system

Abstract

The system according to this embodiment aims to provide an optimal care plan based on individual health conditions and lifestyle data, and to issue emergency notifications. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a notification unit. The collection unit collects health status and lifestyle data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a care plan based on the data analyzed by the analysis unit. The notification unit provides emergency notifications based on the care plan generated by the generation unit.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to collect and provide information on care plans according to the individual needs of disabled or elderly people, and the burden on family members and caregivers is large.

[0005] The system according to the embodiment aims to provide an optimal care plan based on individual health conditions and life data and issue an emergency notification.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a notification unit. The collection unit collects health status and lifestyle data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a care plan based on the data analyzed by the analysis unit. The notification unit provides emergency notifications based on the care plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal care plan based on an individual's health status and lifestyle data, and can also send emergency notifications. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The peace-of-mind care partner AI system according to an embodiment of the present invention is a service that integrates generative AI and sensor technology. This peace-of-mind care partner AI system aims to improve the quality of life of disabled and elderly people and reduce the burden on caregivers. This system provides a care plan optimized for individual needs based on real-time data analysis. First, it monitors the health status and life data (vital signs, activity levels, sleep patterns, environmental conditions, etc.) of each user through sensors and mobile devices. These data are analyzed by generative AI, and a rehabilitation plan and daily support procedures tailored to individual needs are automatically generated. For example, appropriate rehabilitation methods and support procedures are provided for those with physical disabilities, developmental disabilities, visual or hearing impairments. In addition, the generative AI provides continuous feedback based on the collected data and updates the care plan as needed. Furthermore, in case of an emergency, it sends notifications in real time to support prompt response. For example, when the health status of an elderly person suddenly changes, immediate notifications are sent to caregivers and medical institutions, and appropriate actions are taken. This service not only improves the quality of life of disabled and elderly people, but also reduces the burden on caregivers and enables efficient resource allocation. In facilities and support organizations, by providing an optimal care plan based on user data and automating staff work, efficient operation is achieved. Furthermore, it also proposes optimal ways to utilize regional resources and subsidy information and provides reassuring support on a 24-hour basis. As a result, personalized care tailored to individual health conditions is provided, supporting the realization of a safe and independent life. Thereby, the peace-of-mind care partner AI system can improve the quality of life of disabled and elderly people and reduce the burden on caregivers.

[0029] The AI ​​system for safe care according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a notification unit. The data collection unit collects health status and lifestyle data. Health status and lifestyle data include, but are not limited to, vital signs, meal records, exercise levels, and sleep patterns. The data collection unit can, for example, monitor the user's vital signs in real time using sensors. The data collection unit can also record the user's activity level and sleep patterns using a mobile device. Furthermore, the data collection unit can provide an interface for inputting the user's meal records. For example, the data collection unit provides an application for the user to input meal details and collects the data. The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit can use statistical analysis to understand trends in the user's health status. The analysis unit can also use machine learning algorithms to predict the user's health status. Furthermore, the analysis unit can detect abnormalities in the user's health status based on the collected data. For example, the analysis unit detects abnormal vital signs and notifies the user. The generation unit generates a care plan based on the data analyzed by the analysis unit. The care plan may include, but is not limited to, rehabilitation plans, dietary guidance, and exercise programs. For example, the generation unit can use generation AI to automatically generate a rehabilitation plan tailored to the user's health condition. The generation unit can also use generation AI to provide dietary guidance based on the user's diet. Furthermore, the generation unit can use generation AI to create an exercise program tailored to the user's activity level. The notification unit provides emergency notifications based on the care plan generated by the generation unit. Emergency notifications are given, for example, when the user's health condition suddenly changes, but are not limited to this case. For example, the notification unit sends a notification to caregivers or medical institutions if the user's vital signs show abnormal values. The notification unit can also provide emergency notifications if the user falls. Furthermore, the notification unit can provide periodic notifications depending on the user's health condition.For example, the notification unit sends notifications prompting the user to regularly check their health status. This allows the AI ​​system, according to the embodiment, to collect and analyze health and lifestyle data, generate care plans, and send emergency notifications, thereby providing care optimized to individual needs.

[0030] The data collection unit collects health status and lifestyle data. This data includes, but is not limited to, vital signs, meal records, exercise levels, and sleep patterns. For example, the data collection unit monitors the user's vital signs in real time using sensors. Specifically, it continuously measures vital signs such as heart rate, blood pressure, body temperature, and oxygen saturation using sensors in wearable devices or smartwatches, and transmits this data to a cloud server. The data collection unit can also record the user's activity level and sleep patterns using mobile devices. For example, it uses the accelerometer and gyroscope sensors of a smartphone to record the user's steps, exercise level, and sleep quality and duration. Furthermore, the data collection unit can provide an interface for users to input their meal records. For example, the data collection unit provides an application for users to input their meal details and collects that data. Users can take photos of their meals and upload them to the app, or input meal details as text, and utilize a function that automatically analyzes the nutrients and calories of their meals. This allows the data collection unit to centrally collect and monitor diverse data related to the user's health status and lifestyle in real time. Furthermore, the data collection unit can securely store this data and integrate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and generation units. Additionally, the frequency and accuracy of data collection can be adjusted, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using, for example, statistical analysis and machine learning algorithms, but is not limited to these examples. Specifically, statistical analysis is used to understand trends in the user's health status. For example, based on past vital sign data, fluctuations in the user's health status can be graphed, allowing for visual identification of outliers and trends. The analysis unit can also predict the user's health status using machine learning algorithms. For example, a model trained on past data can be used to predict future health risks and enable early countermeasures. Furthermore, the analysis unit can detect abnormalities in the user's health status based on the collected data. For example, the analysis unit can detect abnormal vital sign values ​​and notify the user. Specifically, it can detect abnormal data in real time, such as when the heart rate exceeds the normal range or when blood pressure rises sharply, and issue warnings to the user and caregivers. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific seasons or time periods based on past health data and formulate future countermeasures. This allows the analysis unit to comprehensively evaluate the user's health status and contribute to the creation of appropriate care plans.

[0032] The generation unit generates care plans based on data analyzed by the analysis unit. These care plans may include, but are not limited to, rehabilitation plans, dietary guidance, and exercise programs. Specifically, the generation unit uses generation AI to automatically generate rehabilitation plans tailored to the user's health condition. For example, it considers the user's exercise capacity and health status to propose appropriate rehabilitation menus and create a step-by-step plan. The generation unit can also use generation AI to provide dietary guidance based on the user's diet. For example, it analyzes the user's food records, evaluates nutritional balance and calorie intake, and proposes a healthy meal plan. Furthermore, the generation unit can use generation AI to create exercise programs tailored to the user's activity level. For example, it proposes appropriate exercise menus based on the user's exercise history and current fitness level, supporting daily exercise habits. The generation unit can customize these care plans to meet the user's needs and goals, providing personalized care. Additionally, the generation unit can collect user feedback and evaluate the effectiveness of the care plans. For example, it collects the results of the user's rehabilitation plan execution and dietary guidance, and the generation AI analyzes this data to identify areas for improvement in the care plans. This allows the generation unit to continuously optimize care plans and support improvements in the user's health. Furthermore, the generation unit can flexibly adjust the care plan in response to changes in the user's health. For example, if the user's health improves, the rehabilitation plan can be advanced or the exercise program can be strengthened. This enables the generation unit to provide care plans optimized for the user's health and to realize effective care tailored to individual needs.

[0033] The notification unit issues emergency notifications based on the care plan generated by the generation unit. Emergency notifications are issued, for example, when the user's health condition suddenly changes, but are not limited to such cases. Specifically, the notification unit sends notifications to caregivers and medical institutions if the user's vital signs show abnormal values. For example, it quickly notifies in urgent situations such as a sudden increase in heart rate or an abnormal drop in blood pressure. The notification unit can also issue emergency notifications if the user falls. For example, if the user is wearing a fall detection sensor, it will send a notification to caregivers and family members when a fall is detected to encourage a quick response. Furthermore, the notification unit can also issue periodic notifications according to the user's health condition. For example, it sends notifications to encourage regular checks of the user's health condition. Specifically, it supports users in not neglecting their health management by sending reminders for regular health checkups and notifications to encourage daily checks of vital signs. This allows the notification unit to quickly provide appropriate action instructions to each user and minimize the risk of disaster. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can review and improve notification content based on feedback from users who have received notifications. The notification unit can also reliably transmit information using multiple communication methods. For instance, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the notification unit to provide users with prompt and reliable action instructions, supporting them in maintaining and improving their health.

