system

Abstract

The system according to this embodiment aims to analyze the user's health data and provide health management and preventive measures based on the individual's health condition. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects the user's health data. The analysis unit analyzes the health data collected by the collection unit. The provision unit provides health management and preventive measures based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, continuous collection of users' health data and provision of customized health management and preventive measures based on individual health conditions have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze users' health data and provide health management and preventive measures based on individual health conditions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects the user's health data. The analysis unit analyzes the health data collected by the collection unit. The provision unit provides health management and preventive measures based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's health data and provide health management and preventive measures based on the individual's health condition. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​healthcare agent system according to an embodiment of the present invention is a system that continuously collects a user's health data and provides customized health management and preventive measures based on the individual's health condition. This system analyzes the user's lifestyle, genetic data, and the results of regular health checks to make specific health improvement suggestions. The AI ​​agent collects real-time health data from IoT devices and analyzes the health data using machine learning. This allows the system to propose intervention plans tailored to each user's health condition. For example, it can be expected to improve the rate of health improvement, increase the rate of early disease detection, and optimize lifestyle habits. It is also characterized by a combination of cloud computing and AI, the use of wearable devices, and data management that emphasizes the protection of individual privacy. Target users include health-conscious individuals, patients with chronic diseases, elderly people who want to manage their health efficiently, medical institutions, health insurance companies, and corporate health management departments. The AI ​​analyzes the user's health data and generates optimal health advice in real time. For example, the AI ​​healthcare agent system uses IoT devices to collect the user's health data. IoT devices can collect health data such as heart rate, blood pressure, body temperature, and exercise level in real time. This allows the system to constantly monitor the user's health condition and provide appropriate health management and preventive measures. Furthermore, the AI ​​healthcare agent system analyzes health data using machine learning. Machine learning uses algorithms such as deep learning and support vector machines to analyze patterns in health data and predict the user's health status. For example, based on the user's health data, the AI ​​healthcare agent system provides specific health improvement suggestions, such as dietary guidance, exercise plans, and medical advice. This allows users to receive optimal health management and preventative measures tailored to their individual health condition. The AI ​​healthcare agent system also securely manages health data using cloud computing. Cloud computing protects user privacy by enhancing security protocols such as data encryption and access control.Furthermore, the AI ​​healthcare agent system utilizes wearable devices to collect user health data. These wearable devices enhance user comfort through technologies such as improved sensor accuracy, extended battery life, and improved fit. This allows users to collect health data over extended periods. The AI ​​healthcare agent system can then collect and analyze this data to provide health management and preventative measures.

[0029] The AI ​​healthcare agent system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects user health data. The collection unit can, for example, collect health data such as heart rate, blood pressure, body temperature, and exercise level in real time using IoT devices. The collection unit can also, for example, collect user health data using wearable devices. The collection unit can also, for example, collect user health data using smartphone applications. The analysis unit analyzes the health data collected by the collection unit. The analysis unit analyzes the health data using, for example, machine learning. Machine learning analyzes patterns in health data using algorithms such as deep learning and support vector machines. The analysis unit can, for example, analyze trends in health data and predict the user's health status. The analysis unit can, for example, detect anomalies in health data and issue warnings to the user. The provision unit provides health management and preventive measures based on the analysis results obtained by the analysis unit. The provision unit proposes intervention plans tailored to each user's health status. The provision unit provides specific health improvement suggestions, such as dietary guidance, exercise plans, and medical advice. The provisioning unit can be expected to have effects such as improving the rate of improvement in health status, improving the rate of early detection of diseases, and optimizing lifestyle habits. As a result, the AI ​​healthcare agent system according to the embodiment can collect and analyze the user's health data and provide health management and preventive measures. Some or all of the above-described processes in the collection unit, analysis unit, and provisioning unit may be performed using AI, for example, or without using AI. For example, the collection unit can input health data collected from IoT devices into the AI, and the AI ​​can analyze the data and output the collection results. The analysis unit can input health data obtained from the collection unit into the AI, and the AI ​​can analyze the data and output the analysis results. The provisioning unit can input the analysis results obtained from the analysis unit into the AI, and the AI ​​can generate and provide health management and preventive measures.

[0030] The data collection unit collects user health data. For example, the data collection unit can collect health data such as heart rate, blood pressure, body temperature, and exercise level in real time using IoT devices. Specifically, IoT devices include heart rate sensors for measuring heart rate, blood pressure monitors for measuring blood pressure, thermometers for measuring body temperature, and accelerometers and gyroscopes for measuring exercise level. These devices are worn on the user's body and continuously collect data during daily life. The collected data is transmitted to a smartphone or cloud server via wireless communication such as Bluetooth® or Wi-Fi. The data collection unit can also collect user health data using wearable devices. Wearable devices include smartwatches and fitness trackers, which are worn on the user's wrist or arm and collect data such as heart rate, steps, calories burned, and sleep patterns. This data is transmitted to the user's smartphone through a dedicated application and stored on a cloud server. The data collection unit can also collect user health data using smartphone apps. Smartphone apps integrate data manually entered by the user with data automatically collected from sensors built into the smartphone. For example, users can input daily meal details, weight, blood pressure, and other information into the app. They can also use their smartphone's GPS function to record their exercise levels and distance traveled. This allows the data collection unit to comprehensively collect and manage user health data in real time using a variety of devices and methods. Furthermore, the data collection unit can centrally manage this data and integrate it with other systems and departments as needed. For instance, collected data can be stored on a cloud server, making it accessible to the analysis and provisioning departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes health data collected by the data collection department. For example, the analysis department uses machine learning to analyze health data. Machine learning uses algorithms such as deep learning and support vector machines to analyze patterns in health data. Specifically, deep learning can be used to analyze fluctuation patterns in heart rate and blood pressure and detect abnormal patterns. For example, if a user's heart rate remains higher than normal, it could be a sign of stress, lack of exercise, or illness, and the analysis department can issue a warning to the user. Furthermore, support vector machines can be used to classify user health data and predict trends in health status. For example, based on past data, the analysis department can predict whether a user's health is improving or worsening and suggest appropriate countermeasures. In addition, the analysis department implements algorithms to detect anomalies, automatically detecting abnormal values ​​from the collected data and issuing warnings to users. For example, it can detect anomalies that could have a significant impact on health, such as a sudden increase in blood pressure or an abnormally high body temperature, and notify the user. The analysis department can also utilize past data and statistical information to assess long-term health risks. For example, based on historical data, it is possible to predict fluctuations in health risks during specific seasons or time periods and formulate future countermeasures. This allows the analysis department to quickly and accurately analyze collected data and understand users' health status in real time. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis department can not only understand the situation in real time but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The service provider provides health management and preventive measures based on the analysis results obtained by the analysis provider. For example, the service provider proposes intervention plans tailored to each user's health condition. Specifically, it creates and provides individualized health management plans based on the user's health data. For example, it can propose stress management and exercise plans based on heart rate and blood pressure data. It also provides specific health improvement suggestions such as dietary guidance, exercise plans, and medical advice. For example, it can analyze the user's diet and propose improvements to nutritional balance and adjustments to calorie intake. Furthermore, it can create exercise plans and provide specific advice to improve the user's exercise habits. For example, it can suggest how many times a week and how much exercise should be done to support the improvement of the user's health condition. It can also provide medical advice and encourage users to visit appropriate medical institutions according to their health condition. For example, if abnormal values ​​are detected, it can send a notification to the user recommending a visit to a medical institution. As a result, the service provider can expect effects such as an increase in the rate of improvement in users' health conditions, an increase in the rate of early detection of diseases, and optimization of lifestyle habits. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the health management and preventive measures it provides. For example, the service provider can analyze how users responded to the provided health management plan and incorporate that feedback into future suggestions. Furthermore, the service provider can reliably transmit information using multiple communication methods. For instance, important information can be delivered not only via smartphone notifications but also through voice calls, SMS, and email. This allows the service provider to quickly and reliably provide users with health management and preventative measures, supporting improvements in their health.

[0033] The data collection unit can collect real-time health data from IoT devices. For example, the data collection unit can collect health data such as heart rate, blood pressure, body temperature, and exercise level in real time using IoT devices. The data collection unit can also collect health data such as sleep data and stress levels in real time using IoT devices. The data collection unit can also collect health data such as diet data and water intake in real time using IoT devices. This allows for the collection of real-time health data from IoT devices. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the health data collected from IoT devices into a generating AI, which can then analyze the data and output the collection results.

