system

Abstract

The system according to this embodiment aims to integrate health management and medication management for the elderly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a reminder unit, and an alert unit. The collection unit collects vital data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides health advice based on the analysis results obtained by the analysis unit. The reminder unit provides medication reminders. The alert unit issues an alert when an abnormality is detected.

Description

Technical Field

[0006] , , ,

[0005] , ,

[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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can integrate health management and medication management for the elderly. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The integrated AI agent assistant service integrating health management and medication management according to an embodiment of the present invention is a system that integrates health management and medication management for the elderly. This system uses a wearable device to monitor the vital data of the elderly in real time, and a generating AI analyzes the monitored data to evaluate their health status. Based on the evaluation results, the generating AI provides health advice to the elderly and, if necessary, reminds them to take their medication. In addition, if an abnormality is detected, the generating AI sends an alert to the caregiver. This mechanism makes health management for the elderly easier and reduces the burden on caregivers. For example, vital data such as the elderly person's heart rate, blood pressure, and body temperature are collected using a wearable device. This allows for constant monitoring of the elderly person's health status. Next, the generating AI analyzes the monitored data and detects abnormal values ​​and trends. For example, it can detect abnormalities such as a heart rate that is higher than normal or a sudden change in blood pressure. Based on the evaluation results, the generating AI provides the elderly person with appropriate exercise and dietary advice. In addition, the generating AI prevents forgetting to take medication by providing a reminder when it is time to take it. Furthermore, if an abnormality is detected, the AI ​​will send an alert to the caregiver. For example, if the heart rate is abnormally high or blood pressure fluctuates rapidly, the system will notify the caregiver, enabling a quick response. This system makes health management for the elderly easier and reduces the burden on caregivers. The elderly can monitor their health status in real time and receive appropriate advice. Also, caregivers can respond quickly when an abnormality is detected, allowing them to support the elderly with peace of mind. In this way, the integrated AI agent assistant service, which combines health management and medication management, can make health management for the elderly easier and reduce the burden on caregivers.

[0029] The integrated AI agent assistant service integrating health management and medication management according to the embodiment comprises a data collection unit, an analysis unit, a data provision unit, a reminder unit, and an alert unit. The data collection unit collects vital data. Vital data includes, but is not limited to, heart rate, blood pressure, and body temperature. The data collection unit can, for example, measure heart rate using a wearable device. The data collection unit can also measure blood pressure using a blood pressure monitor. Furthermore, the data collection unit can measure body temperature using a thermometer. For example, the data collection unit monitors heart rate in real time using a wearable device and collects data. It measures blood pressure using a blood pressure monitor and collects data. It measures body temperature using a thermometer and collects data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using, for example, a generative AI and detects abnormal values ​​and trends. The generative AI detects abnormal values ​​and trends based on the collected data. For example, the analysis unit can detect abnormalities such as when the heart rate is higher than normal or when blood pressure fluctuates rapidly. The provision unit provides health advice based on the analysis results obtained by the analysis unit. The provision unit provides appropriate exercise and diet advice, for example, using a generation AI. The generation AI provides appropriate exercise and diet advice based on the collected data. For example, the provision unit provides appropriate exercise and diet advice using a generation AI. The reminder unit provides medication reminders. The reminder unit provides reminders, for example, using a generation AI when it is time to take medication. The generation AI provides reminders, for example, using the collected data when it is time to take medication. For example, the reminder unit provides reminders, for example, using a generation AI when it is time to take medication. The alert unit issues an alert when an abnormality is detected. The alert unit, for example, uses a generation AI to issue an alert to caregivers when the heart rate is abnormally high or when blood pressure fluctuates rapidly. The generation AI issues an alert to caregivers when the heart rate is abnormally high or when blood pressure fluctuates rapidly, based on the collected data. For example, the alert unit uses a generation AI to send an alert to the caregiver if the heart rate is abnormally high or if blood pressure fluctuates rapidly.As a result, the integrated AI agent assistant service, which combines health management and medication management according to the embodiment, can facilitate health management for the elderly and reduce the burden on caregivers.

[0030] The data collection unit collects vital data. This vital data includes, but is not limited to, heart rate, blood pressure, and body temperature. For example, the data collection unit can measure heart rate using a wearable device. Wearable devices are wristwatches or bracelets with built-in heart rate sensors. This allows for real-time monitoring of the user's heart rate and data collection. Furthermore, the data collection unit can also measure blood pressure using a blood pressure monitor. Blood pressure monitors are worn on the upper arm or wrist and display the measurement results digitally. This allows for accurate measurement of the user's blood pressure and data collection. The data collection unit can also measure body temperature using a thermometer. Thermometers are used orally, under the armpit, or in the ear, allowing for rapid and accurate temperature measurement. For example, the data collection unit can monitor heart rate in real time using a wearable device and collect data. It can also measure blood pressure using a blood pressure monitor and collect data. It can measure body temperature using a thermometer and collect data. This allows the data collection unit to collect the user's vital data from multiple perspectives and gain a comprehensive understanding of their health status. Furthermore, the data collection unit can transmit this data to a cloud server for centralized management. This allows the analysis and provisioning units to access the collected data, strengthening the overall system coordination. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if a user is performing a specific activity, such as exercising or sleeping, increasing the data collection frequency allows for a more detailed understanding of their health status. 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. For example, the analysis unit uses generative AI to analyze the collected data and detect anomalies and trends. The generative AI detects anomalies and trends based on the collected data. Specifically, the generative AI analyzes vital data such as heart rate, blood pressure, and body temperature to detect anomalies and trends. For example, the analysis unit can detect anomalies such as a higher-than-normal heart rate or rapid fluctuations in blood pressure. The generative AI learns algorithms for detecting anomalies and trends based on past data and statistical information. This allows the analysis unit to quickly and accurately analyze collected data and detect anomalies and trends. Furthermore, the analysis unit can not only detect anomalies and trends but also predict long-term changes in health status. For example, based on past data, the analysis unit can predict how a user's health status will change and assess future risks. This allows the analysis unit to comprehensively understand the user's health status and provide information for taking appropriate measures. Additionally, the analysis unit can immediately issue alerts when it detects anomalies or trends. This allows users and caregivers to respond quickly when an abnormality occurs. Furthermore, the analysis unit can visualize the user's health status based on the collected data. For example, the analysis unit can display data such as heart rate, blood pressure, and body temperature in graphs and charts, allowing users to understand their health status at a glance. In this way, the analysis unit can support the user's health management and contribute to improving their health status.

[0032] The service provider provides health advice based on the analysis results obtained by the analysis unit. For example, the service provider uses a generative AI to provide appropriate exercise and dietary advice. The generative AI provides appropriate exercise and dietary advice based on collected data. Specifically, the generative AI analyzes the user's vital data and provides exercise and dietary advice tailored to the user's health condition. For example, the service provider uses a generative AI to provide appropriate exercise and dietary advice. The generative AI provides exercise and dietary advice tailored to the user's health condition based on data such as the user's heart rate, blood pressure, and body temperature. For example, if the heart rate is high, light exercise is recommended; if blood pressure is high, a low-salt diet is recommended; and if body temperature is high, appropriate hydration is recommended. This allows the service provider to provide appropriate advice tailored to the user's health condition and support their health management. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, by providing feedback on the results of following the provided advice, the generative AI can evaluate the effectiveness of the advice and reflect it in future advice. Furthermore, the service provider can offer customized advice tailored to the user's preferences and lifestyle. For example, if a user likes a particular ingredient, the service provider can suggest recipes using that ingredient. This allows the service provider to more effectively support the user's health management and contribute to improving their health.

[0033] The reminder unit provides medication reminders. For example, it uses a generating AI to send reminders when it's time to take medication. The generating AI uses collected data to send reminders when medication time is approaching. Specifically, the generating AI manages the user's medication schedule and sends reminders when medication time is approaching. For example, the reminder unit uses a generating AI to send reminders when medication time is approaching. The generating AI uses the user's medication schedule to send reminders when medication time is approaching. Reminders are sent via smartphone notifications, voice alerts, vibrations, etc. This ensures users don't forget to take their medication and take it at the appropriate time. Furthermore, the reminder unit can collect user feedback and continuously improve the accuracy and effectiveness of reminders. For example, by recording the user's actions after receiving a reminder and analyzing that data with the generating AI, the timing and method of reminders can be optimized. The reminder unit can also flexibly adjust the timing of reminders according to the user's lifestyle and daily schedule. For example, if a user is busy during a specific time period, reminders can be scheduled to avoid that time. This allows the reminder function to support the user's medication management and contribute to maintaining their health.

[0034] The alert unit issues an alert when an abnormality is detected. For example, the alert unit uses a generating AI to send an alert to caregivers when the heart rate is abnormally high or when blood pressure fluctuates rapidly. The generating AI uses collected data to send an alert to caregivers when the heart rate is abnormally high or when blood pressure fluctuates rapidly. Specifically, the generating AI monitors the user's vital data in real time and immediately issues an alert when an abnormal value is detected. For example, if the heart rate exceeds the normal range or if blood pressure rises or falls rapidly, the generating AI detects the abnormality and sends an alert to the caregiver or medical institution. The alert is sent via smartphone notification, email, or voice call. This allows caregivers and medical institutions to quickly understand the user's abnormality and take appropriate action. Furthermore, the alert unit can also provide response procedures when an abnormality is detected. For example, if the heart rate is abnormally high, it will instruct the user to rest and recommend contacting a medical institution if necessary. Furthermore, the alert unit can predict future risks based on past anomaly data and issue preventative alerts. This allows the alert unit to continuously monitor the user's health status and support a quick and appropriate response when an anomaly occurs. In addition, the alert unit can collect user feedback and continuously improve the accuracy and effectiveness of alerts. For example, by recording the results of responses after an alert is issued and analyzing this data with a generating AI, the timing and method of alerts can be optimized. In this way, the alert unit can support the user's health management and contribute to maintaining their health status.