[0034] The generation unit can automatically generate rehabilitation plans and daily support procedures using a generation AI. For example, the generation unit can use the generation AI to automatically generate a rehabilitation plan tailored to the user's health condition. For instance, the generation unit can input a prompt to the generation AI such as, "Create a rehabilitation plan based on the user's health condition," and the generation AI will generate the rehabilitation plan. The generation unit can also use the generation AI to automatically generate daily support procedures for the user. For example, the generation unit can input a prompt to the generation AI such as, "Create daily support procedures for the user," and the generation AI will generate the daily support procedures. Furthermore, the generation unit can use the generation AI to continuously update the rehabilitation plan and daily support procedures according to the user's health condition. For example, the generation unit can input a prompt to the generation AI such as, "Update the rehabilitation plan based on the user's latest health data," and the generation AI will update the rehabilitation plan. This allows for the provision of care tailored to individual needs by automatically generating rehabilitation plans and daily support procedures using the generation AI.

[0035] The notification unit can provide real-time notifications in emergencies. For example, it can provide real-time notifications if a user's health condition suddenly changes. For instance, if a user's vital signs show abnormal values, the notification unit can immediately send a notification to caregivers or medical institutions. The notification unit can also provide real-time emergency notifications if a user falls. For example, if the notification unit detects a fall, it can immediately send a notification to caregivers or medical institutions. Furthermore, the notification unit can provide periodic real-time notifications depending on the user's health condition. For example, the notification unit can send real-time notifications prompting regular checks of the user's health condition. This enables a rapid response by providing real-time notifications in emergencies.

[0036] The generation unit can provide continuous feedback based on collected data and update care plans. For example, the generation unit can detect changes in the user's health status based on collected data and update the care plan. For example, the generation unit can use generation AI to analyze the user's health data and update the care plan as needed. The generation unit can also continuously update the user's rehabilitation plan and daily support procedures based on collected data. For example, the generation unit can input a prompt to the generation AI such as "Update the rehabilitation plan based on the user's latest health data," and the generation AI will update the rehabilitation plan. Furthermore, the generation unit can provide feedback tailored to the user's health status based on collected data. For example, the generation unit can use generation AI to provide advice regarding the user's health status. This allows for continuous feedback based on collected data and updates to care plans, ensuring that optimal care is always provided.

[0037] The generation unit can propose the optimal way to utilize local resources and grant information. For example, the generation unit collects local resources and grant information and proposes the optimal way to use them. For example, the generation unit uses generation AI to analyze local resources and grant information and proposes the optimal way to use them for the user. The generation unit can also optimize the user's care plan based on local resources and grant information. For example, the generation unit inputs a prompt to the generation AI, "Please optimize the care plan based on local resources and grant information," and the generation AI optimizes the care plan. Furthermore, the generation unit can also provide specific advice to the user based on local resources and grant information. For example, the generation unit uses generation AI to propose the procedure for applying for grants to the user. This enables efficient resource allocation by proposing the optimal way to utilize local resources and grant information.

[0038] The generation unit can generate care plans that streamline resource management for facilities and support organizations. For example, the generation unit collects data to streamline resource management for facilities and support organizations and generates care plans. For example, the generation unit uses a generation AI to analyze resource management data from facilities and support organizations and generate efficient care plans. The generation unit can also propose specific steps to optimize resource management for facilities and support organizations. For example, the generation unit inputs a prompt to the generation AI, "Please propose steps to optimize resource management for facilities and support organizations," and the generation AI proposes specific steps. Furthermore, the generation unit can provide continuous feedback to streamline resource management for facilities and support organizations. For example, the generation unit uses the generation AI to suggest areas for improvement in resource management. This enables efficient operation by generating care plans that streamline resource management for facilities and support organizations.

[0039] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can identify the most effective collection time from the user's past data and collect data during that time. For example, the data collection unit can use generative AI to analyze the user's past health data and identify the optimal collection time. The data collection unit can also identify when specific health indicators fluctuate from the user's past data and collect data accordingly. For example, the data collection unit can use generative AI to analyze the user's past health data and identify when specific health indicators fluctuate. Furthermore, the data collection unit can optimize the collection frequency from the user's past data, efficiently collecting only the necessary data. For example, the data collection unit can use generative AI to analyze the user's past health data and optimize the collection frequency. This allows for efficient data collection by analyzing the user's past health data and selecting the optimal collection method.

[0040] The data collection unit can filter health data based on the user's current lifestyle and environmental conditions. For example, if the user is outside, the data collection unit can limit the data collected, collecting only the minimum necessary data. For instance, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and limit the data collected if the user is outside. Furthermore, if the user is at home, the data collection unit can collect detailed data and consider the influence of the living environment. For example, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and collect detailed data if the user is at home. Additionally, if the user is exercising, the data collection unit can prioritize collecting exercise-related data. For example, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and prioritize collecting exercise-related data if the user is exercising. This allows for efficient collection of only the necessary data by filtering it based on the user's current lifestyle and environmental conditions.

[0041] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location, when collecting health data. For example, if the user is at high altitude, the data collection unit will prioritize the collection of data such as oxygen concentration and heart rate. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as oxygen concentration and heart rate if the user is at high altitude. The data collection unit can also prioritize the collection of data such as ambient noise and air quality if the user is in an urban area. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as ambient noise and air quality if the user is in an urban area. Furthermore, the data collection unit can prioritize the collection of data such as humidity and temperature if the user is at the beach. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as humidity and temperature if the user is at the beach. By prioritizing the collection of highly relevant data while considering the user's geographical location, a more accurate understanding of the user's health status can be achieved.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts on social media indicating they are feeling stressed, the data collection unit can collect stress-related data. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects stress-related data if the user has posted about feeling stressed. The data collection unit can also collect exercise-related data if a user posts on social media about exercise. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects exercise-related data if the user has posted about exercise. Furthermore, the data collection unit can also collect diet-related data if a user posts on social media about food. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects diet-related data if the user has posted about food. By analyzing a user's social media activity and collecting relevant data, the system can more accurately understand the user's health status.

[0043] The analysis unit can adjust the level of detail in its analysis based on the importance of the health data during data analysis. For example, it can perform detailed analysis on important health indicators and simplify other data. For instance, it can use generative AI to assess the importance of health data and perform detailed analysis on important health indicators. The analysis unit can also adjust the frequency of analysis according to the importance of the health data. For example, it can use generative AI to assess the importance of health data and perform frequent analysis on important data. Furthermore, the analysis unit can apply multiple analysis algorithms to important health data to improve accuracy. For example, it can use generative AI to assess the importance of health data and apply multiple analysis algorithms to important data. This allows for efficient data analysis by adjusting the level of detail based on the importance of the health data.

[0044] The analysis unit can apply different analysis algorithms depending on the category of health data during data analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For instance, the analysis unit analyzes heart rate data using a generative AI and then applies the heart rate variability analysis algorithm. The analysis unit can also apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit analyzes sleep data using a generative AI and then applies the sleep stage analysis algorithm. Furthermore, the analysis unit can apply an activity pattern analysis algorithm to activity level data. For example, the analysis unit analyzes activity level data using a generative AI and then applies the activity pattern analysis algorithm. By applying different analysis algorithms depending on the category of health data, more accurate analysis results can be obtained.