[0034] The analysis unit can analyze health data using machine learning. For example, the analysis unit can analyze health data using deep learning. The analysis unit can also analyze health data using support vector machines. The analysis unit can also analyze health data using random forests. This improves the accuracy of health data analysis by using machine learning. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, which can then analyze the data and output analysis results.

[0035] The service provider can propose intervention plans tailored to each user's health condition. For example, based on the user's health data, the service provider can provide specific health improvement suggestions such as dietary guidance, exercise plans, and medical advice. For example, the service provider can also propose intervention plans such as stress management, sleep improvement, and weight management, depending on the user's health condition. For example, based on the user's health data, the service provider can also propose early detection and prevention measures for diseases. This allows the service provider to propose intervention plans tailored to each user's health condition. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input health data into a generating AI, and the generating AI can generate and provide an intervention plan.

[0036] The data collection unit can analyze the user's past health data history and select the optimal collection method. For example, the data collection unit can select the most reliable sensor from the user's past data. The data collection unit can also analyze the user's past data collection patterns and determine the optimal collection frequency. For example, the data collection unit can select a method from the user's past data that improves accuracy by collecting data at a specific time period. This allows the optimal collection method to be selected by analyzing the user's past health data history. 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 the user's past health data into a generating AI, which can then select the optimal collection method.

[0037] The data collection unit can filter health data based on the user's current lifestyle and activity level. For example, if the user is at work, the data collection unit may prioritize collecting stress levels. If the user is exercising, the data collection unit may prioritize collecting heart rate and calorie consumption. If the user is resting, the data collection unit may prioritize collecting sleep quality and relaxation levels. By filtering health data based on the user's lifestyle and activity level, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and activity level into a generating AI, which can then perform the filtering.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit may prioritize the collection of oxygen saturation data. For example, if the user is in an urban area, the data collection unit may prioritize the collection of air quality data. For example, if the user is outdoors, the data collection unit may prioritize the collection of ultraviolet level data. This allows for the priority collection of highly relevant health data by considering the user's geographical location. 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 the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0039] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user is experiencing stress on social media, the data collection unit may prioritize collecting stress levels. For example, if a user posts about exercise on social media, the data collection unit may prioritize collecting exercise data. For example, if a user posts about food on social media, the data collection unit may prioritize collecting dietary data. In this way, relevant health data can be collected by analyzing a user's social media activity. 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 the user's social media activity data into a generating AI, which can then collect relevant health data.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. For example, the analysis unit can perform a concise analysis on general health data. For example, the analysis unit can perform a rapid analysis on urgent health data. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input health data into a generating AI, which can then adjust the level of detail of the analysis based on importance.

[0041] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit can apply an exercise intensity analysis algorithm to exercise data. By applying different analysis algorithms depending on the category of health data, appropriate analysis according to the characteristics of the data becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, and the generating AI can apply different analysis algorithms depending on the category.

[0042] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also prioritize the analysis of regularly collected data. The analysis unit may also prioritize the analysis of data related to a specific event. This allows for the prioritization of the most recent data by determining the priority of analysis based on when the health data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input health data into a generating AI, which can then determine the priority of analysis based on when the data was collected.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the health data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, which can then adjust the order of analysis based on relevance.

[0044] The information provider can adjust the level of detail provided based on the importance of the health management and preventive measures at the time of provision. For example, the provider can provide detailed information for important health management and preventive measures. For example, the provider can provide concise information for general health management and preventive measures. For example, the provider can provide information quickly for urgent health management and preventive measures. This allows for detailed provision of important information by adjusting the level of detail based on the importance of the health management and preventive measures. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input health management and preventive measures information into a generating AI, which can then adjust the level of detail based on importance.

[0045] The information delivery unit can apply different delivery algorithms depending on the category of health management and preventive measures at the time of delivery. For example, the information delivery unit can apply a nutritional balance analysis algorithm to diet management. For example, the information delivery unit can also apply an exercise intensity analysis algorithm to exercise management. For example, the information delivery unit can also apply a stress level analysis algorithm to stress management. By applying different delivery algorithms depending on the category of health management and preventive measures, appropriate information can be provided. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input health management and preventive measures information into a generating AI, and the generating AI can apply different delivery algorithms depending on the category.

[0046] The service provider can determine the priority of information delivery based on the timing of health management and preventive measures collection. For example, the service provider may prioritize information based on recently collected data. The service provider may also prioritize information based on regularly collected data. The service provider may also prioritize information based on data related to a specific event. This ensures that the most up-to-date information is delivered by prioritizing delivery based on the timing of health management and preventive measures collection. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input health management and preventive measures information into a generating AI, which can then determine the priority of delivery based on the collection timing.

[0047] The information delivery unit can adjust the order of delivery based on the relevance between health management and preventive measures. For example, the delivery unit may prioritize the delivery of highly relevant information. For example, the delivery unit may postpone the delivery of less relevant information. The delivery unit can also dynamically adjust the order of delivery based on the relevance of the information. This allows for the priority delivery of highly relevant information by adjusting the order of delivery based on the relevance between health management and preventive measures. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input health management and preventive measure information into a generating AI, which can then adjust the order of delivery based on relevance.

[0048] Combining cloud computing and AI allows for the dynamic allocation of cloud computing resources, thereby optimizing AI processing capabilities. By dynamically allocating cloud computing resources, AI processing capabilities can be optimized. Cloud computing resources can be dynamically allocated, for example, according to the amount of data a user provides. Cloud computing resources can also be dynamically adjusted, for example, according to the processing load of the AI. Cloud computing resources can also be dynamically allocated, for example, to resources required for real-time data processing. This allows for the optimization of AI processing capabilities by dynamically allocating cloud computing resources. Dynamic allocation of cloud computing resources may be performed using AI, or it may be performed without AI. For example, the usage status of cloud computing resources can be input into a generating AI, which can then dynamically allocate resources.

[0049] Combining cloud computing and AI allows for the decentralization of data processing on the cloud, thereby improving processing speed. Distributing data processing on the cloud can improve processing speed. For example, data processing on the cloud can be distributed across multiple cloud servers. For example, data processing on the cloud can evenly distribute the data processing load. For example, distributed processing can be used to eliminate data processing bottlenecks. Thus, by distributing data processing on the cloud, processing speed can be improved. Distributing data processing on the cloud can be done using AI, or without AI. For example, the data processing load on the cloud can be input into a generating AI, which can then perform the data processing distribution.

[0050] Combining cloud computing with AI can enhance the security protocols of cloud computing and ensure data security. By strengthening the security protocols of cloud computing, data security can be ensured. For example, the security protocols of cloud computing can enhance data encryption. For example, the security protocols of cloud computing can be regularly updated. For example, the security protocols of cloud computing can enhance data access monitoring and logging. Thus, by strengthening the security protocols of cloud computing, data security can be ensured. The strengthening of cloud computing security protocols may be done using AI, or it may be done without AI. For example, the security status of cloud computing can be input into a generating AI, and the generating AI can strengthen the security protocols.

[0051] The combination of cloud computing and AI allows for the sharing of cloud computing resources with other AI applications, enabling efficient resource utilization. By sharing cloud computing resources with other AI applications, efficient resource utilization can be achieved. For example, multiple AI applications can share the same cloud resources. Cloud computing resources can also be dynamically allocated among AI applications based on resource usage. Cloud computing resources can also be efficiently shared among AI applications to minimize resource waste. This allows for efficient resource utilization by sharing cloud computing resources with other AI applications. Resource sharing of cloud computing resources can be performed using AI, or without AI. For example, the usage of cloud computing resources can be input into a generating AI, which can then share the resources.

[0052] The combination of cloud computing and AI allows for the integration of data processing on the cloud with other cloud services, promoting data interoperability. This integration facilitates data sharing and mutual use. For example, data processing on the cloud can share and mutually utilize data with other cloud services. It can also enable efficient data processing by linking data between cloud services. Furthermore, data processing on the cloud can promote data interoperability between cloud services, leading to more effective data utilization. This integration of data processing on the cloud can be performed using AI or without AI. For instance, the status of data processing integration on the cloud can be input into a generating AI, which can then promote data interoperability.