[0035] The data collection unit can collect vital data such as heart rate, blood pressure, and body temperature. For example, the data collection unit can use a wearable device to measure heart rate. For example, the data collection unit can monitor heart rate in real time and collect data. The data collection unit can also use a blood pressure monitor to measure blood pressure. For example, the data collection unit can measure blood pressure and collect data. Furthermore, the data collection unit can use a thermometer to measure body temperature. For example, the data collection unit can measure body temperature and collect data. In this way, the data collection unit can constantly monitor the user's health status by collecting vital data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input heart rate data acquired from a wearable device into a generating AI, which can analyze the heart rate data and detect abnormalities.

[0036] The analysis unit can detect outliers and trends based on the collected data. For example, the analysis unit can analyze the collected data using a generative AI to detect outliers and trends. The generative AI detects outliers and trends based on the collected data. For example, the analysis unit can detect anomalies such as when the heart rate is higher than normal or when blood pressure fluctuates rapidly. The analysis unit can also analyze trends in the collected data to detect changes in health status. For example, the analysis unit can analyze heart rate trends to detect changes in health status. As a result, the analysis unit can evaluate the user's health status by detecting outliers and trends. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then analyze the data to detect outliers and trends.

[0037] The service provider can provide appropriate exercise and dietary advice. For example, the service provider can use a generative AI to provide appropriate exercise and dietary advice. The generative AI provides appropriate exercise and dietary advice based on the collected data. For example, the service provider can use a generative AI to provide appropriate exercise and dietary advice. For example, if the user's heart rate is higher than normal, the service provider can provide appropriate exercise advice. Furthermore, if the user's blood pressure fluctuates rapidly, the service provider can provide appropriate dietary advice. In this way, the service provider can support the user's health by providing appropriate exercise and dietary advice. Some or all of the above processing in the service provider may be performed using a generative AI, or without one. For example, the service provider can input collected data into a generative AI, which can then analyze the data to provide appropriate exercise and dietary advice.

[0038] The reminder unit can issue a reminder when it is time to take medication. The reminder unit can issue a reminder when it is time to take medication, for example, by using a generation AI. The generation AI issues a reminder when it is time to take medication, based on the collected data. For example, the reminder unit can issue a reminder when it is time to take medication, for example, by issuing a reminder when it is time to take medication. The reminder unit can also issue a reminder again if the user has missed the time to take medication. Furthermore, the reminder unit allows the user to set the time to take medication. For example, the reminder unit can issue a reminder at the time the user sets the time to take medication. In this way, the reminder unit can prevent users from forgetting to take medication by reminding them of the time to take medication. Some or all of the above processing in the reminder unit may be performed using a generation AI, for example, or without using a generation AI. For example, the reminder unit inputs the collected data into a generating AI, which analyzes the data and can then send a reminder when it's time to take medication.

[0039] The alert unit can send alerts to caregivers if the heart rate is abnormally high or if blood pressure fluctuates rapidly. For example, the alert unit uses a generation AI to send alerts to caregivers if the heart rate is abnormally high or if blood pressure fluctuates rapidly. The generation AI uses collected data to send alerts to caregivers if the heart rate is abnormally high or if blood pressure fluctuates rapidly. For example, the alert unit can send alerts to caregivers if the heart rate is abnormally high, enabling a quick response. The alert unit can also send alerts to caregivers if blood pressure fluctuates rapidly. Furthermore, the alert unit can send alerts to caregivers if an abnormality is detected. For example, if an abnormality is detected, the alert unit can send an alert to caregivers to encourage a quick response. This allows for a quick response by sending alerts when an abnormality is detected. Some or all of the processing described above in the alert unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the alert unit can input collected data into a generating AI, and the generating AI can analyze the data and issue an alert if an anomaly is detected.

[0040] The data collection unit can analyze the user's past health data and select the optimal collection method. The data collection unit can analyze the user's past health data, for example, using a generative AI. The generative AI analyzes the user's past health data based on the collected data. For example, the data collection unit can analyze the user's past health data using a generative AI. The data collection unit can analyze the user's past heart rate data and concentrate data collection during times when abnormalities are likely to occur. The data collection unit can also adjust the collection frequency based on the user's past blood pressure data. Furthermore, the data collection unit can select a collection method appropriate to the season and time of day, taking into account the user's past body temperature data. For example, the data collection unit can select a collection method appropriate to the season and time of day, taking into account the user's past body temperature data, and collect the data. In this way, the data collection unit can select the optimal collection method by analyzing past health data. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without using a generative AI. For example, the data collection unit can input the user's past health data into a generative AI, which can analyze the data and select the optimal collection method.

[0041] The data collection unit can filter vital data based on the user's current activity level and environment. For example, the data collection unit can analyze the user's current activity level and environment using a generative AI. The generative AI analyzes the user's current activity level and environment based on the collected data. For example, the data collection unit can analyze the user's current activity level and environment using a generative AI. For example, if the user is exercising, the data collection unit can collect only post-exercise data. Also, if the user is sleeping, the data collection unit can prioritize collecting data during sleep. Furthermore, if the user is outside, the data collection unit can consider environmental data from the location when collecting data. For example, if the user is outside, the data collection unit can consider environmental data from the location when collecting data and filter the data. This allows the data collection unit to collect more accurate data by filtering data based on activity level and environment. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without using a generative AI. For example, the data collection unit can input the user's current activity status and environmental data into the generating AI, which can then analyze and filter the data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting vital data. The data collection unit analyzes the user's geographical location using, for example, a generative AI. The generative AI analyzes the user's geographical location based on the collected data. For example, the data collection unit can prioritize the collection of oxygen saturation data if the user is at high altitude. The data collection unit can also prioritize the collection of heart rate and blood pressure data if the user is in an urban area. Furthermore, the data collection unit can prioritize the collection of body temperature and respiratory rate data if the user is at home. For example, the data collection unit can prioritize the collection of body temperature and respiratory rate data if the user is at home. In this way, the data collection unit can prioritize the collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the data and prioritize collecting highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting vital data. The data collection unit can analyze the user's social media activity using, for example, a generative AI. The generative AI analyzes the user's social media activity based on the collected data. For example, the data collection unit can analyze the user's social media activity using a generative AI. For example, if the user posts about feeling stressed, the data collection unit can collect heart rate and blood pressure data. Also, if the user posts about relaxing, the data collection unit can collect body temperature and respiratory rate data. Furthermore, if the user posts about exercise, the data collection unit can collect heart rate and oxygen saturation data. For example, if the user posts about exercise, the data collection unit can collect heart rate and oxygen saturation data. In this way, the data collection unit can collect relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can analyze the data and collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the vital data during the analysis. For example, the analysis unit uses a generation AI to evaluate the importance of the vital data. The generation AI evaluates the importance of the vital data based on the collected data. For example, the analysis unit uses a generation AI to evaluate the importance of the vital data. For example, the analysis unit can perform a detailed analysis if the heart rate is abnormally high. The analysis unit can also perform a detailed analysis if the blood pressure fluctuates rapidly. Furthermore, the analysis unit can perform a detailed analysis if the body temperature is higher than normal. For example, the analysis unit can perform a detailed analysis and analyze the data if the body temperature is higher than normal. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the vital data. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input vital data into a generation AI, which can analyze the data, evaluate its importance, and adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of vital data during analysis. For example, the analysis unit classifies the categories of vital data using a generation AI. The generation AI classifies the categories of vital data based on the collected data. For example, the analysis unit can classify the categories of vital data using a generation AI. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. Furthermore, the analysis unit can apply a body temperature variability analysis algorithm to body temperature data. For example, the analysis unit can apply a body temperature variability analysis algorithm to body temperature data and analyze the data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the category of vital data. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or without using a generation AI. For example, the analysis unit can input vital data into a generation AI, the generation AI can analyze the data and classify the categories, and then apply different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the timing of vital data collection during analysis. For example, the analysis unit can use a generation AI to evaluate the timing of vital data collection. The generation AI evaluates the timing of vital data collection based on the collected data. For example, the analysis unit can use a generation AI to evaluate the timing of vital data collection. The analysis unit can, for example, prioritize the analysis of recently collected data. The analysis unit can also prioritize the analysis of data in which abnormalities have been detected. Furthermore, the analysis unit can prioritize the analysis of data that shows abnormalities when compared to past data. For example, the analysis unit can prioritize the analysis of data that shows abnormalities when compared to past data. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis based on the timing of vital data collection. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input vital data into a generation AI, which can analyze the data, evaluate the collection timing, and determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relationships between vital data during analysis. For example, the analysis unit can use a generative AI to evaluate the relationships between vital data. The generative AI evaluates the relationships between vital data based on the collected data. For example, the analysis unit can use a generative AI to evaluate the relationships between vital data. For example, the analysis unit can analyze data relating heart rate and blood pressure. The analysis unit can also analyze data relating body temperature and respiratory rate. Furthermore, the analysis unit can analyze data relating oxygen saturation and heart rate. For example, the analysis unit can analyze data relating oxygen saturation and heart rate. This allows the analysis unit to efficiently analyze related data by adjusting the order of analysis based on the relationships between vital data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or without using a generative AI. For example, the analysis unit can input vital data into a generative AI, which can analyze the data, evaluate the relationships, and adjust the order of analysis.