[0045] The analysis unit can determine the priority of analysis based on the timing of health data collection during data analysis. For example, the analysis unit can prioritize the analysis of recently collected data to understand the latest health status. For example, the analysis unit can use generative AI to evaluate the timing of health data collection and prioritize the analysis of recently collected data. The analysis unit can also analyze the current health status by referring to past data. For example, the analysis unit can use generative AI to evaluate the timing of health data collection and analyze the current health status by referring to past data. Furthermore, the analysis unit can focus on analyzing data collected during a specific period to gain a detailed understanding of the health status during that period. For example, the analysis unit can use generative AI to evaluate the timing of health data collection and focus on analyzing data collected during a specific period. This allows for the prioritization of analysis based on the timing of health data collection, thereby enabling the understanding of the latest health status.

[0046] The analysis unit can adjust the order of analysis based on the relevance of health data during data analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to grasp the overall health status. For instance, the analysis unit can use generative AI to evaluate the relevance of health data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data and analyze important data first. For example, the analysis unit can use generative AI to evaluate the relevance of health data and postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit can use generative AI to evaluate the relevance of health data and dynamically adjust the order of analysis. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of health data.

[0047] The generation unit can adjust the level of detail generated based on the importance of health data when generating care plans. For example, the generation unit can generate detailed care plans for important health indicators and simplify other data. For instance, the generation unit can use generation AI to evaluate the importance of health data and generate detailed care plans for important health indicators. The generation unit can also adjust the frequency of care plans according to the importance of health data. For example, the generation unit can use generation AI to evaluate the importance of health data and generate care plans frequently for important data. Furthermore, the generation unit can generate multiple care plans for important health data, providing options. For example, the generation unit can use generation AI to evaluate the importance of health data and generate multiple care plans for important data. This allows for efficient care plan generation by adjusting the level of detail based on the importance of health data.

[0048] The generation unit can apply different generation algorithms depending on the category of health data when generating care plans. For example, the generation unit can apply a specialized rehabilitation generation algorithm to rehabilitation plans. For instance, the generation unit generates rehabilitation plans using a generation AI and applies a specialized rehabilitation generation algorithm. The generation unit can also apply a daily living support generation algorithm to daily support procedures. For example, the generation unit generates daily support procedures using a generation AI and applies a daily living support generation algorithm. Furthermore, the generation unit can apply an emergency response generation algorithm to emergency response procedures. For example, the generation unit generates emergency response care plans using a generation AI and applies an emergency response generation algorithm. By applying different generation algorithms depending on the category of health data, more accurate care plans can be generated.

[0049] The generation unit can determine the generation priority based on the timing of health data collection when generating care plans. For example, the generation unit can provide the latest care plan based on recently collected data. For example, the generation unit uses generation AI to evaluate the timing of health data collection and provides the latest care plan based on recently collected data. The generation unit can also provide a care plan based on the current health status while referring to past data. For example, the generation unit uses generation AI to evaluate the timing of health data collection and provides a care plan based on the current health status while referring to past data. Furthermore, the generation unit can provide a care plan that is appropriate for the health status during a specific period based on the data collected during that period. For example, the generation unit uses generation AI to evaluate the timing of health data collection and provides a care plan that is appropriate for the health status during a specific period based on the data collected during that period. By determining the generation priority based on the timing of health data collection, the generation unit can provide a care plan that is appropriate for the latest health status.

[0050] The generation unit can adjust the generation order based on the relevance of health data when generating care plans. For example, the generation unit can prioritize generating care plans based on highly relevant data. For instance, the generation unit can use generation AI to evaluate the relevance of health data and prioritize generating care plans based on highly relevant data. The generation unit can also postpone generating care plans based on important data, while delaying less relevant data. For example, the generation unit can use generation AI to evaluate the relevance of health data and postpone generating less relevant data. Furthermore, the generation unit can dynamically adjust the generation order based on the relevance of the data. For example, the generation unit can use generation AI to evaluate the relevance of health data and dynamically adjust the generation order. This allows for efficient care plan generation by adjusting the generation order based on the relevance of health data.

[0051] The notification unit can adjust the level of detail in notifications based on the importance of the health data. For example, it can provide detailed notifications for important health data and simplified notifications for other data. For instance, it can use generative AI to assess the importance of health data and provide detailed notifications for important data. The notification unit can also adjust the frequency of notifications according to the importance of the health data. For example, it can use generative AI to assess the importance of health data and provide frequent notifications for important data. Furthermore, the notification unit can combine multiple notification methods for important health data. For example, it can use generative AI to assess the importance of health data and combine multiple notification methods for important data. This allows for prioritizing notifications of important information by adjusting the level of detail based on the importance of the health data.

[0052] The notification unit can apply different notification algorithms depending on the category of health data when sending notifications. For example, for rehabilitation-related notifications, the notification unit can apply a specialized rehabilitation notification algorithm. For instance, the notification unit can generate rehabilitation-related notifications using generative AI and apply a specialized rehabilitation notification algorithm. The notification unit can also apply a daily living support notification algorithm for daily support-related notifications. For example, the notification unit can generate daily support-related notifications using generative AI and apply a daily living support notification algorithm. Furthermore, the notification unit can apply an emergency response notification algorithm for emergency response-related notifications. For example, the notification unit can generate emergency response-related notifications using generative AI and apply an emergency response notification algorithm. This allows for more appropriate notifications by applying different notification algorithms depending on the category of health data.

[0053] The notification unit can determine the priority of notifications based on when health data was collected. For example, the notification unit can prioritize notifications about the latest health status based on recently collected data. For example, the notification unit can use generative AI to evaluate when health data was collected and prioritize notifications about the latest health status based on recently collected data. The notification unit can also provide notifications about the current health status by referring to past data. For example, the notification unit can use generative AI to evaluate when health data was collected and provide notifications about the current health status by referring to past data. Furthermore, the notification unit can provide notifications about the health status during a specific period based on data collected during that period. For example, the notification unit can use generative AI to evaluate when health data was collected and provide notifications about the health status during a specific period based on data collected during that period. This allows for prioritizing notifications based on when health data was collected, thereby prioritizing notifications about the latest health status.

[0054] The notification unit can adjust the order of notifications based on the relevance of health data. For example, the notification unit can prioritize notifications based on highly relevant data. For instance, it can use generative AI to evaluate the relevance of health data and prioritize notifications based on highly relevant data. The notification unit can also postpone notifications for less relevant data and prioritize notifications based on important data. For example, it can use generative AI to evaluate the relevance of health data and postpone notifications for less relevant data. Furthermore, the notification unit can dynamically adjust the order of notifications based on the relevance of the data. For example, it can use generative AI to evaluate the relevance of health data and dynamically adjust the order of notifications. This allows important notifications to be prioritized by adjusting the order of notifications based on the relevance of health data.

[0055] The notification unit can select the optimal notification method by considering the user's current location information when sending a notification. For example, if the user is at home, the notification unit will prioritize voice notifications. For example, the notification unit will use generative AI to analyze the user's current location information and prioritize voice notifications if the user is at home. The notification unit can also prioritize vibration notifications if the user is out. For example, the notification unit will use generative AI to analyze the user's current location information and prioritize vibration notifications if the user is out. Furthermore, if the user is driving, the notification unit can avoid visual notifications and prioritize voice notifications. For example, the notification unit will use generative AI to analyze the user's current location information and avoid visual notifications and prioritize voice notifications if the user is driving. By selecting the optimal notification method considering the user's current location information, more appropriate notifications can be provided.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can identify the most effective collection time from the user's past data and collect data during that time. For example, the data collection unit can use generative AI to analyze the user's past health data and identify the optimal collection time. The data collection unit can also identify when specific health indicators fluctuate from the user's past data and collect data accordingly. For example, the data collection unit can use generative AI to analyze the user's past health data and identify when specific health indicators fluctuate. Furthermore, the data collection unit can optimize the collection frequency from the user's past data, efficiently collecting only the necessary data. For example, the data collection unit can use generative AI to analyze the user's past health data and optimize the collection frequency. This allows for efficient data collection by analyzing the user's past health data and selecting the optimal collection method.

[0058] The data collection unit can filter health data based on the user's current lifestyle and environmental conditions. For example, if the user is outside, the data collection unit can limit the data collected, collecting only the minimum necessary data. For instance, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and limit the data collected if the user is outside. Furthermore, if the user is at home, the data collection unit can collect detailed data and consider the influence of the living environment. For example, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and collect detailed data if the user is at home. Additionally, if the user is exercising, the data collection unit can prioritize collecting exercise-related data. For example, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and prioritize collecting exercise-related data if the user is exercising. This allows for efficient collection of only the necessary data by filtering it based on the user's current lifestyle and environmental conditions.