[0053] Combining cloud computing and AI allows for the optimization of cloud computing resources by region, enabling efficient processing of region-specific health data. By optimizing cloud computing resources by region, region-specific health data can be processed efficiently. For example, cloud computing resources can be optimized based on region-specific health data. Cloud computing resources can also be allocated by region to efficiently process region-specific health data. Furthermore, cloud computing resources can be dynamically adjusted to efficiently process region-specific health data. This regional optimization of cloud computing resources can be performed using AI or without AI. For example, cloud computing resource usage can be input into a generating AI, which can then perform regional optimization.

[0054] By improving the sensor accuracy of wearable devices, the reliability of the collected health data can be increased. Wearable device sensors can be periodically calibrated to improve accuracy. Wearable device sensors can also be made more reliable by using high-precision sensors. Furthermore, wearable device sensors can be verified in real time to improve accuracy. This allows for increased reliability of collected health data through improved sensor accuracy. Improving the sensor accuracy of wearable devices can be done using AI or without AI. For example, data from a wearable device sensor can be input into a generating AI, which can then improve the sensor accuracy.

[0055] Wearable devices can have their battery life extended, enabling longer data collection periods. By extending the battery life of wearable devices, longer data collection periods become possible. Wearable device batteries can, for example, perform efficient power management to minimize battery consumption. Wearable device batteries can also, for example, use long-life batteries to enable longer data collection periods. Wearable device batteries can also, for example, adjust the frequency of data collection to minimize battery consumption. This extends the battery life of wearable devices, enabling longer data collection periods. The extension of wearable device battery life may be performed using AI, or it may be performed without AI. For example, the battery usage of a wearable device can be input into a generating AI, which can then extend the battery life.

[0056] Wearable devices can be improved to enhance user comfort by improving the fit. Wearable devices can be improved by using lightweight and flexible materials, for example. They can also be improved by adopting designs tailored to the user's body shape, for example. Wearable devices can also be made more comfortable by using breathable materials, for example, to ensure comfort even during prolonged wear. This improvement in the fit of wearable devices can enhance user comfort. This improvement in the fit of wearable devices may be achieved using AI, or it may be achieved without AI. For example, data on the fit of a wearable device can be input into a generating AI, which can then improve the fit.

[0057] Wearable devices can be linked with other health management devices to collect comprehensive health data. By linking wearable devices with other health management devices, comprehensive health data can be collected. For example, a wearable device can link with a smartphone to collect comprehensive health data. A wearable device can also link with other wearable devices to integrate health data. A wearable device can also link with a health management app to collect comprehensive health data. This allows for the collection of comprehensive health data by linking wearable devices with other health management devices. The linking of wearable devices may be done using AI, or without AI. For example, data from a wearable device can be input into a generating AI, which can then link with other health management devices.

[0058] By diversifying the designs of wearable devices, it is possible to offer choices that suit the user's lifestyle. Wearable devices can, for example, offer designs for sports. Wearable devices can, for example, offer designs for business. Wearable devices can, for example, offer designs for casual wear. In this way, by diversifying the designs of wearable devices, it is possible to offer choices that suit the user's lifestyle. The diversification of wearable device designs can be done using AI, for example, or without AI. For example, design data for wearable devices can be input into a generating AI, and the generating AI can perform the diversification of designs.

[0059] Wearable devices can transmit data to the cloud in real time, enabling immediate health management. By transmitting data from wearable devices to the cloud in real time, immediate health management can be achieved. Wearable devices, for example, can transmit data to the cloud in real time. Wearable devices can also process data in real time on the cloud to perform immediate health management. Wearable devices can also optimize data communication with the cloud to achieve immediate health management. This enables immediate health management by transmitting data from wearable devices to the cloud in real time. Real-time transmission from wearable devices may be performed using AI, or without AI. For example, data from a wearable device can be input into a generating AI, which can then transmit it to the cloud in real time.

[0060] Data management that prioritizes the protection of individual privacy: During data management, the encryption level of data can be dynamically changed to ensure data security. By dynamically changing the encryption level of data during data management, data security can be ensured. During data management, the encryption level can be dynamically changed according to, for example, the importance of the data. During data management, the encryption level can be dynamically adjusted according to, for example, the access status of the data. During data management, the encryption level can be dynamically changed according to, for example, the storage location of the data. In this way, data security can be ensured by dynamically changing the encryption level of data during data management. Dynamic changes in the encryption level during data management may be performed using, for example, AI, or without the use of AI. For example, during data management, the encryption status of the data can be input into a generating AI, and the generating AI can dynamically change the encryption level.

[0061] Data management that prioritizes the protection of individual privacy: When managing data, the scope of data use can be limited with the user's consent. By limiting the scope of data use with the user's consent during data management, privacy protection can be enhanced. For example, when managing data, the scope of data use can be limited with the user's consent. When managing data, for example, the scope of data sharing can be limited with the user's consent. When managing data, for example, the data retention period can be limited with the user's consent. This allows for enhanced privacy protection by limiting the scope of data use with the user's consent during data management. Limiting the scope of data use during data management can be done using AI, for example, or without AI. For example, when managing data, the user's consent status can be input into a generating AI, and the generating AI can limit the scope of data use.

[0062] Data management that prioritizes the protection of individual privacy: When managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. When managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. When managing data, for example, data backups can be distributed and stored in multiple cloud servers. When managing data, for example, data backups can be distributed and stored in multiple physical locations. When managing data, for example, data backups can be performed regularly to reduce the risk of data loss. Thus, when managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. Distributed storage of data backups during data management can be performed using AI, for example, or without using AI. For example, when managing data, the data backup status can be input into a generating AI, and the generating AI can perform distributed storage of backups.

[0063] Data management that prioritizes the protection of individual privacy: During data management, users' privacy policies can be regularly reviewed to ensure compliance with the latest laws and regulations. By regularly reviewing users' privacy policies during data management, compliance with the latest laws and regulations can be ensured. During data management, for example, users' privacy policies can be regularly reviewed to ensure compliance with the latest laws and regulations. During data management, for example, users' privacy policies can be regularly updated to ensure compliance with the latest laws and regulations. During data management, for example, users' privacy policies can be regularly checked to ensure compliance with the latest laws and regulations. As a result, by regularly reviewing users' privacy policies during data management, compliance with the latest laws and regulations can be ensured. The review of privacy policies during data management may be performed using AI, for example, or without the use of AI. For example, during data management, the status of the privacy policy can be input into a generating AI, and the generating AI can perform the review of the privacy policy.

[0064] Data management that prioritizes the protection of individual privacy: When managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. When managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. When managing data, for example, data backups can be distributed and stored in multiple cloud servers. When managing data, for example, data backups can be distributed and stored in multiple physical locations. When managing data, for example, data backups can be performed regularly to reduce the risk of data loss. Thus, when managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. Distributed storage of data backups during data management can be performed using AI, for example, or without using AI. For example, when managing data, the data backup status can be input into a generating AI, and the generating AI can perform distributed storage of backups.

[0065] Data management that prioritizes the protection of individual privacy: During data management, users' privacy policies can be regularly reviewed to ensure compliance with the latest laws and regulations. By regularly reviewing users' privacy policies during data management, compliance with the latest laws and regulations can be ensured. During data management, for example, users' privacy policies can be regularly reviewed to ensure compliance with the latest laws and regulations. During data management, for example, users' privacy policies can be regularly updated to ensure compliance with the latest laws and regulations. During data management, for example, users' privacy policies can be regularly checked to ensure compliance with the latest laws and regulations. As a result, by regularly reviewing users' privacy policies during data management, compliance with the latest laws and regulations can be ensured. The review of privacy policies during data management may be performed using AI, for example, or without the use of AI. For example, during data management, the status of the privacy policy can be input into a generating AI, and the generating AI can perform the review of the privacy policy.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The data collection unit can adjust the data collection method based on the user's living environment when collecting user health data. For example, if the user lives in an urban area, environmental data such as air quality and noise levels can be collected. If the user lives in a rural area, pesticide use and water quality data can also be collected. Furthermore, if the user lives at high altitude, oxygen saturation and atmospheric pressure data can also be collected. By adjusting the health data collection method based on the user's living environment, more appropriate data collection becomes possible. 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 the user's living environment data into a generating AI, which can then adjust the data collection method.

