system

Abstract

The system according to this embodiment aims to predict an individual's health risks and propose appropriate preventive measures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a prediction unit, a proposal unit, an adjustment unit, and a management unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts health risks based on the data obtained by the analysis unit. The proposal unit proposes preventive measures based on the health risks predicted by the prediction unit. The adjustment unit adjusts telemedicine based on the preventive measures proposed by the proposal unit. The management unit securely manages the data collected by the collection 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for an individual to grasp their own future health risks and appropriate preventive measures could not be taken.

[0005] The system according to the embodiment aims to predict an individual's health risk and propose appropriate preventive measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a proposal unit, an adjustment unit, and a management unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The prediction unit predicts health risks based on the data obtained by the analysis unit. The proposal unit proposes preventive measures based on the health risks predicted by the prediction unit. The adjustment unit adjusts telemedicine based on the preventive measures proposed by the proposal unit. The management unit securely manages the data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict an individual's health risks and suggest appropriate preventive measures. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that uses AI to grasp an individual's health status in a 360-degree manner, predict future health risks, and propose personalized preventive measures. This health management system collects, integrates, and analyzes genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. Next, it uses a predictive model to predict future health risks and proposes personalized preventive measures. Furthermore, it collaborates with medical professionals and arranges telemedicine as needed. It also uses blockchain technology to securely manage individual health data and provides users with the option to provide their data for research purposes. This enables the simultaneous realization of individual health management and the advancement of medical research. For example, if genetic information reveals a high risk of a particular disease, it monitors daily exercise levels and heart rate from wearable device data and issues alerts if abnormalities are detected. It refers to past medical history from electronic health records and suggests necessary tests and treatments. It identifies areas for improvement in the living environment from environmental sensor information and provides advice to reduce health risks. Next, it uses a predictive model to predict future health risks. For example, by combining genetic information and daily health data, it is possible to predict the risk of developing future diseases and take preventive measures early. The system proposes personalized preventative measures and supports users in taking action to improve their health. Furthermore, it collaborates with medical professionals and arranges telemedicine consultations as needed. For example, if a user is identified as being at high health risk, they can consult with a medical professional and receive appropriate treatment. Telemedicine allows users to access high-quality medical services regardless of their location. Blockchain technology is also used to securely manage personal health data. Users have the option to provide their data for research purposes, thereby contributing to the advancement of medical research. For example, by cooperating in research on specific diseases, they can contribute to the development of new treatments. In this way, the AI-powered health management system allows individuals to understand their health risks and take preventative measures. The goal is to create a society where people have a personal AI doctor agent and can take action to improve their health anytime, anywhere, regardless of their location.This allows the health management system to gain a 360-degree view of an individual's health status, predict future health risks, and propose personalized preventive measures.

[0029] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a proposal unit, an adjustment unit, and a management unit. The data collection unit collects data. The data collection unit can collect, for example, genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. For example, the data collection unit collects genetic information as DNA sequence data. The data collection unit can also collect real-time data such as heart rate and step count from wearable devices. Furthermore, the data collection unit can also collect medical records and prescription information from electronic health records. The data collection unit can also collect temperature, humidity, and air quality data as environmental sensor information. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit integrates and analyzes the collected data to understand the individual's health status. For example, the analysis unit preprocesses the data to improve data quality. The analysis unit can also analyze the data using machine learning algorithms to evaluate the health status. Furthermore, the analysis unit can visualize the data to visually understand the health status. The prediction unit predicts health risks based on the data obtained by the analysis unit. The prediction unit predicts future health risks, for example, using statistical models. The prediction unit can also predict health risks, for example, using machine learning models. The prediction unit can also predict the risk of developing a disease, for example, by combining genetic information and daily health data. The suggestion unit proposes preventive measures based on the health risks predicted by the prediction unit. The suggestion unit proposes personalized preventive measures, for example. The suggestion unit can propose preventive measures such as exercise recommendations or dietary improvements. The suggestion unit can also provide personalized feedback to support users in implementing health measures. The coordination unit coordinates telemedicine based on the preventive measures proposed by the suggestion unit. The coordination unit coordinates telemedicine, for example, by collaborating with medical professionals. The coordination unit can coordinate video consultations or online consultations, for example. The coordination unit can also coordinate doctor diagnoses and nurse follow-ups, for example. The management unit securely manages the data collected by the collection unit. The management unit manages the data using blockchain technology, for example.The management department can, for example, encrypt the data to ensure its security. The management department can also, for example, maintain data integrity using distributed ledger technology. As a result, the health management system according to this embodiment can gain a 360-degree understanding of an individual's health status, predict future health risks, and propose personalized preventive measures.

[0030] The data collection unit collects data. For example, it can collect genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. Specifically, genetic information is collected as DNA sequencing data, allowing for an understanding of an individual's genetic background and potential health risks. Real-time data such as heart rate, steps, sleep patterns, body temperature, and blood pressure are collected from wearable devices, enabling detailed monitoring of daily health status and activity levels. Electronic health records collect medical records, prescription information, and test results, allowing for an understanding of past medical history and current treatment status. Environmental sensor information such as temperature, humidity, and air quality data is collected, allowing for an evaluation of the impact of the living environment on health. The data collection unit centrally collects this diverse data and transmits it to a central database in real time. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the data collection frequency and accuracy. For example, if an abnormality is detected in a specific health indicator, the collection frequency can be increased to collect more detailed data. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department integrates and analyzes the collected data to understand an individual's health status. Specifically, it performs data preprocessing to improve data quality. Preprocessing includes imputing missing values, detecting and correcting outliers, and normalizing the data. This creates a high-quality dataset suitable for analysis. Next, it analyzes the data using machine learning algorithms to evaluate health status. For example, it uses clustering techniques to identify patterns in health status and detect abnormal patterns. It also uses regression analysis to predict fluctuations in health indicators and assess future health risks. Furthermore, it visualizes the data to visually understand health status. Visualization uses graphs and charts to allow for an intuitive understanding of data trends and anomalies. This enables the analysis department to quickly and accurately analyze the collected data and gain a comprehensive understanding of an individual's health status. In addition, the analysis department can utilize historical data and statistical information to analyze long-term health trends and predict future health risks. This allows the analysis department to not only understand health status in real time but also to support long-term health management, improving the reliability and usefulness of the entire system.

[0032] The prediction unit predicts health risks based on data obtained by the analysis unit. For example, the prediction unit uses statistical models to predict future health risks. Specifically, it uses regression analysis and time series analysis to predict future fluctuations in health indicators. It can also predict health risks using machine learning models. For example, it can use deep learning to analyze complex health data and predict the risk of disease onset with high accuracy. Furthermore, it can combine genetic information with daily health data to predict the risk of disease onset. For example, it can integrate genetic risk factors with lifestyle data to assess the risk of developing specific diseases. This allows the prediction unit to predict individual health risks with high accuracy and provide information for early intervention. Moreover, the prediction unit can continuously revise prediction results based on real-time updated data to adapt to the latest situations. For example, if health indicators or environmental data change rapidly, the prediction unit immediately incorporates the new data and updates the prediction results. The prediction unit can also perform more accurate risk assessments by considering regional characteristics and historical health data. This enables the prediction unit to always provide highly accurate health risk predictions based on the latest information, supporting quick and appropriate responses.