[0059] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location, when collecting health data. For example, if the user is at high altitude, the data collection unit will prioritize the collection of data such as oxygen concentration and heart rate. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as oxygen concentration and heart rate if the user is at high altitude. The data collection unit can also prioritize the collection of data such as ambient noise and air quality if the user is in an urban area. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as ambient noise and air quality if the user is in an urban area. Furthermore, the data collection unit can prioritize the collection of data such as humidity and temperature if the user is at the beach. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as humidity and temperature if the user is at the beach. By prioritizing the collection of highly relevant data while considering the user's geographical location, a more accurate understanding of the user's health status can be achieved.

[0060] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts on social media indicating they are feeling stressed, the data collection unit can collect stress-related data. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects stress-related data if the user has posted about feeling stressed. The data collection unit can also collect exercise-related data if a user posts on social media about exercise. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects exercise-related data if the user has posted about exercise. Furthermore, the data collection unit can also collect diet-related data if a user posts on social media about food. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects diet-related data if the user has posted about food. By analyzing a user's social media activity and collecting relevant data, the system can more accurately understand the user's health status.

[0061] The analysis unit can adjust the level of detail in its analysis based on the importance of the health data during data analysis. For example, it can perform detailed analysis on important health indicators and simplify other data. For instance, it can use generative AI to assess the importance of health data and perform detailed analysis on important health indicators. The analysis unit can also adjust the frequency of analysis according to the importance of the health data. For example, it can use generative AI to assess the importance of health data and perform frequent analysis on important data. Furthermore, the analysis unit can apply multiple analysis algorithms to important health data to improve accuracy. For example, it can use generative AI to assess the importance of health data and apply multiple analysis algorithms to important data. This allows for efficient data analysis by adjusting the level of detail based on the importance of the health data.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects health status and lifestyle data. This data includes vital signs, meal records, exercise levels, and sleep patterns. The data collection unit monitors the user's vital signs in real time using sensors and records the user's activity level and sleep patterns using a mobile device. The data collection unit also provides an application for the user to input their meal details and collects that data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit uses statistical analysis to understand trends in the user's health status and machine learning algorithms to predict the user's health status. In addition, the analysis unit detects abnormalities in the user's health status based on the collected data and notifies the user of any abnormal values ​​in vital signs. Step 3: The generation unit generates a care plan based on the data analyzed by the analysis unit. The care plan includes a rehabilitation plan, dietary guidance, and exercise program. The generation unit uses generation AI to automatically generate a rehabilitation plan tailored to the user's health condition, provides dietary guidance based on their diet, and creates an exercise program appropriate to their activity level. Step 4: The notification unit sends emergency notifications based on the care plan generated by the generation unit. The notification unit sends notifications to caregivers and medical institutions if the user's health condition suddenly changes or if vital signs show abnormal values. It also sends emergency notifications if the user falls and sends regular notifications according to the user's health condition.

[0064] (Example of form 2) The reassuring care partner AI system according to an embodiment of the present invention is a service that integrates generative AI and sensor technology. This reassuring care partner AI system aims to improve the quality of life of disabled and elderly people and reduce the burden on caregivers. This system provides a care plan optimized for individual needs based on real-time data analysis. First, it monitors the health status and life data (vital signs, activity levels, sleep patterns, environmental conditions, etc.) of each user through sensors and mobile devices. These data are analyzed by generative AI, and rehabilitation plans and daily support procedures tailored to individual needs are automatically generated. For example, appropriate rehabilitation methods and support procedures are provided for those with physical disabilities, developmental disabilities, visual or hearing impairments. In addition, the generative AI provides continuous feedback based on the collected data and updates the care plan as needed. Furthermore, in case of an emergency, it sends notifications in real time to support prompt responses. For example, when the health status of an elderly person changes suddenly, immediate notifications are sent to caregivers and medical institutions, and appropriate actions are taken. This service not only improves the quality of life of disabled and elderly people but also reduces the burden on caregivers and enables efficient resource allocation. In facilities and support groups as well, by providing an optimal care plan based on user data and automating staff operations, efficient management is achieved. Furthermore, it also proposes optimal ways to utilize regional resources and subsidy information and provides reassuring support on a 24-hour basis. As a result, personalized care tailored to individual health statuses is provided, supporting the realization of a safe and independent life. Thus, the reassuring care partner AI system can improve the quality of life of disabled and elderly people and reduce the burden on caregivers.

[0065] The AI ​​system for safe care according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a notification unit. The data collection unit collects health status and lifestyle data. Health status and lifestyle data include, but are not limited to, vital signs, meal records, exercise levels, and sleep patterns. The data collection unit can, for example, monitor the user's vital signs in real time using sensors. The data collection unit can also record the user's activity level and sleep patterns using a mobile device. Furthermore, the data collection unit can provide an interface for inputting the user's meal records. For example, the data collection unit provides an application for the user to input meal details and collects the data. The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit can use statistical analysis to understand trends in the user's health status. The analysis unit can also use machine learning algorithms to predict the user's health status. Furthermore, the analysis unit can detect abnormalities in the user's health status based on the collected data. For example, the analysis unit detects abnormal vital signs and notifies the user. The generation unit generates a care plan based on the data analyzed by the analysis unit. The care plan may include, but is not limited to, rehabilitation plans, dietary guidance, and exercise programs. For example, the generation unit can use generation AI to automatically generate a rehabilitation plan tailored to the user's health condition. The generation unit can also use generation AI to provide dietary guidance based on the user's diet. Furthermore, the generation unit can use generation AI to create an exercise program tailored to the user's activity level. The notification unit provides emergency notifications based on the care plan generated by the generation unit. Emergency notifications are given, for example, when the user's health condition suddenly changes, but are not limited to this case. For example, the notification unit sends a notification to caregivers or medical institutions if the user's vital signs show abnormal values. The notification unit can also provide emergency notifications if the user falls. Furthermore, the notification unit can provide periodic notifications depending on the user's health condition.For example, the notification unit sends notifications prompting the user to regularly check their health status. This allows the AI ​​system, according to the embodiment, to collect and analyze health and lifestyle data, generate care plans, and send emergency notifications, thereby providing care optimized to individual needs.

[0066] The data collection unit collects health status and lifestyle data. This data includes, but is not limited to, vital signs, meal records, exercise levels, and sleep patterns. For example, the data collection unit monitors the user's vital signs in real time using sensors. Specifically, it continuously measures vital signs such as heart rate, blood pressure, body temperature, and oxygen saturation using sensors in wearable devices or smartwatches, and transmits this data to a cloud server. The data collection unit can also record the user's activity level and sleep patterns using mobile devices. For example, it uses the accelerometer and gyroscope sensors of a smartphone to record the user's steps, exercise level, and sleep quality and duration. Furthermore, the data collection unit can provide an interface for users to input their meal records. For example, the data collection unit provides an application for users to input their meal details and collects that data. Users can take photos of their meals and upload them to the app, or input meal details as text, and utilize a function that automatically analyzes the nutrients and calories of their meals. This allows the data collection unit to centrally collect and monitor diverse data related to the user's health status and lifestyle in real time. Furthermore, the data collection unit can securely store this data and integrate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and generation units. Additionally, the frequency and accuracy of data collection can be adjusted, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using, for example, statistical analysis and machine learning algorithms, but is not limited to these examples. Specifically, statistical analysis is used to understand trends in the user's health status. For example, based on past vital sign data, fluctuations in the user's health status can be graphed, allowing for visual identification of outliers and trends. The analysis unit can also predict the user's health status using machine learning algorithms. For example, a model trained on past data can be used to predict future health risks and enable early countermeasures. Furthermore, the analysis unit can detect abnormalities in the user's health status based on the collected data. For example, the analysis unit can detect abnormal vital sign values ​​and notify the user. Specifically, it can detect abnormal data in real time, such as when the heart rate exceeds the normal range or when blood pressure rises sharply, and issue warnings to the user and caregivers. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific seasons or time periods based on past health data and formulate future countermeasures. This allows the analysis unit to comprehensively evaluate the user's health status and contribute to the creation of appropriate care plans.