[0068] The analysis unit can improve the accuracy of its analysis by considering the user's genetic data when analyzing the user's health data. For example, it can predict the risk of specific diseases based on the user's genetic data. It can also predict the effects and side effects of drugs based on the user's genetic data. Furthermore, it can suggest optimal diet and exercise plans based on the user's genetic data. This makes it possible to analyze health data with higher accuracy by considering the user's genetic data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's genetic data into a generating AI, which can then improve the accuracy of the analysis.

[0069] The data collection unit can adjust the frequency of data collection based on the user's lifestyle when collecting user health data. For example, if a user exercises regularly, data collected after exercise can be prioritized. If a user has an irregular lifestyle, sleep data can be collected more frequently. Furthermore, if a user consumes a specific diet, data collected after meals can also be collected. By adjusting the frequency of health data collection based on the user's lifestyle, more appropriate data collection becomes possible. 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 user lifestyle data into a generating AI, which can then adjust the collection frequency.

[0070] The data collection unit can adjust the data collection method based on the user's activity level when collecting user health data. For example, if the user has a high activity level, data collected during exercise can be prioritized. If the user has a low activity level, data collected during rest can be prioritized. Furthermore, if the user has a moderate activity level, data from daily life can be collected. By adjusting the health data collection method based on the user's activity level, more appropriate data collection becomes possible. 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 user activity level data into a generating AI, which can then adjust the collection method.

[0071] The analysis unit can adjust its analysis algorithm based on the user's lifestyle when analyzing the user's health data. For example, if the user exercises regularly, the exercise data analysis algorithm can be applied. If the user has an irregular lifestyle, the sleep data analysis algorithm can be applied. Furthermore, if the user consumes a specific diet, the diet data analysis algorithm can be applied. By adjusting the health data analysis algorithm based on the user's lifestyle, more appropriate data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI, which can then adjust the analysis algorithm.

[0072] The data collection unit can adjust the data collection method based on the user's geographical location when collecting user health data. For example, if the user is in an urban area, environmental data such as air quality and noise levels can be collected. If the user is in a rural area, pesticide use and water quality data can also be collected. Furthermore, if the user is at high altitude, oxygen saturation and atmospheric pressure data can also be collected. By adjusting the health data collection method based on the user's geographical location, more appropriate data collection becomes possible. 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 the user's geographical location data into a generating AI, which can then adjust the collection method.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The data collection unit collects the user's health data. The data collection unit can collect health data such as heart rate, blood pressure, body temperature, and exercise level in real time, for example, using IoT devices. The data collection unit can also collect the user's health data using wearable devices or smartphone apps. Step 2: The analysis unit analyzes the health data collected by the collection unit. The analysis unit analyzes the health data using, for example, machine learning. Machine learning uses algorithms such as deep learning and support vector machines to analyze patterns in the health data. Furthermore, the analysis unit can analyze trends in the health data and predict the user's health status. It can also detect anomalies in the health data and issue warnings to the user. Step 3: The service provider provides health management and preventive measures based on the analysis results obtained by the analysis provider. For example, the service provider proposes an intervention plan tailored to each user's health condition. Specifically, this includes health improvement suggestions such as dietary guidance, exercise plans, and medical advice. This is expected to improve the rate of health improvement, increase the rate of early disease detection, and optimize lifestyle habits.

[0075] (Example of form 2) The AI ​​healthcare agent system according to an embodiment of the present invention is a system that continuously collects a user's health data and provides customized health management and preventive measures based on the individual's health condition. This system analyzes the user's lifestyle, genetic data, and the results of regular health checks to make specific health improvement suggestions. The AI ​​agent collects real-time health data from IoT devices and analyzes the health data using machine learning. This allows the system to propose intervention plans tailored to each user's health condition. For example, it can be expected to improve the rate of health improvement, increase the rate of early disease detection, and optimize lifestyle habits. It is also characterized by a combination of cloud computing and AI, the use of wearable devices, and data management that emphasizes the protection of individual privacy. Target users include health-conscious individuals, patients with chronic diseases, elderly people who want to manage their health efficiently, medical institutions, health insurance companies, and corporate health management departments. The AI ​​analyzes the user's health data and generates optimal health advice in real time. For example, the AI ​​healthcare agent system uses IoT devices to collect the user's health data. IoT devices can collect health data such as heart rate, blood pressure, body temperature, and exercise level in real time. This allows the system to constantly monitor the user's health condition and provide appropriate health management and preventive measures. Furthermore, the AI ​​healthcare agent system analyzes health data using machine learning. Machine learning uses algorithms such as deep learning and support vector machines to analyze patterns in health data and predict the user's health status. For example, based on the user's health data, the AI ​​healthcare agent system provides specific health improvement suggestions, such as dietary guidance, exercise plans, and medical advice. This allows users to receive optimal health management and preventative measures tailored to their individual health condition. The AI ​​healthcare agent system also securely manages health data using cloud computing. Cloud computing protects user privacy by enhancing security protocols such as data encryption and access control.Furthermore, the AI ​​healthcare agent system utilizes wearable devices to collect user health data. These wearable devices enhance user comfort through technologies such as improved sensor accuracy, extended battery life, and improved fit. This allows users to collect health data over extended periods. The AI ​​healthcare agent system can then collect and analyze this data to provide health management and preventative measures.

[0076] The AI ​​healthcare agent system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects user health data. The collection unit can, for example, collect health data such as heart rate, blood pressure, body temperature, and exercise level in real time using IoT devices. The collection unit can also, for example, collect user health data using wearable devices. The collection unit can also, for example, collect user health data using smartphone applications. The analysis unit analyzes the health data collected by the collection unit. The analysis unit analyzes the health data using, for example, machine learning. Machine learning analyzes patterns in health data using algorithms such as deep learning and support vector machines. The analysis unit can, for example, analyze trends in health data and predict the user's health status. The analysis unit can, for example, detect anomalies in health data and issue warnings to the user. The provision unit provides health management and preventive measures based on the analysis results obtained by the analysis unit. The provision unit proposes intervention plans tailored to each user's health status. The provision unit provides specific health improvement suggestions, such as dietary guidance, exercise plans, and medical advice. The provisioning unit can be expected to have effects such as improving the rate of improvement in health status, improving the rate of early detection of diseases, and optimizing lifestyle habits. As a result, the AI ​​healthcare agent system according to the embodiment can collect and analyze the user's health data and provide health management and preventive measures. Some or all of the above-described processes in the collection unit, analysis unit, and provisioning unit may be performed using AI, for example, or without using AI. For example, the collection unit can input health data collected from IoT devices into the AI, and the AI ​​can analyze the data and output the collection results. The analysis unit can input health data obtained from the collection unit into the AI, and the AI ​​can analyze the data and output the analysis results. The provisioning unit can input the analysis results obtained from the analysis unit into the AI, and the AI ​​can generate and provide health management and preventive measures.