[0033] The proposal department proposes preventive measures based on the health risks predicted by the prediction department. For example, the proposal department proposes personalized preventive measures. Specifically, it proposes preventive measures such as exercise recommendations and dietary improvements tailored to the individual's health condition and lifestyle. For example, it proposes appropriate exercise volume and type based on heart rate and step count data. It also proposes a nutritionally balanced meal plan based on dietary data. Furthermore, the proposal department provides personalized feedback to support users in implementing health measures. For example, it provides real-time feedback on the progress towards health goals and offers advice to maintain motivation. The proposal department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to provide users with appropriate preventive measures and minimize health risks. In addition, the proposal department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also email, SMS, and voice calls. This allows the proposal department to provide users with preventive measures quickly and reliably and minimize health risks.

[0034] The Coordination Department coordinates telemedicine based on the preventative measures proposed by the Proposal Department. For example, the Coordination Department collaborates with medical professionals to coordinate telemedicine. Specifically, it can coordinate video consultations and online consultations. For instance, if a user is suspected of having a health risk, the Coordination Department schedules a video call with a doctor and prepares the environment for receiving treatment. Users can also receive advice from doctors and nurses through online consultations. Furthermore, the Coordination Department can coordinate doctor diagnoses and nurse follow-ups. For example, based on the diagnosis, it schedules necessary follow-up consultations and tests to ensure users receive appropriate medical services. This allows the Coordination Department to provide users with prompt and appropriate medical services, minimizing health risks. Additionally, the Coordination Department can collect user feedback and continuously improve the quality of telemedicine. For example, it can gather user satisfaction and areas for improvement through post-consultation surveys to enhance services. This allows the Coordination Department to provide users with high-quality telemedicine and minimize health risks.

[0035] The management department securely manages the data collected by the collection department. For example, the management department uses blockchain technology to manage data. Specifically, it utilizes blockchain technology to prevent data tampering and ensure traceability. This improves data integrity and reliability. The management department can also encrypt data to ensure data security. For example, collected data is protected using encryption algorithms to prevent unauthorized access and data leaks. Furthermore, the management department can maintain data integrity using distributed ledger technology. This improves data consistency and availability, enhancing the overall reliability of the system. It is also important for the management department to develop data backup and recovery plans to prepare for data loss or corruption. This allows for rapid data recovery in the event of a failure, ensuring the continuous operation of the system. Furthermore, the management department strictly manages data access rights, ensuring that only necessary users can access the data. This maintains data confidentiality and ensures privacy. This allows the management department to manage collected data safely and efficiently, improving the overall reliability and security of the system.

[0036] The data collection unit collects genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. For example, the data collection unit collects genetic information as DNA sequence data. For example, the data collection unit can collect real-time data such as heart rate and steps from wearable devices. For example, the data collection unit can also collect medical records and prescription information from electronic health records. For example, the data collection unit can collect temperature, humidity, and air quality data as environmental sensor information. This allows for a comprehensive understanding of an individual's health status by collecting information from diverse data sources. 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 real-time data acquired from wearable devices into a generating AI and have the generating AI perform data collection and analysis.

[0037] The analysis unit integrates and analyzes the data collected by the collection unit to understand an individual's health status. The analysis unit may, for example, integrate and analyze the collected data to understand an individual's health status. The analysis unit may, for example, preprocess the data to improve its quality. The analysis unit may also, for example, use machine learning algorithms to analyze the data and evaluate the health status. The analysis unit may, for example, visualize the data to visually understand the health status. This allows for an accurate understanding of an individual's health status by integrating and analyzing the collected 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 may input the collected data into a generating AI and have the generating AI perform data integration and analysis.

[0038] The prediction unit predicts future health risks based on data obtained by the analysis unit. The prediction unit can predict future health risks using, for example, a statistical model. The prediction unit can also predict health risks using, for example, a machine learning model. The prediction unit can also predict the risk of disease onset by combining, for example, genetic information and daily health data. This allows for early preventive measures to be taken by predicting future health risks. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input data obtained by the analysis unit into a generative AI and have the generative AI perform the prediction of future health risks.

[0039] The suggestion unit proposes personalized preventive measures based on the health risks predicted by the prediction unit. The suggestion unit may, for example, propose personalized preventive measures such as exercise recommendations or dietary improvements. The suggestion unit may also, for example, provide personalized feedback to support the user in taking action on health measures. This makes it easier for the user to take action on health measures by proposing personalized preventive measures. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit may input the health risks predicted by the prediction unit into a generating AI and have the generating AI execute the proposal of personalized preventive measures.

[0040] The coordination unit coordinates telemedicine in collaboration with medical professionals based on the preventive measures proposed by the proposal unit. For example, the coordination unit can coordinate telemedicine in collaboration with medical professionals. The coordination unit can coordinate, for example, video consultations or online consultations. The coordination unit can also coordinate, for example, physician diagnoses or nurse follow-ups. In this way, high-quality medical services can be provided by coordinating telemedicine in collaboration with medical professionals. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can input the preventive measures proposed by the proposal unit into a generating AI and have the generating AI perform the coordination of telemedicine.

[0041] The management department securely manages the data collected by the collection department using blockchain technology. The management department manages the data using blockchain technology, for example. The management department can ensure data security by, for example, encrypting the data. The management department can also maintain data integrity using, for example, distributed ledger technology. This allows for the secure management of personal health data using blockchain technology. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the collected data into a generating AI and have the generating AI perform data management.

[0042] The management department can provide users with the option to provide their data for research purposes. For example, the management department can provide users with the option to provide their data for research purposes. For example, the management department can anonymize the data to protect user privacy. For example, the management department can establish a data provision consent process so that users can provide data with confidence. This allows users to contribute to the advancement of medical research by providing their data for research purposes. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user data into a generating AI and have the generating AI perform data anonymization and management of provision.

[0043] The data collection unit analyzes the user's past health data and selects the optimal data collection method. For example, the data collection unit can select the most effective data collection method from the user's past health data. For example, the data collection unit can analyze the user's past health data and adjust the frequency of data collection. For example, the data collection unit can select a data collection method that focuses on specific health indicators based on the user's past health data. This allows the optimal data collection method to be selected by analyzing the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0044] The data collection unit filters data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit prioritizes collecting highly relevant data based on the user's current lifestyle. For example, the data collection unit can collect data that focuses on specific health indicators based on the user's areas of interest. The data collection unit can also adjust the scope of data collection according to the user's lifestyle and areas of interest. This allows for the collection of highly relevant data by filtering the data based on the user's lifestyle and areas of interest. 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 areas of interest into a generating AI and have the generating AI perform data filtering.