[0068] The generation unit generates care plans based on data analyzed by the analysis unit. These care plans may include, but are not limited to, rehabilitation plans, dietary guidance, and exercise programs. Specifically, the generation unit uses generation AI to automatically generate rehabilitation plans tailored to the user's health condition. For example, it considers the user's exercise capacity and health status to propose appropriate rehabilitation menus and create a step-by-step plan. The generation unit can also use generation AI to provide dietary guidance based on the user's diet. For example, it analyzes the user's food records, evaluates nutritional balance and calorie intake, and proposes a healthy meal plan. Furthermore, the generation unit can use generation AI to create exercise programs tailored to the user's activity level. For example, it proposes appropriate exercise menus based on the user's exercise history and current fitness level, supporting daily exercise habits. The generation unit can customize these care plans to meet the user's needs and goals, providing personalized care. Additionally, the generation unit can collect user feedback and evaluate the effectiveness of the care plans. For example, it collects the results of the user's rehabilitation plan execution and dietary guidance, and the generation AI analyzes this data to identify areas for improvement in the care plans. This allows the generation unit to continuously optimize care plans and support improvements in the user's health. Furthermore, the generation unit can flexibly adjust the care plan in response to changes in the user's health. For example, if the user's health improves, the rehabilitation plan can be advanced or the exercise program can be strengthened. This enables the generation unit to provide care plans optimized for the user's health and to realize effective care tailored to individual needs.

[0069] The notification unit issues emergency notifications based on the care plan generated by the generation unit. Emergency notifications are issued, for example, when the user's health condition suddenly changes, but are not limited to such cases. Specifically, the notification unit sends notifications to caregivers and medical institutions if the user's vital signs show abnormal values. For example, it quickly notifies in urgent situations such as a sudden increase in heart rate or an abnormal drop in blood pressure. The notification unit can also issue emergency notifications if the user falls. For example, if the user is wearing a fall detection sensor, it will send a notification to caregivers and family members when a fall is detected to encourage a quick response. Furthermore, the notification unit can also issue periodic notifications according to the user's health condition. For example, it sends notifications to encourage regular checks of the user's health condition. Specifically, it supports users in not neglecting their health management by sending reminders for regular health checkups and notifications to encourage daily checks of vital signs. This allows the notification unit to quickly provide appropriate action instructions to each user and minimize the risk of disaster. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can review and improve notification content based on feedback from users who have received notifications. The notification unit can also reliably transmit information using multiple communication methods. For instance, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the notification unit to provide users with prompt and reliable action instructions, supporting them in maintaining and improving their health.

[0070] The generation unit can automatically generate rehabilitation plans and daily support procedures using a generation AI. For example, the generation unit can use the generation AI to automatically generate a rehabilitation plan tailored to the user's health condition. For instance, the generation unit can input a prompt to the generation AI such as, "Create a rehabilitation plan based on the user's health condition," and the generation AI will generate the rehabilitation plan. The generation unit can also use the generation AI to automatically generate daily support procedures for the user. For example, the generation unit can input a prompt to the generation AI such as, "Create daily support procedures for the user," and the generation AI will generate the daily support procedures. Furthermore, the generation unit can use the generation AI to continuously update the rehabilitation plan and daily support procedures according to the user's health condition. For example, the generation unit can input a prompt to the generation AI such as, "Update the rehabilitation plan based on the user's latest health data," and the generation AI will update the rehabilitation plan. This allows for the provision of care tailored to individual needs by automatically generating rehabilitation plans and daily support procedures using the generation AI.

[0071] The notification unit can provide real-time notifications in emergencies. For example, it can provide real-time notifications if a user's health condition suddenly changes. For instance, if a user's vital signs show abnormal values, the notification unit can immediately send a notification to caregivers or medical institutions. The notification unit can also provide real-time emergency notifications if a user falls. For example, if the notification unit detects a fall, it can immediately send a notification to caregivers or medical institutions. Furthermore, the notification unit can provide periodic real-time notifications depending on the user's health condition. For example, the notification unit can send real-time notifications prompting regular checks of the user's health condition. This enables a rapid response by providing real-time notifications in emergencies.

[0072] The generation unit can provide continuous feedback based on collected data and update care plans. For example, the generation unit can detect changes in the user's health status based on collected data and update the care plan. For example, the generation unit can use generation AI to analyze the user's health data and update the care plan as needed. The generation unit can also continuously update the user's rehabilitation plan and daily support procedures based on collected data. For example, the generation unit can input a prompt to the generation AI such as "Update the rehabilitation plan based on the user's latest health data," and the generation AI will update the rehabilitation plan. Furthermore, the generation unit can provide feedback tailored to the user's health status based on collected data. For example, the generation unit can use generation AI to provide advice regarding the user's health status. This allows for continuous feedback based on collected data and updates to care plans, ensuring that optimal care is always provided.

[0073] The generation unit can propose the optimal way to utilize local resources and grant information. For example, the generation unit collects local resources and grant information and proposes the optimal way to use them. For example, the generation unit uses generation AI to analyze local resources and grant information and proposes the optimal way to use them for the user. The generation unit can also optimize the user's care plan based on local resources and grant information. For example, the generation unit inputs a prompt to the generation AI, "Please optimize the care plan based on local resources and grant information," and the generation AI optimizes the care plan. Furthermore, the generation unit can also provide specific advice to the user based on local resources and grant information. For example, the generation unit uses generation AI to propose the procedure for applying for grants to the user. This enables efficient resource allocation by proposing the optimal way to utilize local resources and grant information.

[0074] The generation unit can generate care plans that streamline resource management for facilities and support organizations. For example, the generation unit collects data to streamline resource management for facilities and support organizations and generates care plans. For example, the generation unit uses a generation AI to analyze resource management data from facilities and support organizations and generate efficient care plans. The generation unit can also propose specific steps to optimize resource management for facilities and support organizations. For example, the generation unit inputs a prompt to the generation AI, "Please propose steps to optimize resource management for facilities and support organizations," and the generation AI proposes specific steps. Furthermore, the generation unit can provide continuous feedback to streamline resource management for facilities and support organizations. For example, the generation unit uses the generation AI to suggest areas for improvement in resource management. This enables efficient operation by generating care plans that streamline resource management for facilities and support organizations.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency and collect data when the user is relaxed. For example, the data collection unit can estimate the user's emotions using an emotion engine and reduce the collection frequency if the user is stressed. The data collection unit can also collect data more frequently when the user is relaxed to gain a more detailed understanding of their health status. For example, the data collection unit can estimate the user's emotions using an emotion engine and collect data more frequently when the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can shorten the collection frequency to quickly acquire data. For example, the data collection unit can estimate the user's emotions using an emotion engine and shorten the collection frequency if the user is in a hurry. By adjusting the timing of health data collection based on the user's emotions, data can be collected at a more appropriate time.

[0076] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can identify the most effective collection time from the user's past data and collect data during that time. For example, the data collection unit can use generative AI to analyze the user's past health data and identify the optimal collection time. The data collection unit can also identify when specific health indicators fluctuate from the user's past data and collect data accordingly. For example, the data collection unit can use generative AI to analyze the user's past health data and identify when specific health indicators fluctuate. Furthermore, the data collection unit can optimize the collection frequency from the user's past data, efficiently collecting only the necessary data. For example, the data collection unit can use generative AI to analyze the user's past health data and optimize the collection frequency. This allows for efficient data collection by analyzing the user's past health data and selecting the optimal collection method.

[0077] The data collection unit can filter health data based on the user's current lifestyle and environmental conditions. For example, if the user is outside, the data collection unit can limit the data collected, collecting only the minimum necessary data. For instance, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and limit the data collected if the user is outside. Furthermore, if the user is at home, the data collection unit can collect detailed data and consider the influence of the living environment. For example, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and collect detailed data if the user is at home. Additionally, if the user is exercising, the data collection unit can prioritize collecting exercise-related data. For example, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and prioritize collecting exercise-related data if the user is exercising. This allows for efficient collection of only the necessary data by filtering it based on the user's current lifestyle and environmental conditions.

