthermore, if the care recipient is feeling anxious, a progress confirmation method that gives a sense of security can be provided. This makes it possible to manage a more appropriate care plan by adjusting the progress confirmation method according to the emotions of the care recipient.

[0116] The 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 feeling stressed, the frequency of data collection can be reduced to reduce the burden on the care recipient. Also, if the care recipient is relaxed, the frequency of data collection can be increased to understand the detailed health condition. Furthermore, if the care recipient is feeling anxious, the frequency of data collection can be adjusted appropriately to provide a sense of security. In this way, the burden on the care recipient can be reduced by adjusting the frequency of data collection according to the emotions of the care recipient.

[0117] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, a detailed analysis can be performed on important health data. A simplified analysis can also be performed on less important health data. Furthermore, the level of detail of the analysis can be dynamically adjusted according to the importance of the health data. This allows for the provision of a more effective care plan by adjusting the level of detail of the analysis according to the importance of the health data.

[0118] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the health condition. For example, a detailed proposal can be made for an important health condition. A simplified proposal can also be made for a less important health condition. Furthermore, the level of detail of the proposal can be dynamically adjusted according to the importance of the health condition. This allows for the provision of a more effective care plan by adjusting the level of detail of the proposal according to the importance of the health condition.

[0119] When checking, the checking unit can select the optimal checking method by referring to the past progress history of the care recipient. For example, the current progress checking method can be optimized based on the past progress history of the care recipient. The checking unit can also adjust the checking method by referring to the past progress history of the care recipient. Furthermore, the accuracy of progress checking can be improved by using the past progress history of the care recipient. This allows for more effective management of care plans by selecting the optimal checking method based on the past progress history.

[0120] When collecting data, the collection unit can filter the data based on the living environment and activity patterns of the care recipient. For example, if the care recipient is outdoors, an outdoor sensor can be used preferentially. Also, if the care recipient is active at night, a nighttime data collection method can be applied. Furthermore, specific data can be filtered according to the living environment of the care recipient, and only necessary information can be collected. This makes it possible to collect data according to the living environment and activity patterns.

[0121] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of changes in the health condition. For example, if the health condition suddenly changes, priority is given to proposals for emergency responses. Also, if the health condition is stable, a long-term care plan can be proposed. Furthermore, the priority of the proposal can be dynamically adjusted depending on the timing of changes in the health condition. In this way, by determining the priority of the proposal based on the timing of changes in the health condition, a more effective care plan can be provided.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The collection unit collects data from sensors or medical devices. The collected data includes, for example, the body temperature, heart rate, blood pressure, and activity level of the care recipient. The collection unit collects data in real time using a wearable device. The collection unit can also integrate data from medical devices to detect daily changes in the care recipient. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit. The analysis unit uses machine learning algorithms, deep learning technology, and natural language processing technology to analyze the data and identify the health condition and lifestyle needs of the person requiring care. Step 3: The proposal unit proposes an individual nursing care plan based on the analysis results obtained by the analysis unit. The proposal unit proposes an appropriate care plan based on the health condition and lifestyle needs of the person requiring care, such as cooling measures if body temperature rises or a rehabilitation exercise plan if activity levels decrease. Step 4: The confirmation unit checks the progress of the care plan proposed by the proposal unit. The confirmation unit checks the progress of the care plan in real time through a dedicated app and sends a notification to the app if there is a change in the health condition of the person requiring care. The confirmation unit can also check the progress of the care plan and revise the plan as necessary.

[0124] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0127] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 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 the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a 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.

[0162] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0173] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0177] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] 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.

[0187] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0189] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0193] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting data from a sensor or medical device; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes an individual care plan based on the analysis results obtained by the analysis unit; a confirmation unit that confirms the progress of the care plan proposed by the proposal unit. A system characterized by:

2. The collecting unit Collecting the care recipient's temperature, heart rate, blood pressure, activity level and other data 2. The system of claim 1.

3. The analysis unit The collected data is analyzed using AI to identify the health condition and lifestyle needs of those requiring care.

2. The system of claim 1.

4. The proposal unit Propose an individualized nursing care plan based on identified health conditions and lifestyle needs 2. The system of claim 1.

5. The confirmation unit Check the progress of care plans in real time through a dedicated app 2. The system of claim 1.

6. The confirmation unit Modify the plan as needed 2. The system of claim 1.

7. The proposal unit If the care recipient's body temperature rises, identify the cause and propose specific countermeasures.

2. The system of claim 1.

8. The proposal unit If the activity level of a care recipient decreases, we analyze the cause and propose a rehabilitation plan.

2. The system of claim 1.

Images