[0045] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location information during data collection. For example, the data collection unit collects data related to region-specific health risks based on the user's geographical location information. For example, the data collection unit can prioritize the collection of environmental sensor information, taking into account the user's geographical location information. For example, the data collection unit can also collect data that focuses on specific health indicators, depending on the user's geographical location information. This allows for the collection of data related to region-specific health risks by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0046] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit can identify health concerns from the user's social media activity and collect relevant data. For example, the data collection unit can analyze the user's social media activity and prioritize the collection of data related to health risks. For example, the data collection unit can collect data that focuses on specific health indicators based on the user's social media activity. This allows for the collection of data related to health concerns by analyzing the 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 and have the generating AI perform the collection of relevant data.

[0047] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. The analysis unit can also prioritize analyses based on the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0048] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a gene analysis algorithm to genetic information. For example, the analysis unit can apply a motion analysis algorithm to data from wearable devices. For example, the analysis unit can apply an environmental risk analysis algorithm to environmental sensor information. By applying different analysis algorithms depending on the data category, the analysis unit can perform the most optimal analysis for each category. 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 the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0049] The analysis unit determines the priority of analysis based on the data collection period during the analysis. For example, the analysis unit prioritizes analysis on the most recent data. For example, the analysis unit can perform analysis on historical data as needed. The analysis unit can also adjust the priority of analysis according to the data collection period. This allows for priority analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0050] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0051] The prediction unit improves the accuracy of predictions by considering the interrelationships between data during prediction. For example, the prediction unit can make predictions by considering the interrelationships between genetic information and data from wearable devices. For example, the prediction unit can make predictions by considering the interrelationships between electronic health records and environmental sensor information. For example, the prediction unit can also make comprehensive predictions by considering the interrelationships of all data. This improves the accuracy of predictions by considering the interrelationships of data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the interrelationships of data into a generative AI and have the generative AI perform the improvement of prediction accuracy.

[0052] The prediction unit makes predictions while considering the attribute information of the data submitter. For example, the prediction unit may consider the age of the data submitter. For example, the prediction unit may consider the gender of the data submitter. For example, the prediction unit may also consider the lifestyle habits of the data submitter. This allows for more personalized predictions by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit may input the attribute information of the data submitter into a generative AI and have the generative AI perform the prediction.

[0053] The prediction unit makes predictions while considering the geographical distribution of the data. For example, the prediction unit predicts region-specific health risks based on the geographical distribution of the data. For example, the prediction unit can make predictions that reflect environmental factors by considering the geographical distribution of the data. For example, the prediction unit can also improve the accuracy of predictions according to the geographical distribution of the data. This makes it possible to predict region-specific health risks by considering the geographical distribution of the data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the geographical distribution of the data into a generative AI and have the generative AI perform the prediction.

[0054] The prediction unit improves the accuracy of its predictions by referring to relevant literature on the data during the prediction process. For example, the prediction unit reinforces its prediction model by referring to relevant literature on the data. For example, the prediction unit can improve the accuracy of its predictions based on relevant literature on the data. The prediction unit can also verify the prediction results by referring to relevant literature on the data. In this way, the accuracy of predictions can be improved by referring to relevant literature on the data. Some or all of the above processes in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input relevant literature on the data into a generative AI and have the generative AI perform the improvement of prediction accuracy.

[0055] The proposal unit adjusts the level of detail of its proposals based on the importance of the predicted health risks. For example, the proposal unit can provide detailed proposals for high-importance health risks, and simplified proposals for low-importance health risks. The proposal unit can also prioritize proposals based on the importance of the health risks. This allows for detailed proposals for important risks by adjusting the level of detail of the proposals based on the importance of the predicted health risks. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance of the health risks into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0056] The proposal unit applies different proposal algorithms depending on the health risk category when making a proposal. For example, the proposal unit can apply a cardiovascular disease prevention algorithm to cardiovascular risk. For example, it can apply a diabetes prevention algorithm to diabetes risk. For example, it can apply a cancer prevention algorithm to cancer risk. By applying different proposal algorithms depending on the health risk category, the proposal unit can make optimal proposals for each category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input health risk categories into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0057] The proposal unit determines the priority of proposals based on the timing of the occurrence of health risks. For example, the proposal unit prioritizes proposals for health risks that will occur in the near future. For example, the proposal unit can make proposals as needed for health risks that will occur in the distant future. The proposal unit can also adjust the priority of proposals according to the timing of the occurrence of health risks. This allows for prioritizing proposals for risks that will occur in the near future by determining the priority of proposals based on the timing of the occurrence of health risks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of the occurrence of health risks into a generating AI and have the generating AI determine the priority of proposals.

[0058] The proposal unit adjusts the order of proposals based on the relevance of health risks when making proposals. For example, the proposal unit prioritizes proposals for highly relevant health risks. For example, the proposal unit can postpone proposals for less relevant health risks. The proposal unit can also adjust the order of proposals according to the relevance of health risks. This allows for prioritizing proposals for highly relevant risks by adjusting the order of proposals based on the relevance of health risks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of health risks into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0059] The adjustment unit selects the optimal treatment method by referring to the user's past medical history when adjusting telemedicine. For example, the adjustment unit can select the optimal treatment method from the user's past medical history. For example, the adjustment unit can determine the priority of treatment by referring to the user's past medical history. For example, the adjustment unit can also propose a specific treatment method based on the user's past medical history. In this way, the optimal treatment method can be selected by referring to the user's past medical history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's past medical history into a generating AI and have the generating AI perform the selection of the optimal treatment method.

[0060] The adjustment unit customizes the means of treatment based on the user's current health condition when coordinating telemedicine. For example, the adjustment unit can customize the means of treatment based on the user's current health condition. For example, the adjustment unit can determine the priority of treatment by taking into account the user's current health condition. For example, the adjustment unit can also adjust the level of detail of treatment according to the user's current health condition. This allows for the provision of appropriate treatment by customizing the means of treatment based on the user's current health condition. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's current health condition into a generating AI and have the generating AI perform the customization of the means of treatment.

[0061] The adjustment unit selects the optimal treatment method when coordinating telemedicine, taking into account the user's geographical location information. For example, the adjustment unit selects a treatment method that addresses region-specific health risks based on the user's geographical location information. For example, the adjustment unit can propose the optimal treatment method, taking into account the user's geographical location information. For example, the adjustment unit can also adjust the priority of treatment according to the user's geographical location information. This allows for the selection of a treatment method that addresses region-specific health risks by taking into account the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal treatment method.