[0078] The data collection unit can estimate the user's emotions and prioritize the health data to collect based on those emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, the data collection unit uses an emotion engine to estimate the user's emotions and prioritizes collecting stress-related data if the user is stressed. The data collection unit can also collect overall health data in a balanced manner if the user is relaxed. For example, the data collection unit uses an emotion engine to estimate the user's emotions and prioritizes collecting overall health data in a balanced manner if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only the most important health indicators. For example, the data collection unit uses an emotion engine to estimate the user's emotions and prioritizes collecting only the most important health indicators if the user is in a hurry. This allows for the priority collection of important data by prioritizing the health data to be collected based on the user's emotions.

[0079] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location, when collecting health data. For example, if the user is at high altitude, the data collection unit will prioritize the collection of data such as oxygen concentration and heart rate. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as oxygen concentration and heart rate if the user is at high altitude. The data collection unit can also prioritize the collection of data such as ambient noise and air quality if the user is in an urban area. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as ambient noise and air quality if the user is in an urban area. Furthermore, the data collection unit can prioritize the collection of data such as humidity and temperature if the user is at the beach. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as humidity and temperature if the user is at the beach. By prioritizing the collection of highly relevant data while considering the user's geographical location, a more accurate understanding of the user's health status can be achieved.

[0080] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts on social media indicating they are feeling stressed, the data collection unit can collect stress-related data. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects stress-related data if the user has posted about feeling stressed. The data collection unit can also collect exercise-related data if a user posts on social media about exercise. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects exercise-related data if the user has posted about exercise. Furthermore, the data collection unit can also collect diet-related data if a user posts on social media about food. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects diet-related data if the user has posted about food. By analyzing a user's social media activity and collecting relevant data, the system can more accurately understand the user's health status.

[0081] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit will focus on analyzing stress-related data. For instance, it can use an emotion engine to estimate the user's emotions and, if stressed, focus on analyzing stress-related data. Furthermore, if the user is relaxed, the analysis unit can analyze overall health data in a balanced manner. For example, it can use an emotion engine to estimate the user's emotions and, if relaxed, analyze overall health data in a balanced manner. Additionally, if the user is in a hurry, the analysis unit can quickly analyze only the most important health indicators. For example, it can use an emotion engine to estimate the user's emotions and, if in a hurry, quickly analyze only the most important health indicators. This allows for more appropriate analysis results by adjusting the data analysis method based on the user's emotions.

[0082] The analysis unit can adjust the level of detail in its analysis based on the importance of the health data during data analysis. For example, it can perform detailed analysis on important health indicators and simplify other data. For instance, it can use generative AI to assess the importance of health data and perform detailed analysis on important health indicators. The analysis unit can also adjust the frequency of analysis according to the importance of the health data. For example, it can use generative AI to assess the importance of health data and perform frequent analysis on important data. Furthermore, the analysis unit can apply multiple analysis algorithms to important health data to improve accuracy. For example, it can use generative AI to assess the importance of health data and apply multiple analysis algorithms to important data. This allows for efficient data analysis by adjusting the level of detail based on the importance of the health data.

[0083] The analysis unit can apply different analysis algorithms depending on the category of health data during data analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For instance, the analysis unit analyzes heart rate data using a generative AI and then applies the heart rate variability analysis algorithm. The analysis unit can also apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit analyzes sleep data using a generative AI and then applies the sleep stage analysis algorithm. Furthermore, the analysis unit can apply an activity pattern analysis algorithm to activity level data. For example, the analysis unit analyzes activity level data using a generative AI and then applies the activity pattern analysis algorithm. By applying different analysis algorithms depending on the category of health data, more accurate analysis results can be obtained.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple and easy-to-read display method. For example, the analysis unit can estimate the user's emotions using an emotion engine and provide a simple and easy-to-read display method if the user is feeling stressed. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit can estimate the user's emotions using an emotion engine and provide a display method that includes detailed information if the user is relaxed. Furthermore, the analysis unit can provide a concise display method if the user is in a hurry. For example, the analysis unit can estimate the user's emotions using an emotion engine and provide a concise display method if the user is in a hurry. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0085] The analysis unit can determine the priority of analysis based on the timing of health data collection during data analysis. For example, the analysis unit can prioritize the analysis of recently collected data to understand the latest health status. For example, the analysis unit can use generative AI to evaluate the timing of health data collection and prioritize the analysis of recently collected data. The analysis unit can also analyze the current health status by referring to past data. For example, the analysis unit can use generative AI to evaluate the timing of health data collection and analyze the current health status by referring to past data. Furthermore, the analysis unit can focus on analyzing data collected during a specific period to gain a detailed understanding of the health status during that period. For example, the analysis unit can use generative AI to evaluate the timing of health data collection and focus on analyzing data collected during a specific period. This allows for the prioritization of analysis based on the timing of health data collection, thereby enabling the understanding of the latest health status.

[0086] The analysis unit can adjust the order of analysis based on the relevance of health data during data analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to grasp the overall health status. For instance, the analysis unit can use generative AI to evaluate the relevance of health data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data and analyze important data first. For example, the analysis unit can use generative AI to evaluate the relevance of health data and postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit can use generative AI to evaluate the relevance of health data and dynamically adjust the order of analysis. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of health data.

[0087] The generation unit can estimate the user's emotions and adjust the care plan generation method based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a care plan that prioritizes stress reduction. For example, the generation unit can estimate the user's emotions using an emotion engine and generate a care plan that prioritizes stress reduction if the user is stressed. The generation unit can also generate a care plan that prioritizes overall health maintenance if the user is relaxed. For example, the generation unit can estimate the user's emotions using an emotion engine and generate a care plan that prioritizes overall health maintenance if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can generate a care plan that can be quickly implemented. For example, the generation unit can estimate the user's emotions using an emotion engine and generate a care plan that can be quickly implemented if the user is in a hurry. In this way, by adjusting the care plan generation method based on the user's emotions, a more appropriate care plan can be provided.

[0088] The generation unit can adjust the level of detail generated based on the importance of health data when generating care plans. For example, the generation unit can generate detailed care plans for important health indicators and simplify other data. For instance, the generation unit can use generation AI to evaluate the importance of health data and generate detailed care plans for important health indicators. The generation unit can also adjust the frequency of care plans according to the importance of health data. For example, the generation unit can use generation AI to evaluate the importance of health data and generate care plans frequently for important data. Furthermore, the generation unit can generate multiple care plans for important health data, providing options. For example, the generation unit can use generation AI to evaluate the importance of health data and generate multiple care plans for important data. This allows for efficient care plan generation by adjusting the level of detail based on the importance of health data.

[0089] The generation unit can apply different generation algorithms depending on the category of health data when generating care plans. For example, the generation unit can apply a specialized rehabilitation generation algorithm to rehabilitation plans. For instance, the generation unit generates rehabilitation plans using a generation AI and applies a specialized rehabilitation generation algorithm. The generation unit can also apply a daily living support generation algorithm to daily support procedures. For example, the generation unit generates daily support procedures using a generation AI and applies a daily living support generation algorithm. Furthermore, the generation unit can apply an emergency response generation algorithm to emergency response procedures. For example, the generation unit generates emergency response care plans using a generation AI and applies an emergency response generation algorithm. By applying different generation algorithms depending on the category of health data, more accurate care plans can be generated.

[0090] The generation unit can estimate the user's emotions and determine the priority of care plans based on those emotions. For example, if the user is stressed, the generation unit can provide a care plan that prioritizes stress reduction. For example, the generation unit uses an emotion engine to estimate the user's emotions and provides a care plan that prioritizes stress reduction if the user is stressed. The generation unit can also provide a care plan that prioritizes overall health maintenance if the user is relaxed. For example, the generation unit uses an emotion engine to estimate the user's emotions and provides a care plan that prioritizes overall health maintenance if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can provide a care plan that can be implemented quickly. For example, the generation unit uses an emotion engine to estimate the user's emotions and provides a care plan that can be implemented quickly if the user is in a hurry. This allows for the priority of important care to be provided by determining the priority of care plans based on the user's emotions.