[0077] The data collection unit collects user health data. For example, the data collection unit can collect health data such as heart rate, blood pressure, body temperature, and exercise level in real time using IoT devices. Specifically, IoT devices include heart rate sensors for measuring heart rate, blood pressure monitors for measuring blood pressure, thermometers for measuring body temperature, and accelerometers and gyroscopes for measuring exercise level. These devices are worn on the user's body and continuously collect data during daily life. The collected data is transmitted to a smartphone or cloud server via wireless communication such as Bluetooth or Wi-Fi. The data collection unit can also collect user health data using wearable devices. Wearable devices include smartwatches and fitness trackers, which are worn on the user's wrist or arm and collect data such as heart rate, steps, calories burned, and sleep patterns. This data is transmitted to the user's smartphone through a dedicated application and stored on a cloud server. The data collection unit can also collect user health data using smartphone apps. Smartphone apps integrate data manually entered by the user with data automatically collected from sensors built into the smartphone. For example, users can input daily meal details, weight, blood pressure, and other information into the app. They can also use their smartphone's GPS function to record their exercise levels and distance traveled. This allows the data collection unit to comprehensively collect and manage user health data in real time using a variety of devices and methods. Furthermore, the data collection unit can centrally manage this data and integrate it with other systems and departments as needed. For instance, collected data can be stored on a cloud server, making it accessible to the analysis and provisioning departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0078] The analysis department analyzes health data collected by the data collection department. For example, the analysis department uses machine learning to analyze health data. Machine learning uses algorithms such as deep learning and support vector machines to analyze patterns in health data. Specifically, deep learning can be used to analyze fluctuation patterns in heart rate and blood pressure and detect abnormal patterns. For example, if a user's heart rate remains higher than normal, it could be a sign of stress, lack of exercise, or illness, and the analysis department can issue a warning to the user. Furthermore, support vector machines can be used to classify user health data and predict trends in health status. For example, based on past data, the analysis department can predict whether a user's health is improving or worsening and suggest appropriate countermeasures. In addition, the analysis department implements algorithms to detect anomalies, automatically detecting abnormal values ​​from the collected data and issuing warnings to users. For example, it can detect anomalies that could have a significant impact on health, such as a sudden increase in blood pressure or an abnormally high body temperature, and notify the user. The analysis department can also utilize past data and statistical information to assess long-term health risks. For example, based on historical data, it is possible to predict fluctuations in health risks during specific seasons or time periods and formulate future countermeasures. This allows the analysis department to quickly and accurately analyze collected data and understand users' health status in real time. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis department can not only understand the situation in real time but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0079] The service provider provides health management and preventive measures based on the analysis results obtained by the analysis provider. For example, the service provider proposes intervention plans tailored to each user's health condition. Specifically, it creates and provides individualized health management plans based on the user's health data. For example, it can propose stress management and exercise plans based on heart rate and blood pressure data. It also provides specific health improvement suggestions such as dietary guidance, exercise plans, and medical advice. For example, it can analyze the user's diet and propose improvements to nutritional balance and adjustments to calorie intake. Furthermore, it can create exercise plans and provide specific advice to improve the user's exercise habits. For example, it can suggest how many times a week and how much exercise should be done to support the improvement of the user's health condition. It can also provide medical advice and encourage users to visit appropriate medical institutions according to their health condition. For example, if abnormal values ​​are detected, it can send a notification to the user recommending a visit to a medical institution. As a result, the service provider can expect effects such as an increase in the rate of improvement in users' health conditions, an increase in the rate of early detection of diseases, and optimization of lifestyle habits. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the health management and preventive measures it provides. For example, the service provider can analyze how users responded to the provided health management plan and incorporate that feedback into future suggestions. Furthermore, the service provider can reliably transmit information using multiple communication methods. For instance, important information can be delivered not only via smartphone notifications but also through voice calls, SMS, and email. This allows the service provider to quickly and reliably provide users with health management and preventative measures, supporting improvements in their health.

[0080] The data collection unit can collect real-time health data from IoT devices. For example, the data collection unit can collect health data such as heart rate, blood pressure, body temperature, and exercise level in real time using IoT devices. The data collection unit can also collect health data such as sleep data and stress levels in real time using IoT devices. The data collection unit can also collect health data such as diet data and water intake in real time using IoT devices. This allows for the collection of real-time health data from IoT devices. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the health data collected from IoT devices into a generating AI, which can then analyze the data and output the collection results.

[0081] The analysis unit can analyze health data using machine learning. For example, the analysis unit can analyze health data using deep learning. The analysis unit can also analyze health data using support vector machines. The analysis unit can also analyze health data using random forests. This improves the accuracy of health data analysis by using machine learning. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, which can then analyze the data and output analysis results.

[0082] The service provider can propose intervention plans tailored to each user's health condition. For example, based on the user's health data, the service provider can provide specific health improvement suggestions such as dietary guidance, exercise plans, and medical advice. For example, the service provider can also propose intervention plans such as stress management, sleep improvement, and weight management, depending on the user's health condition. For example, based on the user's health data, the service provider can also propose early detection and prevention measures for diseases. This allows the service provider to propose intervention plans tailored to each user's health condition. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input health data into a generating AI, and the generating AI can generate and provide an intervention plan.

[0083] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may collect health data during times when the user is relaxed. For example, if the user is tired, the data collection unit may also collect health data during rest. For example, if the user is active, the data collection unit may prioritize collecting data during exercise. This allows for more appropriate data collection by adjusting the timing of health data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can estimate the emotion and adjust the collection timing.

[0084] The data collection unit can analyze the user's past health data history and select the optimal collection method. For example, the data collection unit can select the most reliable sensor from the user's past data. The data collection unit can also analyze the user's past data collection patterns and determine the optimal collection frequency. For example, the data collection unit can select a method from the user's past data that improves accuracy by collecting data at a specific time period. This allows the optimal collection method to be selected by analyzing the user's past health data history. 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 the user's past health data into a generating AI, which can then select the optimal collection method.

[0085] The data collection unit can filter health data based on the user's current lifestyle and activity level. For example, if the user is at work, the data collection unit may prioritize collecting stress levels. If the user is exercising, the data collection unit may prioritize collecting heart rate and calorie consumption. If the user is resting, the data collection unit may prioritize collecting sleep quality and relaxation levels. By filtering health data based on the user's lifestyle and activity level, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and activity level into a generating AI, which can then perform the filtering.

[0086] The data collection unit can estimate the user's emotions and determine the priority of health data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit may prioritize collecting data that measures relaxation levels. For example, if the user is excited, the data collection unit may prioritize collecting heart rate and blood pressure data. This allows for the priority collection of important data by prioritizing health data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of health data to collect.

[0087] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit may prioritize the collection of oxygen saturation data. For example, if the user is in an urban area, the data collection unit may prioritize the collection of air quality data. For example, if the user is outdoors, the data collection unit may prioritize the collection of ultraviolet level data. This allows for the priority collection of highly relevant health data by considering the user's geographical location. 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 the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0088] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user is experiencing stress on social media, the data collection unit may prioritize collecting stress levels. For example, if a user posts about exercise on social media, the data collection unit may prioritize collecting exercise data. For example, if a user posts about food on social media, the data collection unit may prioritize collecting dietary data. In this way, relevant health data can be collected by analyzing a user's social media activity. 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 the user's social media activity data into a generating AI, which can then collect relevant health data.

[0089] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually appealing analysis result. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. For example, the analysis unit can perform a concise analysis on general health data. For example, the analysis unit can perform a rapid analysis on urgent health data. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input health data into a generating AI, which can then adjust the level of detail of the analysis based on importance.

[0091] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit can apply an exercise intensity analysis algorithm to exercise data. By applying different analysis algorithms depending on the category of health data, appropriate analysis according to the characteristics of the data becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, and the generating AI can apply different analysis algorithms depending on the category.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. For example, if the user is excited, the analysis unit can also provide a visually appealing analysis. By adjusting the length of the analysis based on the user's emotions, appropriate analysis results can be provided according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the length of the analysis.

[0093] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also prioritize the analysis of regularly collected data. The analysis unit may also prioritize the analysis of data related to a specific event. This allows for the prioritization of the most recent data by determining the priority of analysis based on when the health data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input health data into a generating AI, which can then determine the priority of analysis based on when the data was collected.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the health data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI, which can then adjust the order of analysis based on relevance.

[0095] The service provider can estimate the user's emotions and adjust the presentation of health management and preventative measures based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and easy-to-understand presentation. For example, if the user is relaxed, the service provider can provide a more detailed presentation. For example, if the user is excited, the service provider can provide a visually appealing presentation. This allows for more appropriate information to be provided by adjusting the presentation of health management and preventative measures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotion and adjust the presentation.

[0096] The information provider can adjust the level of detail provided based on the importance of the health management and preventive measures at the time of provision. For example, the provider can provide detailed information for important health management and preventive measures. For example, the provider can provide concise information for general health management and preventive measures. For example, the provider can provide information quickly for urgent health management and preventive measures. This allows for detailed provision of important information by adjusting the level of detail based on the importance of the health management and preventive measures. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input health management and preventive measures information into a generating AI, which can then adjust the level of detail based on importance.