[0048] The service provider can adjust the level of detail of health advice based on the user's health condition when providing it. For example, the service provider can use a generative AI to evaluate the user's health condition. The generative AI evaluates the user's health condition based on the collected data. For example, the service provider can use a generative AI to evaluate the user's health condition. For example, if the user's heart rate is abnormally high, the service provider can provide detailed exercise advice. The service provider can also provide detailed dietary advice if the user's blood pressure fluctuates rapidly. Furthermore, if the user's body temperature is higher than normal, the service provider can provide detailed rest advice. For example, if the user's body temperature is higher than normal, the service provider can provide detailed rest advice and health advice. This allows the service provider to provide more appropriate advice by adjusting the level of detail of the advice based on the user's health condition. Some or all of the above processing in the service provider may be performed using a generative AI, or without using a generative AI. For example, the service provider can input the user's health data into a generative AI, which can analyze the data to evaluate the health condition and adjust the level of detail of the advice.

[0049] The service provider can apply different advice algorithms depending on the user's lifestyle when providing health advice. For example, the service provider can use a generative AI to evaluate the user's lifestyle. The generative AI evaluates the user's lifestyle based on the collected data. For example, the service provider can use a generative AI to evaluate the user's lifestyle. For example, if the user has an exercise habit, the service provider can apply an exercise advice algorithm. The service provider can also apply a diet advice algorithm if the user is mindful of their diet. Furthermore, if the user is prone to stress, the service provider can apply a stress management advice algorithm. For example, if the service provider is prone to stress, the service provider can apply a stress management advice algorithm and provide health advice. In this way, the service provider can provide more appropriate advice by applying different advice algorithms depending on the user's lifestyle. Some or all of the above processing in the service provider may be performed using a generative AI, or without using a generative AI. For example, the service provider can input the user's lifestyle data into a generative AI, which can analyze the data to evaluate the lifestyle and apply different advice algorithms.

[0050] The service provider can determine the priority of health advice based on when the user's health data is collected. For example, the service provider can use a generative AI to evaluate when the user's health data is collected. The generative AI evaluates when the user's health data is collected based on the collected data. For example, the service provider can use a generative AI to evaluate when the user's health data is collected. The service provider can, for example, provide advice based on recently collected data. The service provider can also provide advice based on data in which anomalies have been detected. Furthermore, the service provider can provide advice based on data that shows anomalies when compared to past data. For example, the service provider can provide health advice based on data that shows anomalies when compared to past data. This allows the service provider to prioritize important advice by determining the priority of advice based on when the user's health data is collected. Some or all of the above processing in the service provider may be performed using a generative AI, for example, or without a generative AI. For example, the service provider can input the user's health data into a generative AI, which can analyze the data, evaluate the collection timing, and determine the priority of advice.

[0051] The service provider can adjust the order of advice based on the user's relevant data when providing health advice. The service provider can, for example, use a generative AI to evaluate the user's relevant data. The generative AI evaluates the user's relevant data based on the collected data. For example, the service provider can use a generative AI to evaluate the user's relevant data. The service provider can, for example, provide advice by associating heart rate and blood pressure data. The service provider can also provide advice by associating body temperature and respiratory rate data. Furthermore, the service provider can provide advice by associating oxygen saturation and heart rate data. For example, the service provider can provide health advice by associating oxygen saturation and heart rate data. This allows the service provider to provide more appropriate advice by adjusting the order of advice based on the user's relevant data. Some or all of the above processing in the service provider may be performed using a generative AI, for example, or without a generative AI. For example, the service provider can input the user's relevant data into a generative AI, which can analyze the data, evaluate its relevance, and adjust the order of advice.

[0052] The reminder unit can adjust the level of detail of reminders based on the user's medication history. The reminder unit evaluates the user's medication history, for example, using a generative AI. The generative AI evaluates the user's medication history based on the collected data. For example, the reminder unit evaluates the user's medication history using a generative AI. The reminder unit can provide a detailed reminder if the user has forgotten to take their medication in the past. The reminder unit can also provide a concise reminder if the user does not forget to take their medication. Furthermore, the reminder unit can provide detailed reminders during times when the user is likely to forget to take their medication. For example, the reminder unit can provide a detailed reminder and send a reminder during times when the user is likely to forget to take their medication. This allows the reminder unit to provide more appropriate reminders by adjusting the level of detail of reminders based on the user's medication history. Some or all of the above processing in the reminder unit may be performed using a generative AI, for example, or without using a generative AI. For example, the reminder unit inputs the user's medication history data into a generating AI, which then analyzes the data to adjust the level of detail in the reminders.

[0053] The reminder unit can apply different reminder algorithms depending on the user's lifestyle when sending reminders. The reminder unit can evaluate the user's lifestyle using, for example, a generative AI. The generative AI evaluates the user's lifestyle based on the collected data. For example, the reminder unit can evaluate the user's lifestyle using the generative AI. The reminder unit can provide regular reminders if the user leads a regular life. It can also provide flexible reminders if the user leads an irregular life. Furthermore, if the user is traveling, the reminder unit can provide reminders that are time-sensitive to the user's travel destination. For example, if the user is traveling, the reminder unit can provide and send reminders that are time-sensitive to the user's travel destination. This allows the reminder unit to provide more appropriate reminders by applying different reminder algorithms depending on the user's lifestyle. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input the user's lifestyle data into a generative AI, which can analyze the data to evaluate the lifestyle and apply different reminder algorithms.

[0054] The reminder unit can determine the priority of reminders based on when the user's medication data is collected. The reminder unit can, for example, use a generative AI to evaluate when the user's medication data is collected. The generative AI evaluates when the user's medication data is collected based on the collected data. For example, the reminder unit can use a generative AI to evaluate when the user's medication data is collected. The reminder unit can, for example, provide reminders based on recently collected medication data. The reminder unit can also provide reminders based on medication data in which abnormalities have been detected. Furthermore, the reminder unit can provide reminders based on medication data that shows abnormalities when compared to past data. For example, the reminder unit can provide and issue reminders based on medication data that shows abnormalities when compared to past data. This allows the reminder unit to prioritize important reminders by determining the priority of reminders based on when the user's medication data is collected. Some or all of the above processing in the reminder unit may be performed using a generative AI, for example, or without using a generative AI. For example, the reminder unit can input the user's medication data into a generating AI, which can then analyze the data, evaluate the timing of data collection, and determine the priority of reminders.

[0055] The reminder unit can adjust the order of reminders based on the user's relevant data when sending reminders. The reminder unit can evaluate the user's relevant data using, for example, a generative AI. The generative AI evaluates the user's relevant data based on the collected data. For example, the reminder unit can evaluate the user's relevant data using the generative AI. The reminder unit can provide reminders by associating heart rate and blood pressure data. It can also provide reminders by associating body temperature and respiratory rate data. Furthermore, the reminder unit can provide reminders by associating oxygen saturation and heart rate data. For example, the reminder unit can provide and send reminders by associating oxygen saturation and heart rate data. This allows the reminder unit to provide more appropriate reminders by adjusting the order of reminders based on the user's relevant data. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reminder unit can input the user's relevant data into the generative AI, which can analyze the data, evaluate the relevance, and adjust the order of reminders.

[0056] The alert unit can adjust the level of detail of an alert based on the user's health data when issuing an alert. The alert unit evaluates the user's health data using, for example, a generative AI. The generative AI evaluates the user's health data based on the collected data. For example, the alert unit evaluates the user's health data using the generative AI. The alert unit can provide a detailed alert if, for example, the user's heart rate is abnormally high. The alert unit can also provide a detailed alert if the user's blood pressure fluctuates rapidly. Furthermore, the alert unit can provide a detailed alert if the user's body temperature is higher than normal. For example, the alert unit can provide a detailed alert and issue an alert if the user's body temperature is higher than normal. This allows the alert unit to provide more appropriate alerts by adjusting the level of detail of the alert based on the user's health data. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the alert unit can input the user's health data into the generative AI, which can analyze the data and adjust the level of detail of the alert.

[0057] The alert unit can apply different alert algorithms depending on the user's lifestyle when issuing an alert. For example, the alert unit can use a generative AI to evaluate the user's lifestyle. The generative AI evaluates the user's lifestyle based on the collected data. For example, the alert unit can use a generative AI to evaluate the user's lifestyle. For example, if the user lives a regular life, the alert unit can provide regular alerts. The alert unit can also provide flexible alerts if the user lives an irregular life. Furthermore, if the user is traveling, the alert unit can provide alerts tailored to the time zone of the travel destination. For example, if the user is traveling, the alert unit can provide and issue alerts tailored to the time zone of the travel destination. This allows the alert unit to provide more appropriate alerts by applying different alert algorithms depending on the user's lifestyle. Some or all of the above processing in the alert unit may be performed using a generative AI, or without using a generative AI. For example, the alert unit can input user lifestyle data into a generative AI, which can analyze the data to evaluate the lifestyle and apply different alert algorithms.

[0058] The alert unit can determine the priority of alerts based on when the user's health data is collected when an alert is issued. For example, the alert unit may use a generative AI to evaluate when the user's health data is collected. The generative AI evaluates when the user's health data is collected based on the collected data. For example, the alert unit may use a generative AI to evaluate when the user's health data is collected. The alert unit may provide alerts based on recently collected data. The alert unit may also provide alerts based on data in which anomalies have been detected. Furthermore, the alert unit may provide alerts based on data that shows anomalies when compared to past data. For example, the alert unit may provide and issue alerts based on data that shows anomalies when compared to past data. This allows the alert unit to prioritize important alerts by determining the priority of alerts based on when the user's health data is collected. Some or all of the above processing in the alert unit may be performed using a generative AI, or not using a generative AI. For example, the alert unit may input the user's health data into a generative AI, which may analyze the data to evaluate the collection timing and determine the priority of alerts.