[0062] The coordination unit analyzes the user's social media activity and proposes treatment options when coordinating telemedicine. For example, the coordination unit can identify health concerns from the user's social media activity and propose relevant treatment options. For example, the coordination unit can analyze the user's social media activity and propose treatment options related to health risks. For example, the coordination unit can propose specific treatment options based on the user's social media activity. In this way, by analyzing the user's social media activity, treatment options related to health concerns can be proposed. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of treatment options.

[0063] The management department selects the optimal management method by referring to past data management history when managing data. For example, the management department can select the optimal data management method from past data management history. For example, the management department can determine the priority of data management by referring to past data management history. For example, the management department can also propose a specific data management method based on past data management history. In this way, the optimal data management method can be selected by referring to past data management history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past data management history into a generating AI and have the generating AI perform the selection of the optimal management method.

[0064] The management unit customizes the means of management based on the user's current data usage when managing data. For example, the management unit can customize the means of management based on the user's current data usage. For example, the management unit can determine management priorities considering the user's current data usage. For example, the management unit can also adjust the level of detail of management according to the user's current data usage. This allows for appropriate data management by customizing the means of management based on the user's current data usage. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's current data usage into a generating AI and have the generating AI perform the customization of the management means.

[0065] The management department selects the optimal management method when managing data, taking into account the user's geographical location information. For example, the management department selects a region-specific data management method based on the user's geographical location information. For example, the management department can propose the optimal data management means, taking into account the user's geographical location information. For example, the management department can also adjust the priority of data management according to the user's geographical location information. This allows for the selection of a region-specific data management method by taking into account the user's geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0066] The management department analyzes users' social media activity and proposes management methods when managing data. For example, the management department identifies concerns regarding data management from users' social media activity and proposes relevant management methods. For example, the management department can analyze users' social media activity and propose methods related to data management. For example, the management department can propose specific data management methods based on users' social media activity. In this way, by analyzing users' social media activity, management methods related to concerns regarding data management can be proposed. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input user social media activity data into a generating AI and have the generating AI execute the proposal of management methods.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The data collection unit analyzes the user's past health data and selects the optimal data collection method. For example, the data collection unit can select the most effective data collection method from the user's past health data. For example, the data collection unit can analyze the user's past health data and adjust the frequency of data collection. For example, the data collection unit can select a data collection method that focuses on specific health indicators based on the user's past health data. This allows the optimal data collection method to be selected by analyzing the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0069] The data collection unit filters data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit prioritizes collecting highly relevant data based on the user's current lifestyle. For example, the data collection unit can collect data that focuses on specific health indicators based on the user's areas of interest. The data collection unit can also adjust the scope of data collection according to the user's lifestyle and areas of interest. This allows for the collection of highly relevant data by filtering the data based on the user's lifestyle and areas of interest. 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 areas of interest into a generating AI and have the generating AI perform data filtering.

[0070] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location information during data collection. For example, the data collection unit collects data related to region-specific health risks based on the user's geographical location information. For example, the data collection unit can prioritize the collection of environmental sensor information, taking into account the user's geographical location information. For example, the data collection unit can also collect data that focuses on specific health indicators, depending on the user's geographical location information. This allows for the collection of data related to region-specific health risks by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0071] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit can identify health concerns from the user's social media activity and collect relevant data. For example, the data collection unit can analyze the user's social media activity and prioritize the collection of data related to health risks. For example, the data collection unit can collect data that focuses on specific health indicators based on the user's social media activity. This allows for the collection of data related to health concerns by analyzing the 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 and have the generating AI perform the collection of relevant data.

[0072] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. The analysis unit can also prioritize analyses based on the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0073] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a gene analysis algorithm to genetic information. For example, the analysis unit can apply a motion analysis algorithm to data from wearable devices. For example, the analysis unit can apply an environmental risk analysis algorithm to environmental sensor information. By applying different analysis algorithms depending on the data category, the analysis unit can perform the most optimal analysis for each category. 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 the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The data collection unit collects data. The data collection unit can collect, for example, genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. For example, the data collection unit can collect genetic information as DNA sequence data. The data collection unit can also collect real-time data such as heart rate and steps from wearable devices. Furthermore, the data collection unit can collect medical records and prescription information from electronic health records. The data collection unit can also collect temperature, humidity, and air quality data as environmental sensor information. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit integrates and analyzes the collected data to understand an individual's health status. For example, the analysis unit preprocesses the data to improve its quality. The analysis unit can also use machine learning algorithms to analyze the data and evaluate the health status. Furthermore, the analysis unit can visualize the data to provide a visual understanding of the health status. Step 3: The prediction unit predicts health risks based on the data obtained by the analysis unit. The prediction unit can predict future health risks, for example, using a statistical model. The prediction unit can also predict health risks, for example, using a machine learning model. The prediction unit can also predict the risk of developing a disease by combining genetic information with daily health data, for example. Step 4: The suggestion unit proposes preventive measures based on the health risks predicted by the prediction unit. The suggestion unit proposes, for example, personalized preventive measures. The suggestion unit may propose preventive measures such as exercise recommendations or dietary improvements. The suggestion unit may also provide, for example, personalized feedback to support the user in taking health measures. Step 5: The Coordination Unit coordinates telemedicine based on the preventive measures proposed by the Proposal Unit. The Coordination Unit coordinates telemedicine, for example, in collaboration with medical professionals. The Coordination Unit can coordinate, for example, video consultations or online counseling. The Coordination Unit can also coordinate, for example, physician diagnoses or nurse follow-ups. Step 6: The management department securely manages the data collected by the collection department. The management department can manage the data using, for example, blockchain technology. The management department can ensure data security by, for example, encrypting the data. The management department can also maintain data integrity using, for example, distributed ledger technology.

[0076] (Example of form 2) The health management system according to an embodiment of the present invention is a system that uses AI to grasp an individual's health status in a 360-degree manner, predict future health risks, and propose personalized preventive measures. This health management system collects, integrates, and analyzes genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. Next, it uses a predictive model to predict future health risks and proposes personalized preventive measures. Furthermore, it collaborates with medical professionals and arranges telemedicine as needed. It also uses blockchain technology to securely manage individual health data and provides users with the option to provide their data for research purposes. This enables the simultaneous realization of individual health management and the advancement of medical research. For example, if genetic information reveals a high risk of a particular disease, it monitors daily exercise levels and heart rate from wearable device data and issues alerts if abnormalities are detected. It refers to past medical history from electronic health records and suggests necessary tests and treatments. It identifies areas for improvement in the living environment from environmental sensor information and provides advice to reduce health risks. Next, it uses a predictive model to predict future health risks. For example, by combining genetic information and daily health data, it is possible to predict the risk of developing future diseases and take preventive measures early. The system proposes personalized preventative measures and supports users in taking action to improve their health. Furthermore, it collaborates with medical professionals and arranges telemedicine consultations as needed. For example, if a user is identified as being at high health risk, they can consult with a medical professional and receive appropriate treatment. Telemedicine allows users to access high-quality medical services regardless of their location. Blockchain technology is also used to securely manage personal health data. Users have the option to provide their data for research purposes, thereby contributing to the advancement of medical research. For example, by cooperating in research on specific diseases, they can contribute to the development of new treatments. In this way, the AI-powered health management system allows individuals to understand their health risks and take preventative measures. The goal is to create a society where people have a personal AI doctor agent and can take action to improve their health anytime, anywhere, regardless of their location.This allows the health management system to gain a 360-degree view of an individual's health status, predict future health risks, and propose personalized preventive measures.