[0097] The information delivery unit can apply different delivery algorithms depending on the category of health management and preventive measures at the time of delivery. For example, the information delivery unit can apply a nutritional balance analysis algorithm to diet management. For example, the information delivery unit can also apply an exercise intensity analysis algorithm to exercise management. For example, the information delivery unit can also apply a stress level analysis algorithm to stress management. By applying different delivery algorithms depending on the category of health management and preventive measures, appropriate information can be provided. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input health management and preventive measures information into a generating AI, and the generating AI can apply different delivery algorithms depending on the category.

[0098] The service provider can estimate the user's emotions and adjust the length of the health management and preventative measures provided based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise information. If the user is relaxed, the service provider can also provide detailed information. If the user is excited, the service provider can also provide visually appealing information. By adjusting the length of health management and preventative measures based on the user's emotions, it becomes possible to provide appropriate information tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then estimate the emotions and adjust the length of the information provided.

[0099] The service provider can determine the priority of information delivery based on the timing of health management and preventive measures collection. For example, the service provider may prioritize information based on recently collected data. The service provider may also prioritize information based on regularly collected data. The service provider may also prioritize information based on data related to a specific event. This ensures that the most up-to-date information is delivered by prioritizing delivery based on the timing of health management and preventive measures collection. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input health management and preventive measures information into a generating AI, which can then determine the priority of delivery based on the collection timing.

[0100] The information delivery unit can adjust the order of delivery based on the relevance between health management and preventive measures. For example, the delivery unit may prioritize the delivery of highly relevant information. For example, the delivery unit may postpone the delivery of less relevant information. The delivery unit can also dynamically adjust the order of delivery based on the relevance of the information. This allows for the priority delivery of highly relevant information by adjusting the order of delivery based on the relevance between health management and preventive measures. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input health management and preventive measure information into a generating AI, which can then adjust the order of delivery based on relevance.

[0101] Combining cloud computing and AI allows for the dynamic allocation of cloud computing resources, thereby optimizing AI processing capabilities. By dynamically allocating cloud computing resources, AI processing capabilities can be optimized. Cloud computing resources can be dynamically allocated, for example, according to the amount of data a user provides. Cloud computing resources can also be dynamically adjusted, for example, according to the processing load of the AI. Cloud computing resources can also be dynamically allocated, for example, to resources required for real-time data processing. This allows for the optimization of AI processing capabilities by dynamically allocating cloud computing resources. Dynamic allocation of cloud computing resources may be performed using AI, or it may be performed without AI. For example, the usage status of cloud computing resources can be input into a generating AI, which can then dynamically allocate resources.

[0102] Combining cloud computing and AI allows for the decentralization of data processing on the cloud, thereby improving processing speed. Distributing data processing on the cloud can improve processing speed. For example, data processing on the cloud can be distributed across multiple cloud servers. For example, data processing on the cloud can evenly distribute the data processing load. For example, distributed processing can be used to eliminate data processing bottlenecks. Thus, by distributing data processing on the cloud, processing speed can be improved. Distributing data processing on the cloud can be done using AI, or without AI. For example, the data processing load on the cloud can be input into a generating AI, which can then perform the data processing distribution.

[0103] Combining cloud computing with AI can enhance the security protocols of cloud computing and ensure data security. By strengthening the security protocols of cloud computing, data security can be ensured. For example, the security protocols of cloud computing can enhance data encryption. For example, the security protocols of cloud computing can be regularly updated. For example, the security protocols of cloud computing can enhance data access monitoring and logging. Thus, by strengthening the security protocols of cloud computing, data security can be ensured. The strengthening of cloud computing security protocols may be done using AI, or it may be done without AI. For example, the security status of cloud computing can be input into a generating AI, and the generating AI can strengthen the security protocols.

[0104] The combination of cloud computing and AI allows for the sharing of cloud computing resources with other AI applications, enabling efficient resource utilization. By sharing cloud computing resources with other AI applications, efficient resource utilization can be achieved. For example, multiple AI applications can share the same cloud resources. Cloud computing resources can also be dynamically allocated among AI applications based on resource usage. Cloud computing resources can also be efficiently shared among AI applications to minimize resource waste. This allows for efficient resource utilization by sharing cloud computing resources with other AI applications. Resource sharing of cloud computing resources can be performed using AI, or without AI. For example, the usage of cloud computing resources can be input into a generating AI, which can then share the resources.

[0105] The combination of cloud computing and AI allows for the integration of data processing on the cloud with other cloud services, promoting data interoperability. This integration facilitates data sharing and mutual use. For example, data processing on the cloud can share and mutually utilize data with other cloud services. It can also enable efficient data processing by linking data between cloud services. Furthermore, data processing on the cloud can promote data interoperability between cloud services, leading to more effective data utilization. This integration of data processing on the cloud can be performed using AI or without AI. For instance, the status of data processing integration on the cloud can be input into a generating AI, which can then promote data interoperability.

[0106] Combining cloud computing and AI allows for the optimization of cloud computing resources by region, enabling efficient processing of region-specific health data. By optimizing cloud computing resources by region, region-specific health data can be processed efficiently. For example, cloud computing resources can be optimized based on region-specific health data. Cloud computing resources can also be allocated by region to efficiently process region-specific health data. Furthermore, cloud computing resources can be dynamically adjusted to efficiently process region-specific health data. This regional optimization of cloud computing resources can be performed using AI or without AI. For example, cloud computing resource usage can be input into a generating AI, which can then perform regional optimization.

[0107] By improving the sensor accuracy of wearable devices, the reliability of the collected health data can be increased. Wearable device sensors can be periodically calibrated to improve accuracy. Wearable device sensors can also be made more reliable by using high-precision sensors. Furthermore, wearable device sensors can be verified in real time to improve accuracy. This allows for increased reliability of collected health data through improved sensor accuracy. Improving the sensor accuracy of wearable devices can be done using AI or without AI. For example, data from a wearable device sensor can be input into a generating AI, which can then improve the sensor accuracy.

[0108] Wearable devices can have their battery life extended, enabling longer data collection periods. By extending the battery life of wearable devices, longer data collection periods become possible. Wearable device batteries can, for example, perform efficient power management to minimize battery consumption. Wearable device batteries can also, for example, use long-life batteries to enable longer data collection periods. Wearable device batteries can also, for example, adjust the frequency of data collection to minimize battery consumption. This extends the battery life of wearable devices, enabling longer data collection periods. The extension of wearable device battery life may be performed using AI, or it may be performed without AI. For example, the battery usage of a wearable device can be input into a generating AI, which can then extend the battery life.

[0109] Wearable devices can be improved to enhance user comfort by improving the fit. Wearable devices can be improved by using lightweight and flexible materials, for example. They can also be improved by adopting designs tailored to the user's body shape, for example. Wearable devices can also be made more comfortable by using breathable materials, for example, to ensure comfort even during prolonged wear. This improvement in the fit of wearable devices can enhance user comfort. This improvement in the fit of wearable devices may be achieved using AI, or it may be achieved without AI. For example, data on the fit of a wearable device can be input into a generating AI, which can then improve the fit.

[0110] Wearable devices can be linked with other health management devices to collect comprehensive health data. By linking wearable devices with other health management devices, comprehensive health data can be collected. For example, a wearable device can link with a smartphone to collect comprehensive health data. A wearable device can also link with other wearable devices to integrate health data. A wearable device can also link with a health management app to collect comprehensive health data. This allows for the collection of comprehensive health data by linking wearable devices with other health management devices. The linking of wearable devices may be done using AI, or without AI. For example, data from a wearable device can be input into a generating AI, which can then link with other health management devices.

[0111] By diversifying the designs of wearable devices, it is possible to offer choices that suit the user's lifestyle. Wearable devices can, for example, offer designs for sports. Wearable devices can, for example, offer designs for business. Wearable devices can, for example, offer designs for casual wear. In this way, by diversifying the designs of wearable devices, it is possible to offer choices that suit the user's lifestyle. The diversification of wearable device designs can be done using AI, for example, or without AI. For example, design data for wearable devices can be input into a generating AI, and the generating AI can perform the diversification of designs.

[0112] Wearable devices can transmit data to the cloud in real time, enabling immediate health management. By transmitting data from wearable devices to the cloud in real time, immediate health management can be achieved. Wearable devices, for example, can transmit data to the cloud in real time. Wearable devices can also process data in real time on the cloud to perform immediate health management. Wearable devices can also optimize data communication with the cloud to achieve immediate health management. This enables immediate health management by transmitting data from wearable devices to the cloud in real time. Real-time transmission from wearable devices may be performed using AI, or without AI. For example, data from a wearable device can be input into a generating AI, which can then transmit it to the cloud in real time.