[0059] The alert unit can adjust the order of alerts based on the user's relevant data when an alert is issued. The alert unit evaluates the user's relevant data using, for example, a generative AI. The generative AI evaluates the user's relevant data based on the collected data. For example, the alert unit evaluates the user's relevant data using the generative AI. The alert unit can provide an alert by associating heart rate and blood pressure data. The alert unit can also provide an alert by associating body temperature and respiratory rate data. Furthermore, the alert unit can provide an alert by associating oxygen saturation and heart rate data. For example, the alert unit can provide an alert by associating oxygen saturation and heart rate data and issue an alert. This allows the alert unit to provide more appropriate alerts by adjusting the order of alerts based on the user's relevant data. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the alert unit can input the user's relevant data into the generative AI, which can analyze the data, evaluate the relevance, and adjust the order of alerts.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, it can analyze the user's past heart rate data and concentrate data collection during times when abnormalities are likely to occur. It can also adjust the collection frequency based on the user's past blood pressure data. Furthermore, it can select a collection method appropriate for the season and time of day by referring to the user's past body temperature data. In this way, the data collection unit can select the optimal collection method by analyzing past health data.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the vital data during the analysis. For example, if the heart rate is abnormally high, a detailed analysis can be performed. Similarly, if blood pressure fluctuates rapidly, a detailed analysis can be performed. Furthermore, if body temperature is higher than normal, a detailed analysis can be performed. In this way, the analysis unit can perform detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the vital data.

[0063] The service provider can adjust the level of detail in health advice based on the user's health condition. For example, if a user's heart rate is abnormally high, it can provide detailed exercise advice. Similarly, if a user's blood pressure fluctuates rapidly, it can provide detailed dietary advice. Furthermore, if a user's body temperature is higher than normal, it can provide detailed rest advice. This allows the service provider to offer more appropriate advice by adjusting the level of detail based on the user's health condition.

[0064] The reminder function can adjust the level of detail in reminders based on the user's medication history. For example, if a user has forgotten to take their medication in the past, it can provide a detailed reminder. Conversely, if the user does not forget to take their medication, it can provide a concise reminder. Furthermore, it can provide detailed reminders during times when the user is likely to forget to take their medication. In this way, the reminder function can provide more appropriate reminders by adjusting the level of detail based on the user's medication history.

[0065] The alert unit can adjust the level of detail of an alert based on the user's health data when an alert is issued. For example, if the user's heart rate is abnormally high, a detailed alert can be provided. Similarly, if the user's blood pressure fluctuates rapidly, a detailed alert can be provided. Furthermore, if the user's body temperature is higher than normal, a detailed alert can be provided. In this way, the alert unit can provide more appropriate alerts by adjusting the level of detail of the alert based on the user's health data.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects vital data. This vital data includes heart rate, blood pressure, and body temperature. The data collection unit measures heart rate using a wearable device, blood pressure using a blood pressure monitor, and body temperature using a thermometer. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses a generation AI to analyze the collected data and detect anomalies and trends. For example, it can detect anomalies such as a heart rate that is higher than normal or a sudden change in blood pressure. Step 3: The service provider provides health advice based on the analysis results obtained by the analysis unit. The service provider uses a generating AI to provide appropriate exercise and dietary advice. Step 4: The reminder unit provides medication reminders. The reminder unit uses a generation AI to send reminders when it's time to take the medication. Step 5: The alert unit issues an alert when an abnormality is detected. The alert unit uses AI generation to send an alert to the caregiver if the heart rate is abnormally high or if blood pressure fluctuates rapidly.

[0068] (Example of form 2) The integrated AI agent assistant service integrating health management and medication management according to an embodiment of the present invention is a system that integrates health management and medication management for the elderly. This system uses a wearable device to monitor the vital data of the elderly in real time, and a generating AI analyzes the monitored data to evaluate their health status. Based on the evaluation results, the generating AI provides health advice to the elderly and, if necessary, reminds them to take their medication. In addition, if an abnormality is detected, the generating AI sends an alert to the caregiver. This mechanism makes health management for the elderly easier and reduces the burden on caregivers. For example, vital data such as the elderly person's heart rate, blood pressure, and body temperature are collected using a wearable device. This allows for constant monitoring of the elderly person's health status. Next, the generating AI analyzes the monitored data and detects abnormal values ​​and trends. For example, it can detect abnormalities such as a heart rate that is higher than normal or a sudden change in blood pressure. Based on the evaluation results, the generating AI provides the elderly person with appropriate exercise and dietary advice. In addition, the generating AI prevents forgetting to take medication by providing a reminder when it is time to take it. Furthermore, if an abnormality is detected, the AI ​​will send an alert to the caregiver. For example, if the heart rate is abnormally high or blood pressure fluctuates rapidly, the system will notify the caregiver, enabling a quick response. This system makes health management for the elderly easier and reduces the burden on caregivers. The elderly can monitor their health status in real time and receive appropriate advice. Also, caregivers can respond quickly when an abnormality is detected, allowing them to support the elderly with peace of mind. In this way, the integrated AI agent assistant service, which combines health management and medication management, can make health management for the elderly easier and reduce the burden on caregivers.

[0069] The integrated AI agent assistant service integrating health management and medication management according to the embodiment comprises a data collection unit, an analysis unit, a data provision unit, a reminder unit, and an alert unit. The data collection unit collects vital data. Vital data includes, but is not limited to, heart rate, blood pressure, and body temperature. The data collection unit can, for example, measure heart rate using a wearable device. The data collection unit can also measure blood pressure using a blood pressure monitor. Furthermore, the data collection unit can measure body temperature using a thermometer. For example, the data collection unit monitors heart rate in real time using a wearable device and collects data. It measures blood pressure using a blood pressure monitor and collects data. It measures body temperature using a thermometer and collects data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using, for example, a generative AI and detects abnormal values ​​and trends. The generative AI detects abnormal values ​​and trends based on the collected data. For example, the analysis unit can detect abnormalities such as when the heart rate is higher than normal or when blood pressure fluctuates rapidly. The provision unit provides health advice based on the analysis results obtained by the analysis unit. The provision unit provides appropriate exercise and diet advice, for example, using a generation AI. The generation AI provides appropriate exercise and diet advice based on the collected data. For example, the provision unit provides appropriate exercise and diet advice using a generation AI. The reminder unit provides medication reminders. The reminder unit provides reminders, for example, using a generation AI when it is time to take medication. The generation AI provides reminders, for example, using the collected data when it is time to take medication. For example, the reminder unit provides reminders, for example, using a generation AI when it is time to take medication. The alert unit issues an alert when an abnormality is detected. The alert unit, for example, uses a generation AI to issue an alert to caregivers when the heart rate is abnormally high or when blood pressure fluctuates rapidly. The generation AI issues an alert to caregivers when the heart rate is abnormally high or when blood pressure fluctuates rapidly, based on the collected data. For example, the alert unit uses a generation AI to send an alert to the caregiver if the heart rate is abnormally high or if blood pressure fluctuates rapidly.As a result, the integrated AI agent assistant service, which combines health management and medication management according to the embodiment, can facilitate health management for the elderly and reduce the burden on caregivers.

[0070] The data collection unit collects vital data. This vital data includes, but is not limited to, heart rate, blood pressure, and body temperature. For example, the data collection unit can measure heart rate using a wearable device. Wearable devices are wristwatches or bracelets with built-in heart rate sensors. This allows for real-time monitoring of the user's heart rate and data collection. Furthermore, the data collection unit can also measure blood pressure using a blood pressure monitor. Blood pressure monitors are worn on the upper arm or wrist and display the measurement results digitally. This allows for accurate measurement of the user's blood pressure and data collection. The data collection unit can also measure body temperature using a thermometer. Thermometers are used orally, under the armpit, or in the ear, allowing for rapid and accurate temperature measurement. For example, the data collection unit can monitor heart rate in real time using a wearable device and collect data. It can also measure blood pressure using a blood pressure monitor and collect data. It can measure body temperature using a thermometer and collect data. This allows the data collection unit to collect the user's vital data from multiple perspectives and gain a comprehensive understanding of their health status. Furthermore, the data collection unit can transmit this data to a cloud server for centralized management. This allows the analysis and provisioning units to access the collected data, strengthening the overall system coordination. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if a user is performing a specific activity, such as exercising or sleeping, increasing the data collection frequency allows for a more detailed understanding of their health status. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses generative AI to analyze the collected data and detect anomalies and trends. The generative AI detects anomalies and trends based on the collected data. Specifically, the generative AI analyzes vital data such as heart rate, blood pressure, and body temperature to detect anomalies and trends. For example, the analysis unit can detect anomalies such as a higher-than-normal heart rate or rapid fluctuations in blood pressure. The generative AI learns algorithms for detecting anomalies and trends based on past data and statistical information. This allows the analysis unit to quickly and accurately analyze collected data and detect anomalies and trends. Furthermore, the analysis unit can not only detect anomalies and trends but also predict long-term changes in health status. For example, based on past data, the analysis unit can predict how a user's health status will change and assess future risks. This allows the analysis unit to comprehensively understand the user's health status and provide information for taking appropriate measures. Additionally, the analysis unit can immediately issue alerts when it detects anomalies or trends. This allows users and caregivers to respond quickly when an abnormality occurs. Furthermore, the analysis unit can visualize the user's health status based on the collected data. For example, the analysis unit can display data such as heart rate, blood pressure, and body temperature in graphs and charts, allowing users to understand their health status at a glance. In this way, the analysis unit can support the user's health management and contribute to improving their health status.

[0072] The service provider provides health advice based on the analysis results obtained by the analysis unit. For example, the service provider uses a generative AI to provide appropriate exercise and dietary advice. The generative AI provides appropriate exercise and dietary advice based on collected data. Specifically, the generative AI analyzes the user's vital data and provides exercise and dietary advice tailored to the user's health condition. For example, the service provider uses a generative AI to provide appropriate exercise and dietary advice. The generative AI provides exercise and dietary advice tailored to the user's health condition based on data such as the user's heart rate, blood pressure, and body temperature. For example, if the heart rate is high, light exercise is recommended; if blood pressure is high, a low-salt diet is recommended; and if body temperature is high, appropriate hydration is recommended. This allows the service provider to provide appropriate advice tailored to the user's health condition and support their health management. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, by providing feedback on the results of following the provided advice, the generative AI can evaluate the effectiveness of the advice and reflect it in future advice. Furthermore, the service provider can offer customized advice tailored to the user's preferences and lifestyle. For example, if a user likes a particular ingredient, the service provider can suggest recipes using that ingredient. This allows the service provider to more effectively support the user's health management and contribute to improving their health.