[0077] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a proposal unit, an adjustment unit, and a management unit. The data collection unit collects data. The data collection unit can collect, for example, genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. For example, the data collection unit collects genetic information as DNA sequence data. The data collection unit can also collect real-time data such as heart rate and step count from wearable devices. Furthermore, the data collection unit can also collect medical records and prescription information from electronic health records. The data collection unit can also collect temperature, humidity, and air quality data as environmental sensor information. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit integrates and analyzes the collected data to understand the individual's health status. For example, the analysis unit preprocesses the data to improve data quality. The analysis unit can also analyze the data using machine learning algorithms to evaluate the health status. Furthermore, the analysis unit can visualize the data to visually understand the health status. The prediction unit predicts health risks based on the data obtained by the analysis unit. The prediction unit predicts future health risks, for example, using statistical models. The prediction unit can also predict health risks, for example, using machine learning models. The prediction unit can also predict the risk of developing a disease, for example, by combining genetic information and daily health data. The suggestion unit proposes preventive measures based on the health risks predicted by the prediction unit. The suggestion unit proposes personalized preventive measures, for example. The suggestion unit can propose preventive measures such as exercise recommendations or dietary improvements. The suggestion unit can also provide personalized feedback to support users in implementing health measures. The coordination unit coordinates telemedicine based on the preventive measures proposed by the suggestion unit. The coordination unit coordinates telemedicine, for example, by collaborating with medical professionals. The coordination unit can coordinate video consultations or online consultations, for example. The coordination unit can also coordinate doctor diagnoses and nurse follow-ups, for example. The management unit securely manages the data collected by the collection unit. The management unit manages the data using blockchain technology, for example.The management department can, for example, encrypt the data to ensure its security. The management department can also, for example, maintain data integrity using distributed ledger technology. As a result, the health management system according to this embodiment can gain a 360-degree understanding of an individual's health status, predict future health risks, and propose personalized preventive measures.

[0078] The data collection unit collects data. For example, it can collect genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. Specifically, genetic information is collected as DNA sequencing data, allowing for an understanding of an individual's genetic background and potential health risks. Real-time data such as heart rate, steps, sleep patterns, body temperature, and blood pressure are collected from wearable devices, enabling detailed monitoring of daily health status and activity levels. Electronic health records collect medical records, prescription information, and test results, allowing for an understanding of past medical history and current treatment status. Environmental sensor information such as temperature, humidity, and air quality data is collected, allowing for an evaluation of the impact of the living environment on health. The data collection unit centrally collects this diverse data and transmits it to a central database in real time. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the data collection frequency and accuracy. For example, if an abnormality is detected in a specific health indicator, the collection frequency can be increased to collect more detailed data. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0079] The analysis department analyzes the data collected by the data collection department. For example, the analysis department integrates and analyzes the collected data to understand an individual's health status. Specifically, it performs data preprocessing to improve data quality. Preprocessing includes imputing missing values, detecting and correcting outliers, and normalizing the data. This creates a high-quality dataset suitable for analysis. Next, it analyzes the data using machine learning algorithms to evaluate health status. For example, it uses clustering techniques to identify patterns in health status and detect abnormal patterns. It also uses regression analysis to predict fluctuations in health indicators and assess future health risks. Furthermore, it visualizes the data to visually understand health status. Visualization uses graphs and charts to allow for an intuitive understanding of data trends and anomalies. This enables the analysis department to quickly and accurately analyze the collected data and gain a comprehensive understanding of an individual's health status. In addition, the analysis department can utilize historical data and statistical information to analyze long-term health trends and predict future health risks. This allows the analysis department to not only understand health status in real time but also to support long-term health management, improving the reliability and usefulness of the entire system.

[0080] The prediction unit predicts health risks based on data obtained by the analysis unit. For example, the prediction unit uses statistical models to predict future health risks. Specifically, it uses regression analysis and time series analysis to predict future fluctuations in health indicators. It can also predict health risks using machine learning models. For example, it can use deep learning to analyze complex health data and predict the risk of disease onset with high accuracy. Furthermore, it can combine genetic information with daily health data to predict the risk of disease onset. For example, it can integrate genetic risk factors with lifestyle data to assess the risk of developing specific diseases. This allows the prediction unit to predict individual health risks with high accuracy and provide information for early intervention. Moreover, the prediction unit can continuously revise prediction results based on real-time updated data to adapt to the latest situations. For example, if health indicators or environmental data change rapidly, the prediction unit immediately incorporates the new data and updates the prediction results. The prediction unit can also perform more accurate risk assessments by considering regional characteristics and historical health data. This enables the prediction unit to always provide highly accurate health risk predictions based on the latest information, supporting quick and appropriate responses.

[0081] The proposal department proposes preventive measures based on the health risks predicted by the prediction department. For example, the proposal department proposes personalized preventive measures. Specifically, it proposes preventive measures such as exercise recommendations and dietary improvements tailored to the individual's health condition and lifestyle. For example, it proposes appropriate exercise volume and type based on heart rate and step count data. It also proposes a nutritionally balanced meal plan based on dietary data. Furthermore, the proposal department provides personalized feedback to support users in implementing health measures. For example, it provides real-time feedback on the progress towards health goals and offers advice to maintain motivation. The proposal department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to provide users with appropriate preventive measures and minimize health risks. In addition, the proposal department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also email, SMS, and voice calls. This allows the proposal department to provide users with preventive measures quickly and reliably and minimize health risks.

[0082] The Coordination Department coordinates telemedicine based on the preventative measures proposed by the Proposal Department. For example, the Coordination Department collaborates with medical professionals to coordinate telemedicine. Specifically, it can coordinate video consultations and online consultations. For instance, if a user is suspected of having a health risk, the Coordination Department schedules a video call with a doctor and prepares the environment for receiving treatment. Users can also receive advice from doctors and nurses through online consultations. Furthermore, the Coordination Department can coordinate doctor diagnoses and nurse follow-ups. For example, based on the diagnosis, it schedules necessary follow-up consultations and tests to ensure users receive appropriate medical services. This allows the Coordination Department to provide users with prompt and appropriate medical services, minimizing health risks. Additionally, the Coordination Department can collect user feedback and continuously improve the quality of telemedicine. For example, it can gather user satisfaction and areas for improvement through post-consultation surveys to enhance services. This allows the Coordination Department to provide users with high-quality telemedicine and minimize health risks.