[0113] Data management that prioritizes the protection of individual privacy: During data management, the system can estimate the user's emotions and adjust data access permissions based on those estimated emotions. By estimating the user's emotions during data management and adjusting data access permissions based on those estimated emotions, privacy protection can be enhanced. For example, during data management, if the user is stressed, data access permissions can be restricted. For example, during data management, if the user is relaxed, data access permissions can be relaxed. For example, during data management, if the user is excited, data access permissions can be dynamically adjusted. This enhances privacy protection by adjusting data access permissions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing during data management may be performed using AI, or not using AI. For example, during data management, user emotion data can be input into a generative AI, which can estimate the emotions and adjust data access permissions.

[0114] Data management that prioritizes the protection of individual privacy: During data management, the encryption level of data can be dynamically changed to ensure data security. By dynamically changing the encryption level of data during data management, data security can be ensured. During data management, the encryption level can be dynamically changed according to, for example, the importance of the data. During data management, the encryption level can be dynamically adjusted according to, for example, the access status of the data. During data management, the encryption level can be dynamically changed according to, for example, the storage location of the data. In this way, data security can be ensured by dynamically changing the encryption level of data during data management. Dynamic changes in the encryption level during data management may be performed using, for example, AI, or without the use of AI. For example, during data management, the encryption status of the data can be input into a generating AI, and the generating AI can dynamically change the encryption level.

[0115] Data management that prioritizes the protection of individual privacy: When managing data, the scope of data use can be limited with the user's consent. By limiting the scope of data use with the user's consent during data management, privacy protection can be enhanced. For example, when managing data, the scope of data use can be limited with the user's consent. When managing data, for example, the scope of data sharing can be limited with the user's consent. When managing data, for example, the data retention period can be limited with the user's consent. This allows for enhanced privacy protection by limiting the scope of data use with the user's consent during data management. Limiting the scope of data use during data management can be done using AI, for example, or without AI. For example, when managing data, the user's consent status can be input into a generating AI, and the generating AI can limit the scope of data use.

[0116] Data management that prioritizes the protection of individual privacy: During data management, the user's emotions can be estimated, and the data retention period can be adjusted based on the estimated emotions. By estimating the user's emotions during data management and adjusting the data retention period based on the estimated emotions, privacy protection can be enhanced. During data management, for example, if the user is stressed, the data retention period can be shortened. During data management, for example, if the user is relaxed, the data retention period can be extended. During data management, for example, if the user is excited, the data retention period can be dynamically adjusted. In this way, privacy protection can be enhanced by adjusting the data retention period based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing during data management may be performed using AI, for example, or not using AI. For example, during data management, user emotion data can be input into a generative AI, and the generative AI can estimate the emotions and adjust the data retention period.

[0117] Data management that prioritizes the protection of individual privacy: When managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. When managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. When managing data, for example, data backups can be distributed and stored in multiple cloud servers. When managing data, for example, data backups can be distributed and stored in multiple physical locations. When managing data, for example, data backups can be performed regularly to reduce the risk of data loss. Thus, when managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. Distributed storage of data backups during data management can be performed using AI, for example, or without using AI. For example, when managing data, the data backup status can be input into a generating AI, and the generating AI can perform distributed storage of backups.

[0118] Data management that prioritizes the protection of individual privacy: During data management, users' privacy policies can be regularly reviewed to ensure compliance with the latest laws and regulations. By regularly reviewing users' privacy policies during data management, compliance with the latest laws and regulations can be ensured. During data management, for example, users' privacy policies can be regularly reviewed to ensure compliance with the latest laws and regulations. During data management, for example, users' privacy policies can be regularly updated to ensure compliance with the latest laws and regulations. During data management, for example, users' privacy policies can be regularly checked to ensure compliance with the latest laws and regulations. As a result, by regularly reviewing users' privacy policies during data management, compliance with the latest laws and regulations can be ensured. The review of privacy policies during data management may be performed using AI, for example, or without the use of AI. For example, during data management, the status of the privacy policy can be input into a generating AI, and the generating AI can perform the review of the privacy policy.

[0119] Data management that prioritizes the protection of individual privacy: During data management, the user's emotions can be estimated, and the data retention period can be adjusted based on the estimated emotions. By estimating the user's emotions during data management and adjusting the data retention period based on the estimated emotions, privacy protection can be enhanced. During data management, for example, if the user is stressed, the data retention period can be shortened. During data management, for example, if the user is relaxed, the data retention period can be extended. During data management, for example, if the user is excited, the data retention period can be dynamically adjusted. In this way, privacy protection can be enhanced by adjusting the data retention period based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing during data management may be performed using AI, for example, or not using AI. For example, during data management, user emotion data can be input into a generative AI, and the generative AI can estimate the emotions and adjust the data retention period.

[0120] Data management that prioritizes the protection of individual privacy: When managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. When managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. When managing data, for example, data backups can be distributed and stored in multiple cloud servers. When managing data, for example, data backups can be distributed and stored in multiple physical locations. When managing data, for example, data backups can be performed regularly to reduce the risk of data loss. Thus, when managing data, data backups can be distributed and stored in multiple locations to reduce the risk of data loss. Distributed storage of data backups during data management can be performed using AI, for example, or without using AI. For example, when managing data, the data backup status can be input into a generating AI, and the generating AI can perform distributed storage of backups.

[0121] Data management that prioritizes the protection of individual privacy: During data management, users' privacy policies can be regularly reviewed to ensure compliance with the latest laws and regulations. By regularly reviewing users' privacy policies during data management, compliance with the latest laws and regulations can be ensured. During data management, for example, users' privacy policies can be regularly reviewed to ensure compliance with the latest laws and regulations. During data management, for example, users' privacy policies can be regularly updated to ensure compliance with the latest laws and regulations. During data management, for example, users' privacy policies can be regularly checked to ensure compliance with the latest laws and regulations. As a result, by regularly reviewing users' privacy policies during data management, compliance with the latest laws and regulations can be ensured. The review of privacy policies during data management may be performed using AI, for example, or without the use of AI. For example, during data management, the status of the privacy policy can be input into a generating AI, and the generating AI can perform the review of the privacy policy.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The data collection unit can adjust the data collection method based on the user's living environment when collecting user health data. For example, if the user lives in an urban area, environmental data such as air quality and noise levels can be collected. If the user lives in a rural area, pesticide use and water quality data can also be collected. Furthermore, if the user lives at high altitude, oxygen saturation and atmospheric pressure data can also be collected. By adjusting the health data collection method based on the user's living environment, more appropriate data collection becomes possible. 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 the user's living environment data into a generating AI, which can then adjust the data collection method.

[0124] The analysis unit can improve the accuracy of its analysis by considering the user's genetic data when analyzing the user's health data. For example, it can predict the risk of specific diseases based on the user's genetic data. It can also predict the effects and side effects of drugs based on the user's genetic data. Furthermore, it can suggest optimal diet and exercise plans based on the user's genetic data. This makes it possible to analyze health data with higher accuracy by considering the user's genetic data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's genetic data into a generating AI, which can then improve the accuracy of the analysis.

[0125] The service provider can estimate the user's emotions based on the user's health data and provide health management and preventative measures based on the estimated emotions. For example, if the user is stressed, it can suggest relaxation methods to reduce stress. If the user is relaxed, it can suggest an exercise plan to maintain health. Furthermore, if the user is excited, it can provide advice to stabilize heart rate and blood pressure. This allows for more appropriate information to be provided by offering health management and preventative measures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's emotion data into a generative AI, which can then estimate the emotions and provide health management and preventative measures.

[0126] The data collection unit can adjust the frequency of data collection based on the user's lifestyle when collecting user health data. For example, if a user exercises regularly, data collected after exercise can be prioritized. If a user has an irregular lifestyle, sleep data can be collected more frequently. Furthermore, if a user consumes a specific diet, data collected after meals can also be collected. By adjusting the frequency of health data collection based on the user's lifestyle, more appropriate data collection becomes possible. 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 user lifestyle data into a generating AI, which can then adjust the collection frequency.