[0073] The reminder unit provides medication reminders. For example, it uses a generating AI to send reminders when it's time to take medication. The generating AI uses collected data to send reminders when medication time is approaching. Specifically, the generating AI manages the user's medication schedule and sends reminders when medication time is approaching. For example, the reminder unit uses a generating AI to send reminders when medication time is approaching. The generating AI uses the user's medication schedule to send reminders when medication time is approaching. Reminders are sent via smartphone notifications, voice alerts, vibrations, etc. This ensures users don't forget to take their medication and take it at the appropriate time. Furthermore, the reminder unit can collect user feedback and continuously improve the accuracy and effectiveness of reminders. For example, by recording the user's actions after receiving a reminder and analyzing that data with the generating AI, the timing and method of reminders can be optimized. The reminder unit can also flexibly adjust the timing of reminders according to the user's lifestyle and daily schedule. For example, if a user is busy during a specific time period, reminders can be scheduled to avoid that time. This allows the reminder function to support the user's medication management and contribute to maintaining their health.

[0074] The alert unit issues an alert when an abnormality is detected. For example, the alert unit uses a generating AI to send an alert to caregivers when the heart rate is abnormally high or when blood pressure fluctuates rapidly. The generating AI uses collected data to send an alert to caregivers when the heart rate is abnormally high or when blood pressure fluctuates rapidly. Specifically, the generating AI monitors the user's vital data in real time and immediately issues an alert when an abnormal value is detected. For example, if the heart rate exceeds the normal range or if blood pressure rises or falls rapidly, the generating AI detects the abnormality and sends an alert to the caregiver or medical institution. The alert is sent via smartphone notification, email, or voice call. This allows caregivers and medical institutions to quickly understand the user's abnormality and take appropriate action. Furthermore, the alert unit can also provide response procedures when an abnormality is detected. For example, if the heart rate is abnormally high, it will instruct the user to rest and recommend contacting a medical institution if necessary. Furthermore, the alert unit can predict future risks based on past anomaly data and issue preventative alerts. This allows the alert unit to continuously monitor the user's health status and support a quick and appropriate response when an anomaly occurs. In addition, the alert unit can collect user feedback and continuously improve the accuracy and effectiveness of alerts. For example, by recording the results of responses after an alert is issued and analyzing this data with a generating AI, the timing and method of alerts can be optimized. In this way, the alert unit can support the user's health management and contribute to maintaining their health status.

[0075] The data collection unit can collect vital data such as heart rate, blood pressure, and body temperature. For example, the data collection unit can use a wearable device to measure heart rate. For example, the data collection unit can monitor heart rate in real time and collect data. The data collection unit can also use a blood pressure monitor to measure blood pressure. For example, the data collection unit can measure blood pressure and collect data. Furthermore, the data collection unit can use a thermometer to measure body temperature. For example, the data collection unit can measure body temperature and collect data. In this way, the data collection unit can constantly monitor the user's health status by collecting vital data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input heart rate data acquired from a wearable device into a generating AI, which can analyze the heart rate data and detect abnormalities.

[0076] The analysis unit can detect outliers and trends based on the collected data. For example, the analysis unit can analyze the collected data using a generative AI to detect outliers and trends. The generative AI detects outliers and trends based on the collected data. For example, the analysis unit can detect anomalies such as when the heart rate is higher than normal or when blood pressure fluctuates rapidly. The analysis unit can also analyze trends in the collected data to detect changes in health status. For example, the analysis unit can analyze heart rate trends to detect changes in health status. As a result, the analysis unit can evaluate the user's health status by detecting outliers and trends. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then analyze the data to detect outliers and trends.

[0077] The service provider can provide appropriate exercise and dietary advice. For example, the service provider can use a generative AI to provide appropriate exercise and dietary advice. The generative AI provides appropriate exercise and dietary advice based on the collected data. For example, the service provider can use a generative AI to provide appropriate exercise and dietary advice. For example, if the user's heart rate is higher than normal, the service provider can provide appropriate exercise advice. Furthermore, if the user's blood pressure fluctuates rapidly, the service provider can provide appropriate dietary advice. In this way, the service provider can support the user's health by providing appropriate exercise and dietary advice. Some or all of the above processing in the service provider may be performed using a generative AI, or without one. For example, the service provider can input collected data into a generative AI, which can then analyze the data to provide appropriate exercise and dietary advice.

[0078] The reminder unit can issue a reminder when it is time to take medication. The reminder unit can issue a reminder when it is time to take medication, for example, by using a generation AI. The generation AI issues a reminder when it is time to take medication, based on the collected data. For example, the reminder unit can issue a reminder when it is time to take medication, for example, by issuing a reminder when it is time to take medication. The reminder unit can also issue a reminder again if the user has missed the time to take medication. Furthermore, the reminder unit allows the user to set the time to take medication. For example, the reminder unit can issue a reminder at the time the user sets the time to take medication. In this way, the reminder unit can prevent users from forgetting to take medication by reminding them of the time to take medication. Some or all of the above processing in the reminder unit may be performed using a generation AI, for example, or without using a generation AI. For example, the reminder unit inputs the collected data into a generating AI, which analyzes the data and can then send a reminder when it's time to take medication.

[0079] The alert unit can send alerts to caregivers if the heart rate is abnormally high or if blood pressure fluctuates rapidly. For example, the alert unit uses a generation AI to send alerts to caregivers if the heart rate is abnormally high or if blood pressure fluctuates rapidly. The generation AI uses collected data to send alerts to caregivers if the heart rate is abnormally high or if blood pressure fluctuates rapidly. For example, the alert unit can send alerts to caregivers if the heart rate is abnormally high, enabling a quick response. The alert unit can also send alerts to caregivers if blood pressure fluctuates rapidly. Furthermore, the alert unit can send alerts to caregivers if an abnormality is detected. For example, if an abnormality is detected, the alert unit can send an alert to caregivers to encourage a quick response. This allows for a quick response by sending alerts when an abnormality is detected. Some or all of the processing described above in the alert unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the alert unit can input collected data into a generating AI, and the generating AI can analyze the data and issue an alert if an anomaly is detected.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of vital data collection based on the estimated user emotions. The data collection unit estimates the user's emotions using, for example, an emotion engine or generative AI. The emotion engine or generative AI estimates the user's emotions based on the collected data. For example, the data collection unit estimates the user's emotions using an emotion engine or generative AI. For example, if the user is feeling stressed, the data collection unit can increase the collection frequency to obtain more detailed data. The data collection unit can also reduce the collection frequency to lessen the burden on the user if they are relaxed. Furthermore, if the user is exercising, the data collection unit can set the collection timing to coincide with the recovery time after exercise. For example, if the user is exercising, the data collection unit can set the collection timing to coincide with the recovery time after exercise and collect data. This allows the data collection unit to collect more appropriate data by adjusting the collection timing according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the data collection unit can input user emotion data into an emotion engine or generative AI, which can then analyze the data and adjust the timing of data collection.

[0081] The data collection unit can analyze the user's past health data and select the optimal collection method. The data collection unit can analyze the user's past health data, for example, using a generative AI. The generative AI analyzes the user's past health data based on the collected data. For example, the data collection unit can analyze the user's past health data using a generative AI. The data collection unit can analyze the user's past heart rate data and concentrate data collection during times when abnormalities are likely to occur. The data collection unit can also adjust the collection frequency based on the user's past blood pressure data. Furthermore, the data collection unit can select a collection method appropriate to the season and time of day, taking into account the user's past body temperature data. For example, the data collection unit can select a collection method appropriate to the season and time of day, taking into account the user's past body temperature data, and collect the data. In this way, the data collection unit can select the optimal collection method by analyzing past health data. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without using a generative AI. For example, the data collection unit can input the user's past health data into a generative AI, which can analyze the data and select the optimal collection method.

[0082] The data collection unit can filter vital data based on the user's current activity level and environment. For example, the data collection unit can analyze the user's current activity level and environment using a generative AI. The generative AI analyzes the user's current activity level and environment based on the collected data. For example, the data collection unit can analyze the user's current activity level and environment using a generative AI. For example, if the user is exercising, the data collection unit can collect only post-exercise data. Also, if the user is sleeping, the data collection unit can prioritize collecting data during sleep. Furthermore, if the user is outside, the data collection unit can consider environmental data from the location when collecting data. For example, if the user is outside, the data collection unit can consider environmental data from the location when collecting data and filter the data. This allows the data collection unit to collect more accurate data by filtering data based on activity level and environment. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without using a generative AI. For example, the data collection unit can input the user's current activity status and environmental data into the generating AI, which can then analyze and filter the data.