[0083] The management department securely manages the data collected by the collection department. For example, the management department uses blockchain technology to manage data. Specifically, it utilizes blockchain technology to prevent data tampering and ensure traceability. This improves data integrity and reliability. The management department can also encrypt data to ensure data security. For example, collected data is protected using encryption algorithms to prevent unauthorized access and data leaks. Furthermore, the management department can maintain data integrity using distributed ledger technology. This improves data consistency and availability, enhancing the overall reliability of the system. It is also important for the management department to develop data backup and recovery plans to prepare for data loss or corruption. This allows for rapid data recovery in the event of a failure, ensuring the continuous operation of the system. Furthermore, the management department strictly manages data access rights, ensuring that only necessary users can access the data. This maintains data confidentiality and ensures privacy. This allows the management department to manage collected data safely and efficiently, improving the overall reliability and security of the system.

[0084] The data collection unit collects genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. For example, the data collection unit collects genetic information as DNA sequence data. For example, the data collection unit can collect real-time data such as heart rate and steps from wearable devices. For example, the data collection unit can also collect medical records and prescription information from electronic health records. For example, the data collection unit can collect temperature, humidity, and air quality data as environmental sensor information. This allows for a comprehensive understanding of an individual's health status by collecting information from diverse data sources. 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 real-time data acquired from wearable devices into a generating AI and have the generating AI perform data collection and analysis.

[0085] The analysis unit integrates and analyzes the data collected by the collection unit to understand an individual's health status. The analysis unit may, for example, integrate and analyze the collected data to understand an individual's health status. The analysis unit may, for example, preprocess the data to improve its quality. The analysis unit may also, for example, use machine learning algorithms to analyze the data and evaluate the health status. The analysis unit may, for example, visualize the data to visually understand the health status. This allows for an accurate understanding of an individual's health status by integrating and analyzing the collected 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 may input the collected data into a generating AI and have the generating AI perform data integration and analysis.

[0086] The prediction unit predicts future health risks based on data obtained by the analysis unit. The prediction unit can predict future health risks using, for example, a statistical model. The prediction unit can also predict health risks using, for example, a machine learning model. The prediction unit can also predict the risk of disease onset by combining, for example, genetic information and daily health data. This allows for early preventive measures to be taken by predicting future health risks. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input data obtained by the analysis unit into a generative AI and have the generative AI perform the prediction of future health risks.

[0087] The suggestion unit proposes personalized preventive measures based on the health risks predicted by the prediction unit. The suggestion unit may, for example, propose personalized preventive measures such as exercise recommendations or dietary improvements. The suggestion unit may also, for example, provide personalized feedback to support the user in taking action on health measures. This makes it easier for the user to take action on health measures by proposing personalized preventive measures. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit may input the health risks predicted by the prediction unit into a generating AI and have the generating AI execute the proposal of personalized preventive measures.

[0088] The coordination unit coordinates telemedicine in collaboration with medical professionals based on the preventive measures proposed by the proposal unit. For example, the coordination unit can coordinate telemedicine in collaboration with medical professionals. The coordination unit can coordinate, for example, video consultations or online consultations. The coordination unit can also coordinate, for example, physician diagnoses or nurse follow-ups. In this way, high-quality medical services can be provided by coordinating telemedicine in collaboration with medical professionals. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can input the preventive measures proposed by the proposal unit into a generating AI and have the generating AI perform the coordination of telemedicine.

[0089] The management department securely manages the data collected by the collection department using blockchain technology. The management department manages the data using blockchain technology, for example. The management department can ensure data security by, for example, encrypting the data. The management department can also maintain data integrity using, for example, distributed ledger technology. This allows for the secure management of personal health data using blockchain technology. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the collected data into a generating AI and have the generating AI perform data management.

[0090] The management department can provide users with the option to provide their data for research purposes. For example, the management department can provide users with the option to provide their data for research purposes. For example, the management department can anonymize the data to protect user privacy. For example, the management department can establish a data provision consent process so that users can provide data with confidence. This allows users to contribute to the advancement of medical research by providing their data for research purposes. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user data into a generating AI and have the generating AI perform data anonymization and management of provision.

[0091] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit delays the collection timing to collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can advance the collection timing to collect accurate data. For example, if the user is in a hurry, the data collection unit can shorten the collection timing to collect data quickly. By adjusting the data collection timing according to the user's emotions, more accurate data can be collected. 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0092] The data collection unit analyzes the user's past health data and selects the optimal data collection method. For example, the data collection unit can select the most effective data collection method from the user's past health data. For example, the data collection unit can analyze the user's past health data and adjust the frequency of data collection. For example, the data collection unit can select a data collection method that focuses on specific health indicators based on the user's past health data. This allows the optimal data collection method to be selected by analyzing the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0093] The data collection unit filters data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit prioritizes collecting highly relevant data based on the user's current lifestyle. For example, the data collection unit can collect data that focuses on specific health indicators based on the user's areas of interest. The data collection unit can also adjust the scope of data collection according to the user's lifestyle and areas of interest. This allows for the collection of highly relevant data by filtering the data based on the user's lifestyle and areas of interest. 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 areas of interest into a generating AI and have the generating AI perform data filtering.

[0094] The data collection unit estimates the user's emotions and determines the priority of 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. If the user is relaxed, the data collection unit can collect overall health data in a balanced manner. If the user is in a hurry, the data collection unit can also focus on collecting data for key health indicators. This allows for the priority collection of important data by prioritizing data according to 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 and have the generative AI determine the data prioritization.

[0095] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location information during data collection. For example, the data collection unit collects data related to region-specific health risks based on the user's geographical location information. For example, the data collection unit can prioritize the collection of environmental sensor information, taking into account the user's geographical location information. For example, the data collection unit can also collect data that focuses on specific health indicators, depending on the user's geographical location information. This allows for the collection of data related to region-specific health risks by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0096] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit can identify health concerns from the user's social media activity and collect relevant data. For example, the data collection unit can analyze the user's social media activity and prioritize the collection of data related to health risks. For example, the data collection unit can collect data that focuses on specific health indicators based on the user's social media activity. This allows for the collection of data related to health concerns by analyzing the 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 and have the generating AI perform the collection of relevant data.