[0091] The generation unit can determine the generation priority based on the timing of health data collection when generating care plans. For example, the generation unit can provide the latest care plan based on recently collected data. For example, the generation unit uses generation AI to evaluate the timing of health data collection and provides the latest care plan based on recently collected data. The generation unit can also provide a care plan based on the current health status while referring to past data. For example, the generation unit uses generation AI to evaluate the timing of health data collection and provides a care plan based on the current health status while referring to past data. Furthermore, the generation unit can provide a care plan that is appropriate for the health status during a specific period based on the data collected during that period. For example, the generation unit uses generation AI to evaluate the timing of health data collection and provides a care plan that is appropriate for the health status during a specific period based on the data collected during that period. By determining the generation priority based on the timing of health data collection, the generation unit can provide a care plan that is appropriate for the latest health status.

[0092] The generation unit can adjust the generation order based on the relevance of health data when generating care plans. For example, the generation unit can prioritize generating care plans based on highly relevant data. For instance, the generation unit can use generation AI to evaluate the relevance of health data and prioritize generating care plans based on highly relevant data. The generation unit can also postpone generating care plans based on important data, while delaying less relevant data. For example, the generation unit can use generation AI to evaluate the relevance of health data and postpone generating less relevant data. Furthermore, the generation unit can dynamically adjust the generation order based on the relevance of the data. For example, the generation unit can use generation AI to evaluate the relevance of health data and dynamically adjust the generation order. This allows for efficient care plan generation by adjusting the generation order based on the relevance of health data.

[0093] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit can send a notification in a calm voice. For example, the notification unit can estimate the user's emotions using an emotion engine and send a notification in a calm voice if the user is stressed. The notification unit can also send a notification in a cheerful voice if the user is relaxed. For example, the notification unit can estimate the user's emotions using an emotion engine and send a notification in a cheerful voice if the user is relaxed. Furthermore, if the user is in a hurry, the notification unit can send a quick and concise notification. For example, the notification unit can estimate the user's emotions using an emotion engine and send a quick and concise notification if the user is in a hurry. In this way, by adjusting the notification method based on the user's emotions, more appropriate notifications can be made.

[0094] The notification unit can adjust the level of detail in notifications based on the importance of the health data. For example, it can provide detailed notifications for important health data and simplified notifications for other data. For instance, it can use generative AI to assess the importance of health data and provide detailed notifications for important data. The notification unit can also adjust the frequency of notifications according to the importance of the health data. For example, it can use generative AI to assess the importance of health data and provide frequent notifications for important data. Furthermore, the notification unit can combine multiple notification methods for important health data. For example, it can use generative AI to assess the importance of health data and combine multiple notification methods for important data. This allows for prioritizing notifications of important information by adjusting the level of detail based on the importance of the health data.

[0095] The notification unit can apply different notification algorithms depending on the category of health data when sending notifications. For example, for rehabilitation-related notifications, the notification unit can apply a specialized rehabilitation notification algorithm. For instance, the notification unit can generate rehabilitation-related notifications using generative AI and apply a specialized rehabilitation notification algorithm. The notification unit can also apply a daily living support notification algorithm for daily support-related notifications. For example, the notification unit can generate daily support-related notifications using generative AI and apply a daily living support notification algorithm. Furthermore, the notification unit can apply an emergency response notification algorithm for emergency response-related notifications. For example, the notification unit can generate emergency response-related notifications using generative AI and apply an emergency response notification algorithm. This allows for more appropriate notifications by applying different notification algorithms depending on the category of health data.

[0096] The notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is stressed, the notification unit will prioritize notifications related to stress reduction. For example, the notification unit uses an emotion engine to estimate the user's emotions and prioritizes notifications related to stress reduction if the user is stressed. The notification unit can also prioritize notifications related to overall health maintenance if the user is relaxed. For example, the notification unit uses an emotion engine to estimate the user's emotions and prioritizes notifications related to overall health maintenance if the user is relaxed. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications that require immediate attention. For example, the notification unit uses an emotion engine to estimate the user's emotions and prioritizes notifications that require immediate attention if the user is in a hurry. In this way, important notifications can be prioritized by determining notification priorities based on the user's emotions.

[0097] The notification unit can determine the priority of notifications based on when health data was collected. For example, the notification unit can prioritize notifications about the latest health status based on recently collected data. For example, the notification unit can use generative AI to evaluate when health data was collected and prioritize notifications about the latest health status based on recently collected data. The notification unit can also provide notifications about the current health status by referring to past data. For example, the notification unit can use generative AI to evaluate when health data was collected and provide notifications about the current health status by referring to past data. Furthermore, the notification unit can provide notifications about the health status during a specific period based on data collected during that period. For example, the notification unit can use generative AI to evaluate when health data was collected and provide notifications about the health status during a specific period based on data collected during that period. This allows for prioritizing notifications based on when health data was collected, thereby prioritizing notifications about the latest health status.

[0098] The notification unit can adjust the order of notifications based on the relevance of health data. For example, the notification unit can prioritize notifications based on highly relevant data. For instance, it can use generative AI to evaluate the relevance of health data and prioritize notifications based on highly relevant data. The notification unit can also postpone notifications for less relevant data and prioritize notifications based on important data. For example, it can use generative AI to evaluate the relevance of health data and postpone notifications for less relevant data. Furthermore, the notification unit can dynamically adjust the order of notifications based on the relevance of the data. For example, it can use generative AI to evaluate the relevance of health data and dynamically adjust the order of notifications. This allows important notifications to be prioritized by adjusting the order of notifications based on the relevance of health data.

[0099] The notification unit can select the optimal notification method by considering the user's current location information when sending a notification. For example, if the user is at home, the notification unit will prioritize voice notifications. For example, the notification unit will use generative AI to analyze the user's current location information and prioritize voice notifications if the user is at home. The notification unit can also prioritize vibration notifications if the user is out. For example, the notification unit will use generative AI to analyze the user's current location information and prioritize vibration notifications if the user is out. Furthermore, if the user is driving, the notification unit can avoid visual notifications and prioritize voice notifications. For example, the notification unit will use generative AI to analyze the user's current location information and avoid visual notifications and prioritize voice notifications if the user is driving. By selecting the optimal notification method considering the user's current location information, more appropriate notifications can be provided.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency and collect data when the user is relaxed. For example, the data collection unit can estimate the user's emotions using an emotion engine and reduce the collection frequency if the user is stressed. The data collection unit can also collect data more frequently when the user is relaxed to gain a more detailed understanding of their health status. For example, the data collection unit can estimate the user's emotions using an emotion engine and collect data more frequently when the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can shorten the collection frequency to quickly acquire data. For example, the data collection unit can estimate the user's emotions using an emotion engine and shorten the collection frequency if the user is in a hurry. By adjusting the timing of health data collection based on the user's emotions, data can be collected at a more appropriate time.

[0102] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit will focus on analyzing stress-related data. For instance, it can use an emotion engine to estimate the user's emotions and, if stressed, focus on analyzing stress-related data. Furthermore, if the user is relaxed, the analysis unit can analyze overall health data in a balanced manner. For example, it can use an emotion engine to estimate the user's emotions and, if relaxed, analyze overall health data in a balanced manner. Additionally, if the user is in a hurry, the analysis unit can quickly analyze only the most important health indicators. For example, it can use an emotion engine to estimate the user's emotions and, if in a hurry, quickly analyze only the most important health indicators. This allows for more appropriate analysis results by adjusting the data analysis method based on the user's emotions.

[0103] The generation unit can estimate the user's emotions and adjust the care plan generation method based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a care plan that prioritizes stress reduction. For example, the generation unit can estimate the user's emotions using an emotion engine and generate a care plan that prioritizes stress reduction if the user is stressed. The generation unit can also generate a care plan that prioritizes overall health maintenance if the user is relaxed. For example, the generation unit can estimate the user's emotions using an emotion engine and generate a care plan that prioritizes overall health maintenance if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can generate a care plan that can be quickly implemented. For example, the generation unit can estimate the user's emotions using an emotion engine and generate a care plan that can be quickly implemented if the user is in a hurry. In this way, by adjusting the care plan generation method based on the user's emotions, a more appropriate care plan can be provided.