[0083] The data collection unit can estimate the user's emotions and determine the priority of vital data to collect based on the estimated user emotions. The data collection unit estimates the user's emotions using, for example, an emotion engine or generative AI. The emotion engine or generative AI estimates the user's emotions based on the collected data. For example, the data collection unit estimates the user's emotions using an emotion engine or generative AI. For example, if the user is stressed, the data collection unit can prioritize the collection of heart rate and blood pressure data. Also, if the user is relaxed, the data collection unit can prioritize the collection of body temperature and respiratory rate data. Furthermore, if the user is exercising, the data collection unit can prioritize the collection of heart rate and oxygen saturation data. For example, if the user is exercising, the data collection unit can prioritize the collection of heart rate and oxygen saturation data. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the data collection unit can input user emotion data into an emotion engine or generative AI, which can then analyze the data and determine the priority of vital data to collect.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting vital data. The data collection unit analyzes the user's geographical location using, for example, a generative AI. The generative AI analyzes the user's geographical location based on the collected data. For example, the data collection unit can prioritize the collection of oxygen saturation data if the user is at high altitude. The data collection unit can also prioritize the collection of heart rate and blood pressure data if the user is in an urban area. Furthermore, the data collection unit can prioritize the collection of body temperature and respiratory rate data if the user is at home. For example, the data collection unit can prioritize the collection of body temperature and respiratory rate data if the user is at home. In this way, the data collection unit can prioritize the collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the data and prioritize collecting highly relevant data.

[0085] The data collection unit can analyze the user's social media activity and collect relevant data when collecting vital data. The data collection unit can analyze the user's social media activity using, for example, a generative AI. The generative AI analyzes the user's social media activity based on the collected data. For example, the data collection unit can analyze the user's social media activity using a generative AI. For example, if the user posts about feeling stressed, the data collection unit can collect heart rate and blood pressure data. Also, if the user posts about relaxing, the data collection unit can collect body temperature and respiratory rate data. Furthermore, if the user posts about exercise, the data collection unit can collect heart rate and oxygen saturation data. For example, if the user posts about exercise, the data collection unit can collect heart rate and oxygen saturation data. In this way, the data collection unit can collect relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can analyze the data and collect relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, an emotion engine or generative AI. The emotion engine or generative AI estimates the user's emotions based on the collected data. For example, the analysis unit estimates the user's emotions using an emotion engine or generative AI. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point and analyze the data. This allows the analysis unit to provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the analysis unit can input user emotion data into an emotion engine or generative AI, which can then analyze the data and adjust the method of representing the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the vital data during the analysis. For example, the analysis unit uses a generation AI to evaluate the importance of the vital data. The generation AI evaluates the importance of the vital data based on the collected data. For example, the analysis unit uses a generation AI to evaluate the importance of the vital data. For example, the analysis unit can perform a detailed analysis if the heart rate is abnormally high. The analysis unit can also perform a detailed analysis if the blood pressure fluctuates rapidly. Furthermore, the analysis unit can perform a detailed analysis if the body temperature is higher than normal. For example, the analysis unit can perform a detailed analysis and analyze the data if the body temperature is higher than normal. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the vital data. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input vital data into a generation AI, which can analyze the data, evaluate its importance, and adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of vital data during analysis. For example, the analysis unit classifies the categories of vital data using a generation AI. The generation AI classifies the categories of vital data based on the collected data. For example, the analysis unit can classify the categories of vital data using a generation AI. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. Furthermore, the analysis unit can apply a body temperature variability analysis algorithm to body temperature data. For example, the analysis unit can apply a body temperature variability analysis algorithm to body temperature data and analyze the data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the category of vital data. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or without using a generation AI. For example, the analysis unit can input vital data into a generation AI, the generation AI can analyze the data and classify the categories, and then apply different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, an emotion engine or generative AI. The emotion engine or generative AI estimates the user's emotions based on the collected data. For example, the analysis unit estimates the user's emotions using an emotion engine or generative AI. For example, if the user is stressed, the analysis unit can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a brief analysis result. For example, if the user is in a hurry, the analysis unit can provide a brief analysis result and analyze the data. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the analysis unit can input user emotion data into an emotion engine or generative AI, which can then analyze the data and adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of vital data collection during analysis. For example, the analysis unit can use a generation AI to evaluate the timing of vital data collection. The generation AI evaluates the timing of vital data collection based on the collected data. For example, the analysis unit can use a generation AI to evaluate the timing of vital data collection. The analysis unit can, for example, prioritize the analysis of recently collected data. The analysis unit can also prioritize the analysis of data in which abnormalities have been detected. Furthermore, the analysis unit can prioritize the analysis of data that shows abnormalities when compared to past data. For example, the analysis unit can prioritize the analysis of data that shows abnormalities when compared to past data. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis based on the timing of vital data collection. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input vital data into a generation AI, which can analyze the data, evaluate the collection timing, and determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relationships between vital data during analysis. For example, the analysis unit can use a generative AI to evaluate the relationships between vital data. The generative AI evaluates the relationships between vital data based on the collected data. For example, the analysis unit can use a generative AI to evaluate the relationships between vital data. For example, the analysis unit can analyze data relating heart rate and blood pressure. The analysis unit can also analyze data relating body temperature and respiratory rate. Furthermore, the analysis unit can analyze data relating oxygen saturation and heart rate. For example, the analysis unit can analyze data relating oxygen saturation and heart rate. This allows the analysis unit to efficiently analyze related data by adjusting the order of analysis based on the relationships between vital data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or without using a generative AI. For example, the analysis unit can input vital data into a generative AI, which can analyze the data, evaluate the relationships, and adjust the order of analysis.

[0092] The service provider can estimate the user's emotions and adjust the way health advice is presented based on the estimated emotions. The service provider estimates the user's emotions, for example, using an emotion engine or generative AI. The emotion engine or generative AI estimates the user's emotions based on collected data. For example, the service provider estimates the user's emotions using an emotion engine or generative AI. For example, if the user is stressed, the service provider can provide simple and easy-to-understand advice. The service provider can also provide detailed advice if the user is relaxed. Furthermore, if the user is in a hurry, the service provider can provide concise and to-the-point advice. For example, if the user is in a hurry, the service provider can provide concise and to-the-point advice and health advice. This allows the service provider to provide more appropriate advice by adjusting the way health advice is presented according to the user's emotions. Some or all of the above processing in the service provider may be performed, for example, using an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the service provider can input user emotional data into an emotion engine or generative AI, which can then analyze the data and adjust how health advice is presented.

[0093] The service provider can adjust the level of detail of health advice based on the user's health condition when providing it. For example, the service provider can use a generative AI to evaluate the user's health condition. The generative AI evaluates the user's health condition based on the collected data. For example, the service provider can use a generative AI to evaluate the user's health condition. For example, if the user's heart rate is abnormally high, the service provider can provide detailed exercise advice. The service provider can also provide detailed dietary advice if the user's blood pressure fluctuates rapidly. Furthermore, if the user's body temperature is higher than normal, the service provider can provide detailed rest advice. For example, if the user's body temperature is higher than normal, the service provider can provide detailed rest advice and health advice. This allows the service provider to provide more appropriate advice by adjusting the level of detail of the advice based on the user's health condition. Some or all of the above processing in the service provider may be performed using a generative AI, or without using a generative AI. For example, the service provider can input the user's health data into a generative AI, which can analyze the data to evaluate the health condition and adjust the level of detail of the advice.

[0094] The service provider can apply different advice algorithms depending on the user's lifestyle when providing health advice. For example, the service provider can use a generative AI to evaluate the user's lifestyle. The generative AI evaluates the user's lifestyle based on the collected data. For example, the service provider can use a generative AI to evaluate the user's lifestyle. For example, if the user has an exercise habit, the service provider can apply an exercise advice algorithm. The service provider can also apply a diet advice algorithm if the user is mindful of their diet. Furthermore, if the user is prone to stress, the service provider can apply a stress management advice algorithm. For example, if the service provider is prone to stress, the service provider can apply a stress management advice algorithm and provide health advice. In this way, the service provider can provide more appropriate advice by applying different advice algorithms depending on the user's lifestyle. Some or all of the above processing in the service provider may be performed using a generative AI, or without using a generative AI. For example, the service provider can input the user's lifestyle data into a generative AI, which can analyze the data to evaluate the lifestyle and apply different advice algorithms.

[0095] The service provider can estimate the user's emotions and adjust the length of health advice based on the estimated emotions. The service provider estimates the user's emotions using, for example, an emotion engine or generative AI. The emotion engine or generative AI estimates the user's emotions based on collected data. For example, the service provider estimates the user's emotions using an emotion engine or generative AI. For example, if the user is stressed, the service provider can provide short, concise advice. The service provider can also provide detailed advice if the user is relaxed. Furthermore, if the user is in a hurry, the service provider can provide brief advice. For example, if the user is in a hurry, the service provider can provide brief advice and health advice. This allows the service provider to provide more appropriate advice by adjusting the length of health advice according to the user's emotions. Some or all of the above processing in the service provider may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the service provider can input user emotional data into an emotion engine or generative AI, which can then analyze the data and adjust the length of the health advice.

[0096] The service provider can determine the priority of health advice based on when the user's health data is collected. For example, the service provider can use a generative AI to evaluate when the user's health data is collected. The generative AI evaluates when the user's health data is collected based on the collected data. For example, the service provider can use a generative AI to evaluate when the user's health data is collected. The service provider can, for example, provide advice based on recently collected data. The service provider can also provide advice based on data in which anomalies have been detected. Furthermore, the service provider can provide advice based on data that shows anomalies when compared to past data. For example, the service provider can provide health advice based on data that shows anomalies when compared to past data. This allows the service provider to prioritize important advice by determining the priority of advice based on when the user's health data is collected. Some or all of the above processing in the service provider may be performed using a generative AI, for example, or without a generative AI. For example, the service provider can input the user's health data into a generative AI, which can analyze the data, evaluate the collection timing, and determine the priority of advice.

[0097] The service provider can adjust the order of advice based on the user's relevant data when providing health advice. The service provider can, for example, use a generative AI to evaluate the user's relevant data. The generative AI evaluates the user's relevant data based on the collected data. For example, the service provider can use a generative AI to evaluate the user's relevant data. The service provider can, for example, provide advice by associating heart rate and blood pressure data. The service provider can also provide advice by associating body temperature and respiratory rate data. Furthermore, the service provider can provide advice by associating oxygen saturation and heart rate data. For example, the service provider can provide health advice by associating oxygen saturation and heart rate data. This allows the service provider to provide more appropriate advice by adjusting the order of advice based on the user's relevant data. Some or all of the above processing in the service provider may be performed using a generative AI, for example, or without a generative AI. For example, the service provider can input the user's relevant data into a generative AI, which can analyze the data, evaluate its relevance, and adjust the order of advice.