[0097] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0098] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. The analysis unit can also prioritize analyses based on the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0099] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a gene analysis algorithm to genetic information. For example, the analysis unit can apply a motion analysis algorithm to data from wearable devices. For example, the analysis unit can apply an environmental risk analysis algorithm to environmental sensor information. By applying different analysis algorithms depending on the data category, the analysis unit can perform the most optimal analysis for each category. 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 the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0100] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, if the user is in a hurry, the analysis unit can also provide a brief analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0101] The analysis unit determines the priority of analysis based on the data collection period during the analysis. For example, the analysis unit prioritizes analysis on the most recent data. For example, the analysis unit can perform analysis on historical data as needed. The analysis unit can also adjust the priority of analysis according to the data collection period. This allows for priority analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0102] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0103] The prediction unit estimates the user's emotions and adjusts the prediction criteria based on the estimated emotions. For example, if the user is tense, the prediction unit may apply conservative prediction criteria. For example, if the user is relaxed, the prediction unit may apply standard prediction criteria. For example, if the user is in a hurry, the prediction unit may apply rapid prediction criteria. By adjusting the prediction criteria according to the user's emotions, the system can provide appropriate prediction results for the user. 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 prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the prediction criteria.

[0104] The prediction unit improves the accuracy of predictions by considering the interrelationships between data during prediction. For example, the prediction unit can make predictions by considering the interrelationships between genetic information and data from wearable devices. For example, the prediction unit can make predictions by considering the interrelationships between electronic health records and environmental sensor information. For example, the prediction unit can also make comprehensive predictions by considering the interrelationships of all data. This improves the accuracy of predictions by considering the interrelationships of data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the interrelationships of data into a generative AI and have the generative AI perform the improvement of prediction accuracy.

[0105] The prediction unit makes predictions while considering the attribute information of the data submitter. For example, the prediction unit may consider the age of the data submitter. For example, the prediction unit may consider the gender of the data submitter. For example, the prediction unit may also consider the lifestyle habits of the data submitter. This allows for more personalized predictions by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit may input the attribute information of the data submitter into a generative AI and have the generative AI perform the prediction.

[0106] The prediction unit estimates the user's emotions and adjusts the order in which the prediction results are displayed based on the estimated emotions. For example, if the user is nervous, the prediction unit will display important results first. If the user is relaxed, the prediction unit can display detailed results sequentially. If the user is in a hurry, the prediction unit can also display concise results first. By adjusting the order in which the prediction results are displayed according to the user's emotions, the system can provide prediction results that are easy for the user to understand. 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 prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the prediction results.

[0107] The prediction unit makes predictions while considering the geographical distribution of the data. For example, the prediction unit predicts region-specific health risks based on the geographical distribution of the data. For example, the prediction unit can make predictions that reflect environmental factors by considering the geographical distribution of the data. For example, the prediction unit can also improve the accuracy of predictions according to the geographical distribution of the data. This makes it possible to predict region-specific health risks by considering the geographical distribution of the data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the geographical distribution of the data into a generative AI and have the generative AI perform the prediction.

[0108] The prediction unit improves the accuracy of its predictions by referring to relevant literature on the data during the prediction process. For example, the prediction unit reinforces its prediction model by referring to relevant literature on the data. For example, the prediction unit can improve the accuracy of its predictions based on relevant literature on the data. The prediction unit can also verify the prediction results by referring to relevant literature on the data. In this way, the accuracy of predictions can be improved by referring to relevant literature on the data. Some or all of the above processes in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input relevant literature on the data into a generative AI and have the generative AI perform the improvement of prediction accuracy.

[0109] The suggestion unit estimates the user's emotions and adjusts the way the suggestions are presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit will make simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. If the user is in a hurry, the suggestion unit can also make concise suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are easy for the user to understand. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0110] The proposal unit adjusts the level of detail of its proposals based on the importance of the predicted health risks. For example, the proposal unit can provide detailed proposals for high-importance health risks, and simplified proposals for low-importance health risks. The proposal unit can also prioritize proposals based on the importance of the health risks. This allows for detailed proposals for important risks by adjusting the level of detail of the proposals based on the importance of the predicted health risks. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance of the health risks into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0111] The proposal unit applies different proposal algorithms depending on the health risk category when making a proposal. For example, the proposal unit can apply a cardiovascular disease prevention algorithm to cardiovascular risk. For example, it can apply a diabetes prevention algorithm to diabetes risk. For example, it can apply a cancer prevention algorithm to cancer risk. By applying different proposal algorithms depending on the health risk category, the proposal unit can make optimal proposals for each category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input health risk categories into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0112] The suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide short, concise suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. If the user is in a hurry, for example, the suggestion unit can also provide brief suggestions. By adjusting the length of suggestions according to the user's emotions, the system can provide suggestions of an appropriate length for the user. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0113] The proposal unit determines the priority of proposals based on the timing of the occurrence of health risks. For example, the proposal unit prioritizes proposals for health risks that will occur in the near future. For example, the proposal unit can make proposals as needed for health risks that will occur in the distant future. The proposal unit can also adjust the priority of proposals according to the timing of the occurrence of health risks. This allows for prioritizing proposals for risks that will occur in the near future by determining the priority of proposals based on the timing of the occurrence of health risks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of the occurrence of health risks into a generating AI and have the generating AI determine the priority of proposals.

[0114] The proposal unit adjusts the order of proposals based on the relevance of health risks when making proposals. For example, the proposal unit prioritizes proposals for highly relevant health risks. For example, the proposal unit can postpone proposals for less relevant health risks. The proposal unit can also adjust the order of proposals according to the relevance of health risks. This allows for prioritizing proposals for highly relevant risks by adjusting the order of proposals based on the relevance of health risks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of health risks into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0115] The adjustment unit estimates the user's emotions and modifies the telemedicine adjustment method based on the estimated user emotions. For example, if the user is nervous, the adjustment unit will conduct the telemedicine in a calm voice. For example, if the user is relaxed, the adjustment unit can conduct a telemedicine that includes detailed explanations. For example, if the user is in a hurry, the adjustment unit can conduct a quick and concise telemedicine. In this way, by changing the telemedicine adjustment method according to the user's emotions, appropriate medical care can be provided to the user. 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 adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into the generative AI and have the generative AI perform the modification of the telemedicine adjustment method.

[0116] The adjustment unit selects the optimal treatment method by referring to the user's past medical history when adjusting telemedicine. For example, the adjustment unit can select the optimal treatment method from the user's past medical history. For example, the adjustment unit can determine the priority of treatment by referring to the user's past medical history. For example, the adjustment unit can also propose a specific treatment method based on the user's past medical history. In this way, the optimal treatment method can be selected by referring to the user's past medical history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's past medical history into a generating AI and have the generating AI perform the selection of the optimal treatment method.

[0117] The adjustment unit customizes the means of treatment based on the user's current health condition when coordinating telemedicine. For example, the adjustment unit can customize the means of treatment based on the user's current health condition. For example, the adjustment unit can determine the priority of treatment by taking into account the user's current health condition. For example, the adjustment unit can also adjust the level of detail of treatment according to the user's current health condition. This allows for the provision of appropriate treatment by customizing the means of treatment based on the user's current health condition. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's current health condition into a generating AI and have the generating AI perform the customization of the means of treatment.