[0127] The analysis unit can estimate the user's emotions when analyzing user health data and determine the analysis priority based on the estimated emotions. For example, if the user is stressed, stress-related data can be prioritized for analysis. If the user is relaxed, data measuring relaxation level can be prioritized for analysis. Furthermore, if the user is excited, heart rate and blood pressure data can be prioritized for analysis. This allows for the prioritization of important data by determining the analysis priority of health data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and determine the analysis priority.

[0128] The service provider can estimate the user's emotions based on the user's health data and adjust the timing of providing health management and preventative measures based on the estimated emotions. For example, if the user is stressed, health management and preventative measures can be provided during times of relaxation. If the user is tired, health management and preventative measures can also be provided during rest. Furthermore, if the user is active, health management and preventative measures can be provided based on data from exercise. This allows for more appropriate information to be provided by adjusting the timing of health management and preventative measures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's emotion data into a generative AI, which can estimate the emotions and adjust the timing of provision.

[0129] The data collection unit can adjust the data collection method based on the user's activity level when collecting user health data. For example, if the user has a high activity level, data collected during exercise can be prioritized. If the user has a low activity level, data collected during rest can be prioritized. Furthermore, if the user has a moderate activity level, data from daily life can be collected. By adjusting the health data collection method based on the user's activity level, more appropriate data collection becomes possible. 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 user activity level data into a generating AI, which can then adjust the collection method.

[0130] The analysis unit can adjust its analysis algorithm based on the user's lifestyle when analyzing the user's health data. For example, if the user exercises regularly, the exercise data analysis algorithm can be applied. If the user has an irregular lifestyle, the sleep data analysis algorithm can be applied. Furthermore, if the user consumes a specific diet, the diet data analysis algorithm can be applied. By adjusting the health data analysis algorithm based on the user's lifestyle, more appropriate data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI, which can then adjust the analysis algorithm.

[0131] The service provider can estimate the user's emotions based on the user's health data and adjust the content of health management and preventive measures based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxation methods to reduce stress. If the user is relaxed, it can suggest an exercise plan to maintain health. Furthermore, if the user is excited, it can provide advice to stabilize heart rate and blood pressure. This allows for the provision of more appropriate information by adjusting the content of health management and preventive measures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the user's emotion data into a generative AI, which can estimate the emotions and adjust the content of health management and preventive measures.

[0132] The data collection unit can adjust the data collection method based on the user's geographical location when collecting user health data. For example, if the user is in an urban area, environmental data such as air quality and noise levels can be collected. If the user is in a rural area, pesticide use and water quality data can also be collected. Furthermore, if the user is at high altitude, oxygen saturation and atmospheric pressure data can also be collected. By adjusting the health data collection method based on the user's geographical location, more appropriate data collection becomes possible. 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 the user's geographical location data into a generating AI, which can then adjust the collection method.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The data collection unit collects the user's health data. The data collection unit can collect health data such as heart rate, blood pressure, body temperature, and exercise level in real time, for example, using IoT devices. The data collection unit can also collect the user's health data using wearable devices or smartphone apps. Step 2: The analysis unit analyzes the health data collected by the collection unit. The analysis unit analyzes the health data using, for example, machine learning. Machine learning uses algorithms such as deep learning and support vector machines to analyze patterns in the health data. Furthermore, the analysis unit can analyze trends in the health data and predict the user's health status. It can also detect anomalies in the health data and issue warnings to the user. Step 3: The service provider provides health management and preventive measures based on the analysis results obtained by the analysis provider. For example, the service provider proposes an intervention plan tailored to each user's health condition. Specifically, this includes health improvement suggestions such as dietary guidance, exercise plans, and medical advice. This is expected to improve the rate of health improvement, increase the rate of early disease detection, and optimize lifestyle habits.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects health data using sensors or IoT devices of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the health data using machine learning. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides health management and preventive measures based on the analysis results. Each element of the collection unit, analysis unit, and provision unit may also be implemented in the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision 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 health data using sensors and IoT devices of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the health data using machine learning. The data provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides health management and preventive measures based on the analysis results. Each element of the data collection unit, analysis unit, and data provision unit may also be implemented in the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects health data using sensors and IoT devices of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the health data using machine learning. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides health management and preventive measures based on the analysis results. Each element of the collection unit, analysis unit, and provision unit may also be implemented in the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects health data using sensors and IoT devices of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the health data using machine learning. The data provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides health management and preventive measures based on the analysis results. Each element of the data collection unit, analysis unit, and data provision unit may also be implemented in the control unit 46A of the robot 414, for example. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) A collection unit that collects user health data, An analysis unit analyzes the health data collected by the aforementioned collection unit, A provision unit that provides health management and preventive measures based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect real-time health data from IoT devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyzing health data using machine learning The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We propose an intervention plan tailored to each user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past health data history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is 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 15) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts how health management and preventative measures are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, adjust the level of detail based on the importance of health management and preventive measures. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, different delivery algorithms are applied depending on the category of health management and preventive measures. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of health management and preventative measures provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, Prioritizing the distribution based on the timing of health management and preventive measures collection. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing services, adjust the order of service based on the relationship between health management and preventive measures. The system described in Appendix 1, characterized by the features described herein. (Note 23) Dynamically allocate cloud computing resources to optimize AI processing power. The system described in Appendix 1, characterized by the features described herein. (Note 24) Distributing data processing on the cloud improves processing speed. The system described in Appendix 1, characterized by the features described herein. (Note 25) Strengthen cloud computing security protocols to ensure data safety The system described in Appendix 1, characterized by the features described herein. (Note 26) Sharing cloud computing resources with other AI applications enables efficient resource utilization. The system described in Appendix 1, characterized by the features described herein. (Note 27) By integrating data processing on the cloud with other cloud services, we promote the interoperability of data. The system described in Appendix 1, characterized by the features described herein. (Note 28) Optimize cloud computing resources by region to efficiently process region-specific health data. The system described in Appendix 1, characterized by the features described herein. (Note 29) Improving the sensor accuracy of wearable devices enhances the reliability of the health data they collect. The system described in Appendix 1, characterized by the features described herein. (Note 30) Extends the battery life of wearable devices and enables long-term data collection. The system described in Appendix 1, characterized by the features described herein. (Note 31) To improve the fit of wearable devices and enhance user comfort. The system described in Appendix 1, characterized by the features described herein. (Note 32) The wearable device will be linked with other health management devices to collect comprehensive health data. The system described in Appendix 1, characterized by the features described herein. (Note 33) Diversifying the design of wearable devices and providing options that suit users' lifestyles. The system described in Appendix 1, characterized by the features described herein. (Note 34) Data from wearable devices is sent to the cloud in real time, enabling immediate health management. The system described in Appendix 1, characterized by the features described herein. (Note 35) During data management, the system estimates the user's emotions and adjusts data access permissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) During data management, the encryption level of the data is dynamically changed to ensure data security. The system described in Appendix 1, characterized by the features described herein. (Note 37) When managing data, the scope of data use will be limited after obtaining the user's consent. The system described in Appendix 1, characterized by the features described herein. (Note 38) During data management, the system estimates user sentiment and adjusts the data retention period based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) During data management, distribute data backups across multiple locations to reduce the risk of data loss. The system described in Appendix 1, characterized by the features described herein. (Note 40) During data management, we regularly review user privacy policies to ensure compliance with the latest laws and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 41) During data management, the system estimates user sentiment and adjusts the data retention period based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 42) During data management, distribute data backups across multiple locations to reduce the risk of data loss. The system described in Appendix 1, characterized by the features described herein. (Note 43) During data management, we regularly review user privacy policies to ensure compliance with the latest laws and regulations. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 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 user health data, An analysis unit analyzes the health data collected by the aforementioned collection unit, A provision unit that provides health management and preventive measures based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect real-time health data from IoT devices. The system according to feature 1.

3. The aforementioned analysis unit is Analyzing health data using machine learning The system according to feature 1.

4. The aforementioned supply unit is, We propose an intervention plan tailored to each user's health condition. The system according to feature 1.

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

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

7. The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and activity level. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system according to feature 1.

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