[0098] The reminder unit can estimate the user's emotions and adjust the way the reminder is presented based on the estimated emotions. For example, the reminder unit estimates the user's emotions using an emotion engine or generative AI. The emotion engine or generative AI estimates the user's emotions based on collected data. For example, the reminder unit estimates the user's emotions using an emotion engine or generative AI. For example, if the user is stressed, the reminder unit can provide a simple and highly visible reminder. Also, if the user is relaxed, the reminder unit can provide a detailed reminder. Furthermore, if the user is in a hurry, the reminder unit can provide a concise and to-the-point reminder. For example, if the user is in a hurry, the reminder unit can provide a concise and to-the-point reminder. This allows the reminder unit to provide more appropriate reminders by adjusting the way the reminder is presented according to the user's emotions. Some or all of the above processing in the reminder unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the reminder unit can input user emotion data into an emotion engine or generative AI, which can then analyze the data and adjust how the reminder is expressed.

[0099] The reminder unit can adjust the level of detail of reminders based on the user's medication history. The reminder unit evaluates the user's medication history, for example, using a generative AI. The generative AI evaluates the user's medication history based on the collected data. For example, the reminder unit evaluates the user's medication history using a generative AI. The reminder unit can provide a detailed reminder if the user has forgotten to take their medication in the past. The reminder unit can also provide a concise reminder if the user does not forget to take their medication. Furthermore, the reminder unit can provide detailed reminders during times when the user is likely to forget to take their medication. For example, the reminder unit can provide a detailed reminder and send a reminder during times when the user is likely to forget to take their medication. This allows the reminder unit to provide more appropriate reminders by adjusting the level of detail of reminders based on the user's medication history. Some or all of the above processing in the reminder unit may be performed using a generative AI, for example, or without using a generative AI. For example, the reminder unit inputs the user's medication history data into a generating AI, which then analyzes the data to adjust the level of detail in the reminders.

[0100] The reminder unit can apply different reminder algorithms depending on the user's lifestyle when sending reminders. The reminder unit can evaluate the user's lifestyle using, for example, a generative AI. The generative AI evaluates the user's lifestyle based on the collected data. For example, the reminder unit can evaluate the user's lifestyle using the generative AI. The reminder unit can provide regular reminders if the user leads a regular life. It can also provide flexible reminders if the user leads an irregular life. Furthermore, if the user is traveling, the reminder unit can provide reminders that are time-sensitive to the user's travel destination. For example, if the user is traveling, the reminder unit can provide and send reminders that are time-sensitive to the user's travel destination. This allows the reminder unit to provide more appropriate reminders by applying different reminder algorithms depending on the user's lifestyle. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reminder unit can input the user's lifestyle data into a generative AI, which can analyze the data to evaluate the lifestyle and apply different reminder algorithms.

[0101] The reminder unit can estimate the user's emotions and adjust the length of the reminder based on the estimated emotions. The reminder unit estimates the user's emotions, for example, using an emotion engine or generative AI. The emotion engine or generative AI estimates the user's emotions based on collected data. For example, the reminder unit estimates the user's emotions using an emotion engine or generative AI. For example, if the user is stressed, the reminder unit can provide a short, to-the-point reminder. Also, if the user is relaxed, the reminder unit can provide a detailed reminder. Furthermore, if the user is in a hurry, the reminder unit can provide a concise reminder. For example, if the user is in a hurry, the reminder unit can provide a concise reminder and send a reminder. In this way, the reminder unit can provide more appropriate reminders by adjusting the length of the reminder according to the user's emotions. Some or all of the above processing in the reminder unit may be performed, for example, using an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the reminder unit can input user emotion data into an emotion engine or generative AI, which can then analyze the data and adjust the length of the reminder.

[0102] The reminder unit can determine the priority of reminders based on when the user's medication data is collected. The reminder unit can, for example, use a generative AI to evaluate when the user's medication data is collected. The generative AI evaluates when the user's medication data is collected based on the collected data. For example, the reminder unit can use a generative AI to evaluate when the user's medication data is collected. The reminder unit can, for example, provide reminders based on recently collected medication data. The reminder unit can also provide reminders based on medication data in which abnormalities have been detected. Furthermore, the reminder unit can provide reminders based on medication data that shows abnormalities when compared to past data. For example, the reminder unit can provide and issue reminders based on medication data that shows abnormalities when compared to past data. This allows the reminder unit to prioritize important reminders by determining the priority of reminders based on when the user's medication data is collected. Some or all of the above processing in the reminder unit may be performed using a generative AI, for example, or without using a generative AI. For example, the reminder unit can input the user's medication data into a generating AI, which can then analyze the data, evaluate the timing of data collection, and determine the priority of reminders.

[0103] The reminder unit can adjust the order of reminders based on the user's relevant data when sending reminders. The reminder unit can evaluate the user's relevant data using, for example, a generative AI. The generative AI evaluates the user's relevant data based on the collected data. For example, the reminder unit can evaluate the user's relevant data using the generative AI. The reminder unit can provide reminders by associating heart rate and blood pressure data. It can also provide reminders by associating body temperature and respiratory rate data. Furthermore, the reminder unit can provide reminders by associating oxygen saturation and heart rate data. For example, the reminder unit can provide and send reminders by associating oxygen saturation and heart rate data. This allows the reminder unit to provide more appropriate reminders by adjusting the order of reminders based on the user's relevant data. Some or all of the above processing in the reminder unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reminder unit can input the user's relevant data into the generative AI, which can analyze the data, evaluate the relevance, and adjust the order of reminders.

[0104] The alert unit can estimate the user's emotions and adjust the way the alert is presented based on the estimated emotions. The alert unit estimates the user's emotions, for example, using an emotion engine or generative AI. The emotion engine or generative AI estimates the user's emotions based on collected data. For example, the alert unit estimates the user's emotions using an emotion engine or generative AI. For example, if the user is stressed, the alert unit can provide a simple and highly visible alert. The alert unit can also provide a detailed alert if the user is relaxed. Furthermore, if the user is in a hurry, the alert unit can provide a concise and to-the-point alert. For example, if the user is in a hurry, the alert unit can provide a concise and to-the-point alert and issue an alert. This allows the alert unit to provide more appropriate alerts by adjusting the way the alert is presented according to the user's emotions. Some or all of the above processing in the alert unit may be performed, for example, using an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the alert unit can input user emotion data into an emotion engine or generative AI, which can then analyze the data and adjust how the alert is expressed.

[0105] The alert unit can adjust the level of detail of an alert based on the user's health data when issuing an alert. The alert unit evaluates the user's health data using, for example, a generative AI. The generative AI evaluates the user's health data based on the collected data. For example, the alert unit evaluates the user's health data using the generative AI. The alert unit can provide a detailed alert if, for example, the user's heart rate is abnormally high. The alert unit can also provide a detailed alert if the user's blood pressure fluctuates rapidly. Furthermore, the alert unit can provide a detailed alert if the user's body temperature is higher than normal. For example, the alert unit can provide a detailed alert and issue an alert if the user's body temperature is higher than normal. This allows the alert unit to provide more appropriate alerts by adjusting the level of detail of the alert based on the user's health data. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the alert unit can input the user's health data into the generative AI, which can analyze the data and adjust the level of detail of the alert.

[0106] The alert unit can apply different alert algorithms depending on the user's lifestyle when issuing an alert. For example, the alert unit can use a generative AI to evaluate the user's lifestyle. The generative AI evaluates the user's lifestyle based on the collected data. For example, the alert unit can use a generative AI to evaluate the user's lifestyle. For example, if the user lives a regular life, the alert unit can provide regular alerts. The alert unit can also provide flexible alerts if the user lives an irregular life. Furthermore, if the user is traveling, the alert unit can provide alerts tailored to the time zone of the travel destination. For example, if the user is traveling, the alert unit can provide and issue alerts tailored to the time zone of the travel destination. This allows the alert unit to provide more appropriate alerts by applying different alert algorithms depending on the user's lifestyle. Some or all of the above processing in the alert unit may be performed using a generative AI, or without using a generative AI. For example, the alert unit can input user lifestyle data into a generative AI, which can analyze the data to evaluate the lifestyle and apply different alert algorithms.

[0107] The alert unit can estimate the user's emotions and adjust the length of the alert based on the estimated emotions. The alert unit estimates the user's emotions, for example, using an emotion engine or generative AI. The emotion engine or generative AI estimates the user's emotions based on collected data. For example, the alert unit estimates the user's emotions using an emotion engine or generative AI. For example, if the user is stressed, the alert unit can provide a short, to-the-point alert. The alert unit can also provide a detailed alert if the user is relaxed. Furthermore, if the user is in a hurry, the alert unit can provide a concise alert. For example, if the user is in a hurry, the alert unit can provide a concise alert and issue an alert. This allows the alert unit to provide more appropriate alerts by adjusting the length of the alert according to the user's emotions. Some or all of the above processing in the alert unit may be performed, for example, using an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the alert unit can input user emotion data into an emotion engine or generative AI, which can then analyze the data and adjust the length of the alert.