[0104] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit can send a notification in a calm voice. For example, the notification unit can estimate the user's emotions using an emotion engine and send a notification in a calm voice if the user is stressed. The notification unit can also send a notification in a cheerful voice if the user is relaxed. For example, the notification unit can estimate the user's emotions using an emotion engine and send a notification in a cheerful voice if the user is relaxed. Furthermore, if the user is in a hurry, the notification unit can send a quick and concise notification. For example, the notification unit can estimate the user's emotions using an emotion engine and send a quick and concise notification if the user is in a hurry. In this way, by adjusting the notification method based on the user's emotions, more appropriate notifications can be made.

[0105] The notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is stressed, the notification unit will prioritize notifications related to stress reduction. For example, the notification unit uses an emotion engine to estimate the user's emotions and prioritizes notifications related to stress reduction if the user is stressed. The notification unit can also prioritize notifications related to overall health maintenance if the user is relaxed. For example, the notification unit uses an emotion engine to estimate the user's emotions and prioritizes notifications related to overall health maintenance if the user is relaxed. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications that require immediate attention. For example, the notification unit uses an emotion engine to estimate the user's emotions and prioritizes notifications that require immediate attention if the user is in a hurry. In this way, important notifications can be prioritized by determining notification priorities based on the user's emotions.

[0106] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can identify the most effective collection time from the user's past data and collect data during that time. For example, the data collection unit can use generative AI to analyze the user's past health data and identify the optimal collection time. The data collection unit can also identify when specific health indicators fluctuate from the user's past data and collect data accordingly. For example, the data collection unit can use generative AI to analyze the user's past health data and identify when specific health indicators fluctuate. Furthermore, the data collection unit can optimize the collection frequency from the user's past data, efficiently collecting only the necessary data. For example, the data collection unit can use generative AI to analyze the user's past health data and optimize the collection frequency. This allows for efficient data collection by analyzing the user's past health data and selecting the optimal collection method.

[0107] The data collection unit can filter health data based on the user's current lifestyle and environmental conditions. For example, if the user is outside, the data collection unit can limit the data collected, collecting only the minimum necessary data. For instance, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and limit the data collected if the user is outside. Furthermore, if the user is at home, the data collection unit can collect detailed data and consider the influence of the living environment. For example, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and collect detailed data if the user is at home. Additionally, if the user is exercising, the data collection unit can prioritize collecting exercise-related data. For example, the data collection unit can use generative AI to analyze the user's current lifestyle and environmental conditions and prioritize collecting exercise-related data if the user is exercising. This allows for efficient collection of only the necessary data by filtering it based on the user's current lifestyle and environmental conditions.

[0108] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location, when collecting health data. For example, if the user is at high altitude, the data collection unit will prioritize the collection of data such as oxygen concentration and heart rate. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as oxygen concentration and heart rate if the user is at high altitude. The data collection unit can also prioritize the collection of data such as ambient noise and air quality if the user is in an urban area. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as ambient noise and air quality if the user is in an urban area. Furthermore, the data collection unit can prioritize the collection of data such as humidity and temperature if the user is at the beach. For example, the data collection unit will use generative AI to analyze the user's geographical location and prioritize the collection of data such as humidity and temperature if the user is at the beach. By prioritizing the collection of highly relevant data while considering the user's geographical location, a more accurate understanding of the user's health status can be achieved.

[0109] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts on social media indicating they are feeling stressed, the data collection unit can collect stress-related data. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects stress-related data if the user has posted about feeling stressed. The data collection unit can also collect exercise-related data if a user posts on social media about exercise. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects exercise-related data if the user has posted about exercise. Furthermore, the data collection unit can also collect diet-related data if a user posts on social media about food. For example, the data collection unit uses generative AI to analyze a user's social media activity and collects diet-related data if the user has posted about food. By analyzing a user's social media activity and collecting relevant data, the system can more accurately understand the user's health status.

[0110] The analysis unit can adjust the level of detail in its analysis based on the importance of the health data during data analysis. For example, it can perform detailed analysis on important health indicators and simplify other data. For instance, it can use generative AI to assess the importance of health data and perform detailed analysis on important health indicators. The analysis unit can also adjust the frequency of analysis according to the importance of the health data. For example, it can use generative AI to assess the importance of health data and perform frequent analysis on important data. Furthermore, the analysis unit can apply multiple analysis algorithms to important health data to improve accuracy. For example, it can use generative AI to assess the importance of health data and apply multiple analysis algorithms to important data. This allows for efficient data analysis by adjusting the level of detail based on the importance of the health data.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects health status and lifestyle data. This data includes vital signs, meal records, exercise levels, and sleep patterns. The data collection unit monitors the user's vital signs in real time using sensors and records the user's activity level and sleep patterns using a mobile device. The data collection unit also provides an application for the user to input their meal details and collects that data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit uses statistical analysis to understand trends in the user's health status and machine learning algorithms to predict the user's health status. In addition, the analysis unit detects abnormalities in the user's health status based on the collected data and notifies the user of any abnormal values ​​in vital signs. Step 3: The generation unit generates a care plan based on the data analyzed by the analysis unit. The care plan includes a rehabilitation plan, dietary guidance, and exercise program. The generation unit uses generation AI to automatically generate a rehabilitation plan tailored to the user's health condition, provides dietary guidance based on their diet, and creates an exercise program appropriate to their activity level. Step 4: The notification unit sends emergency notifications based on the care plan generated by the generation unit. The notification unit sends notifications to caregivers and medical institutions if the user's health condition suddenly changes or if vital signs show abnormal values. It also sends emergency notifications if the user falls and sends regular notifications according to the user's health condition.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's health status and lifestyle data using the sensors of the smart device 14 or a mobile device. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates a care plan based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The notification unit sends an emergency notification based on the care plan generated by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's health status and lifestyle data using the sensors of the smart glasses 214 and a mobile device. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates a care plan based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The notification unit provides emergency notifications based on the care plan generated by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's health status and lifestyle data using the sensors of the headset terminal 314 and a mobile device. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates a care plan based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The notification unit sends an emergency notification based on the care plan generated by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's health status and lifestyle data using the robot 414's sensors and mobile devices. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates a care plan based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The notification unit provides emergency notifications based on the care plan generated by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The collection department collects health status and lifestyle data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a care plan based on the data analyzed by the analysis unit, A notification unit that issues emergency notifications based on the care plan generated by the generation unit, Equipped with A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates rehabilitation plans and daily support procedures automatically. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Provide real-time notifications in emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We provide continuous feedback based on the collected data and update care plans accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We propose the best way to utilize local resources and grant information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate care plans that streamline resource management for facilities and support organizations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During data analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing data, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing data, prioritize the analysis based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During data analysis, adjust the order of analysis based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts the care plan generation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a care plan, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating care plans, different generation algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the user's emotions and prioritizes care plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating care plans, the priority of generation is determined based on when health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating care plans, the order of generation is adjusted based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, the system prioritizes notifications based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the order of notifications will be adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending a notification, the system will select the most suitable notification method, taking into account the user's current location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects health status and lifestyle data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a care plan based on the data analyzed by the analysis unit, A notification unit that issues emergency notifications based on the care plan generated by the generation unit, Equipped with A system characterized by the following features.

2. The generating unit is The AI ​​generates rehabilitation plans and daily support procedures automatically. The system according to feature 1.

3. The aforementioned notification unit, Provide real-time notifications in emergencies. The system according to feature 1.

4. The generating unit is We provide continuous feedback based on the collected data and update care plans accordingly. The system according to feature 1.

5. The generating unit is We propose the best way to utilize local resources and grant information. The system according to feature 1.

6. The generating unit is Generate care plans that streamline resource management for facilities and support organizations. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system according to feature 1.

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