[0108] The alert unit can determine the priority of alerts based on when the user's health data is collected when an alert is issued. For example, the alert unit may use a generative AI to evaluate when the user's health data is collected. The generative AI evaluates when the user's health data is collected based on the collected data. For example, the alert unit may use a generative AI to evaluate when the user's health data is collected. The alert unit may provide alerts based on recently collected data. The alert unit may also provide alerts based on data in which anomalies have been detected. Furthermore, the alert unit may provide alerts based on data that shows anomalies when compared to past data. For example, the alert unit may provide and issue alerts based on data that shows anomalies when compared to past data. This allows the alert unit to prioritize important alerts by determining the priority of alerts based on when the user's health data is collected. Some or all of the above processing in the alert unit may be performed using a generative AI, or not using a generative AI. For example, the alert unit may input the user's health data into a generative AI, which may analyze the data to evaluate the collection timing and determine the priority of alerts.

[0109] The alert unit can adjust the order of alerts based on the user's relevant data when an alert is issued. The alert unit evaluates the user's relevant data using, for example, a generative AI. The generative AI evaluates the user's relevant data based on the collected data. For example, the alert unit evaluates the user's relevant data using the generative AI. The alert unit can provide an alert by associating heart rate and blood pressure data. The alert unit can also provide an alert by associating body temperature and respiratory rate data. Furthermore, the alert unit can provide an alert by associating oxygen saturation and heart rate data. For example, the alert unit can provide an alert by associating oxygen saturation and heart rate data and issue an alert. This allows the alert unit to provide more appropriate alerts by adjusting the order of alerts based on the user's relevant data. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the alert unit can input the user's relevant data into the generative AI, which can analyze the data, evaluate the relevance, and adjust the order of alerts.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can estimate the user's emotions and adjust the timing of vital data collection based on those emotions. For example, if the user is stressed, the data collection timing can be increased to obtain more detailed data. Conversely, if the user is relaxed, the data collection timing can be reduced to lessen the burden. Furthermore, if the user is exercising, the data collection timing can be set to coincide with the recovery time after exercise. In this way, the data collection unit can adjust the data collection timing according to the user's emotions, enabling more appropriate data collection.

[0112] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, it can analyze the user's past heart rate data and concentrate data collection during times when abnormalities are likely to occur. It can also adjust the collection frequency based on the user's past blood pressure data. Furthermore, it can select a collection method appropriate for the season and time of day by referring to the user's past body temperature data. In this way, the data collection unit can select the optimal collection method by analyzing past health data.

[0113] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results that get straight to the point. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions.

[0114] The analysis unit can adjust the level of detail of the analysis based on the importance of the vital data during the analysis. For example, if the heart rate is abnormally high, a detailed analysis can be performed. Similarly, if blood pressure fluctuates rapidly, a detailed analysis can be performed. Furthermore, if body temperature is higher than normal, a detailed analysis can be performed. In this way, the analysis unit can perform detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the vital data.

[0115] The service provider can estimate the user's emotions and adjust the way health advice is presented based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand advice. If the user is relaxed, it can provide more detailed advice. Furthermore, if the user is in a hurry, it can provide concise advice that gets straight to the point. In this way, the service provider can provide more appropriate advice by adjusting the way health advice is presented according to the user's emotions.

[0116] The service provider can adjust the level of detail in health advice based on the user's health condition. For example, if a user's heart rate is abnormally high, it can provide detailed exercise advice. Similarly, if a user's blood pressure fluctuates rapidly, it can provide detailed dietary advice. Furthermore, if a user's body temperature is higher than normal, it can provide detailed rest advice. This allows the service provider to offer more appropriate advice by adjusting the level of detail based on the user's health condition.

[0117] The reminder function can estimate the user's emotions and adjust the way reminders are presented based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible reminder. If the user is relaxed, it can provide a more detailed reminder. Furthermore, if the user is in a hurry, it can provide a concise reminder that gets straight to the point. In this way, the reminder function can provide more appropriate reminders by adjusting the way reminders are presented according to the user's emotions.

[0118] The reminder function can adjust the level of detail in reminders based on the user's medication history. For example, if a user has forgotten to take their medication in the past, it can provide a detailed reminder. Conversely, if the user does not forget to take their medication, it can provide a concise reminder. Furthermore, it can provide detailed reminders during times when the user is likely to forget to take their medication. In this way, the reminder function can provide more appropriate reminders by adjusting the level of detail based on the user's medication history.

[0119] The alert unit can estimate the user's emotions and adjust the way the alert is presented based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible alert. If the user is relaxed, it can provide a more detailed alert. Furthermore, if the user is in a hurry, it can provide a concise alert that gets straight to the point. In this way, the alert unit can provide more appropriate alerts by adjusting the way the alert is presented according to the user's emotions.

[0120] The alert unit can adjust the level of detail of an alert based on the user's health data when an alert is issued. For example, if the user's heart rate is abnormally high, a detailed alert can be provided. Similarly, if the user's blood pressure fluctuates rapidly, a detailed alert can be provided. Furthermore, if the user's body temperature is higher than normal, a detailed alert can be provided. In this way, the alert unit can provide more appropriate alerts by adjusting the level of detail of the alert based on the user's health data.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects vital data. This vital data includes heart rate, blood pressure, and body temperature. The data collection unit measures heart rate using a wearable device, blood pressure using a blood pressure monitor, and body temperature using a thermometer. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses a generation AI to analyze the collected data and detect anomalies and trends. For example, it can detect anomalies such as a heart rate that is higher than normal or a sudden change in blood pressure. Step 3: The service provider provides health advice based on the analysis results obtained by the analysis unit. The service provider uses a generating AI to provide appropriate exercise and dietary advice. Step 4: The reminder unit provides medication reminders. The reminder unit uses a generation AI to send reminders when it's time to take the medication. Step 5: The alert unit issues an alert when an abnormality is detected. The alert unit uses AI generation to send an alert to the caregiver if the heart rate is abnormally high or if blood pressure fluctuates rapidly.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, reminder unit, and alert unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects vital data such as heart rate, blood pressure, and body temperature using the wearable device of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to detect abnormal values ​​and trends. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides health advice based on the analysis results. The reminder unit is implemented in the specific processing unit 46A of the smart device 14, for example, and provides a reminder when it is time to take medication. The alert unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and sends an alert to the caregiver when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 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.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the 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.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, reminder unit, and alert unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects vital data such as heart rate, blood pressure, and body temperature using the wearable device of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to detect abnormal values ​​and trends. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides health advice based on the analysis results. The reminder unit is implemented in the specific processing unit 46A of the smart glasses 214, for example, and provides a reminder when it is time to take medication. The alert unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and sends an alert to the caregiver when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, reminder unit, and alert unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects vital data such as heart rate, blood pressure, and body temperature using the wearable device of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to detect abnormal values ​​and trends. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides health advice based on the analysis results. The reminder unit is implemented in the specific processing unit 46A of the headset terminal 314, for example, and provides a reminder when it is time to take medication. The alert unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and sends an alert to the caregiver when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, reminder unit, and alert unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects vital data such as heart rate, blood pressure, and body temperature using the wearable device of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to detect abnormal values ​​and trends. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides health advice based on the analysis results. The reminder unit is implemented in the control unit 46A of the robot 414, for example, and provides a reminder when it is time to take medication. The alert unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and sends an alert to the caregiver when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection unit that collects vital data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provisioning unit that provides health advice based on the analysis results obtained by the aforementioned analysis unit, The reminder department provides medication reminders, It includes an alert unit that issues an alert when an abnormality is detected. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect vital data such as heart rate, blood pressure, and body temperature. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Detect outliers and trends based on collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide advice on appropriate exercise and diet. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reminder unit, Set up reminders when it's time to take your medication. The system described in Appendix 1, characterized by the features described herein. (Note 6) The alert unit is, The system sends an alert to the caregiver if the heart rate is abnormally high or if blood pressure fluctuates rapidly. 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 vital data collection based on those 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 vital data, filtering is performed based on the user's current activity status and environment. 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 determines the priority of vital data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting vital data, the system prioritizes the collection of highly relevant data, taking into account 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 vital data, 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, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the vital data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of vital 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 the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the vital data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of vital data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way health advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing health advice, adjust the level of detail of the advice based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing health advice, different advice algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of health advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing health advice, the priority of the advice is determined based on when the user's health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing health advice, the order of advice is adjusted based on the user's relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reminder unit, It estimates the user's emotions and adjusts the way reminders are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reminder unit, When sending a reminder, adjust the level of detail based on the user's medication history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reminder unit, When sending reminders, different reminder algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reminder unit, It estimates the user's emotions and adjusts the length of the reminder based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reminder unit, When sending reminders, the priority of reminders is determined based on when the user's medication data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reminder unit, When sending reminders, adjust the order of reminders based on the user's relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The alert unit is, It estimates the user's emotions and adjusts how alerts are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The alert unit is, When an alert is issued, adjust the level of detail of the alert based on the user's health data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The alert unit is, When an alert is issued, different alert algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The alert unit is, It estimates the user's sentiment and adjusts the length of the alert based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The alert unit is, When an alert is issued, the priority of the alert is determined based on when the user's health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 36) The alert unit is, When an alert is issued, the order of the alerts is adjusted based on the user's relevant data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects vital data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provisioning unit that provides health advice based on the analysis results obtained by the aforementioned analysis unit, The reminder department provides medication reminders, It includes an alert unit that issues an alert when an abnormality is detected. A system characterized by the following features.

2. The aforementioned collection unit is Collect vital data such as heart rate, blood pressure, and body temperature. The system according to feature 1.

3. The aforementioned analysis unit, Detect outliers and trends based on collected data. The system according to feature 1.

4. The aforementioned supply unit is, We provide advice on appropriate exercise and diet. The system according to feature 1.

5. The aforementioned reminder unit, Set up reminders when it's time to take your medication. The system according to feature 1.

6. The alert unit is, The system sends an alert to the caregiver if the heart rate is abnormally high or if blood pressure fluctuates rapidly. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of vital data collection based on those 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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