[0118] The adjustment unit estimates the user's emotions and determines the priority of telemedicine based on the estimated emotions. For example, if the user is stressed, the adjustment unit will prioritize telemedicine. For example, if the user is relaxed, the adjustment unit can perform telemedicine with the normal priority. For example, if the user is in a hurry, the adjustment unit can also perform telemedicine quickly. This allows for prioritizing highly urgent consultations by determining the priority of telemedicine according to 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 adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine the priority of telemedicine.

[0119] The adjustment unit selects the optimal treatment method when coordinating telemedicine, taking into account the user's geographical location information. For example, the adjustment unit selects a treatment method that addresses region-specific health risks based on the user's geographical location information. For example, the adjustment unit can propose the optimal treatment method, taking into account the user's geographical location information. For example, the adjustment unit can also adjust the priority of treatment according to the user's geographical location information. This allows for the selection of a treatment method that addresses region-specific health risks by taking into account the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal treatment method.

[0120] The coordination unit analyzes the user's social media activity and proposes treatment options when coordinating telemedicine. For example, the coordination unit can identify health concerns from the user's social media activity and propose relevant treatment options. For example, the coordination unit can analyze the user's social media activity and propose treatment options related to health risks. For example, the coordination unit can propose specific treatment options based on the user's social media activity. In this way, by analyzing the user's social media activity, treatment options related to health concerns can be proposed. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of treatment options.

[0121] The management unit estimates the user's emotions and adjusts the data management method based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple and highly visible data management method. For example, if the user is relaxed, the management unit can provide a detailed data management method. For example, if the user is in a hurry, the management unit can provide a quick and concise data management method. In this way, by adjusting the data management method according to the user's emotions, a user-friendly data management method can be provided. 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 management unit may be performed using AI or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the data management method.

[0122] The management department selects the optimal management method by referring to past data management history when managing data. For example, the management department can select the optimal data management method from past data management history. For example, the management department can determine the priority of data management by referring to past data management history. For example, the management department can also propose a specific data management method based on past data management history. In this way, the optimal data management method can be selected by referring to past data management history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past data management history into a generating AI and have the generating AI perform the selection of the optimal management method.

[0123] The management unit customizes the means of management based on the user's current data usage when managing data. For example, the management unit can customize the means of management based on the user's current data usage. For example, the management unit can determine management priorities considering the user's current data usage. For example, the management unit can also adjust the level of detail of management according to the user's current data usage. This allows for appropriate data management by customizing the means of management based on the user's current data usage. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's current data usage into a generating AI and have the generating AI perform the customization of the management means.

[0124] The management unit estimates the user's emotions and determines data management priorities based on the estimated emotions. For example, if the user is stressed, the management unit prioritizes data management. If the user is relaxed, the management unit can manage data with normal priorities. If the user is in a hurry, the management unit can also manage data quickly. This allows for the priority management of urgent data by determining data management priorities according to 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 management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine the data management priorities.

[0125] The management department selects the optimal management method when managing data, taking into account the user's geographical location information. For example, the management department selects a region-specific data management method based on the user's geographical location information. For example, the management department can propose the optimal data management means, taking into account the user's geographical location information. For example, the management department can also adjust the priority of data management according to the user's geographical location information. This allows for the selection of a region-specific data management method by taking into account the user's geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0126] The management department analyzes users' social media activity and proposes management methods when managing data. For example, the management department identifies concerns regarding data management from users' social media activity and proposes relevant management methods. For example, the management department can analyze users' social media activity and propose methods related to data management. For example, the management department can propose specific data management methods based on users' social media activity. In this way, by analyzing users' social media activity, management methods related to concerns regarding data management can be proposed. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input user social media activity data into a generating AI and have the generating AI execute the proposal of management methods.

[0127] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0128] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit delays the collection timing to collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can advance the collection timing to collect accurate data. For example, if the user is in a hurry, the data collection unit can shorten the collection timing to collect data quickly. By adjusting the data collection timing according to the user's emotions, more accurate data can be collected. 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0129] The data collection unit analyzes the user's past health data and selects the optimal data collection method. For example, the data collection unit can select the most effective data collection method from the user's past health data. For example, the data collection unit can analyze the user's past health data and adjust the frequency of data collection. For example, the data collection unit can select a data collection method that focuses on specific health indicators based on the user's past health data. This allows the optimal data collection method to be selected by analyzing the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0130] The data collection unit filters data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit prioritizes collecting highly relevant data based on the user's current lifestyle. For example, the data collection unit can collect data that focuses on specific health indicators based on the user's areas of interest. The data collection unit can also adjust the scope of data collection according to the user's lifestyle and areas of interest. This allows for the collection of highly relevant data by filtering the data based on the user's lifestyle and areas of interest. 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 areas of interest into a generating AI and have the generating AI perform data filtering.

[0131] The data collection unit estimates the user's emotions and determines the priority of 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. If the user is relaxed, the data collection unit can collect overall health data in a balanced manner. If the user is in a hurry, the data collection unit can also focus on collecting data for key health indicators. This allows for the priority collection of important data by prioritizing data according to 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 and have the generative AI determine the data prioritization.

[0132] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location information during data collection. For example, the data collection unit collects data related to region-specific health risks based on the user's geographical location information. For example, the data collection unit can prioritize the collection of environmental sensor information, taking into account the user's geographical location information. For example, the data collection unit can also collect data that focuses on specific health indicators, depending on the user's geographical location information. This allows for the collection of data related to region-specific health risks by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0133] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit can identify health concerns from the user's social media activity and collect relevant data. For example, the data collection unit can analyze the user's social media activity and prioritize the collection of data related to health risks. For example, the data collection unit can collect data that focuses on specific health indicators based on the user's social media activity. This allows for the collection of data related to health concerns by analyzing the 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 and have the generating AI perform the collection of relevant data.

[0134] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0135] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. The analysis unit can also prioritize analyses based on the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0136] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a gene analysis algorithm to genetic information. For example, the analysis unit can apply a motion analysis algorithm to data from wearable devices. For example, the analysis unit can apply an environmental risk analysis algorithm to environmental sensor information. By applying different analysis algorithms depending on the data category, the analysis unit can perform the most optimal analysis for each category. 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 the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0137] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, if the user is in a hurry, the analysis unit can also provide a brief analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0138] The following briefly describes the processing flow for example form 2.

[0139] Step 1: The data collection unit collects data. The data collection unit can collect, for example, genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. For example, the data collection unit can collect genetic information as DNA sequence data. The data collection unit can also collect real-time data such as heart rate and steps from wearable devices. Furthermore, the data collection unit can collect medical records and prescription information from electronic health records. The data collection unit can also collect temperature, humidity, and air quality data as environmental sensor information. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit integrates and analyzes the collected data to understand an individual's health status. For example, the analysis unit preprocesses the data to improve its quality. The analysis unit can also use machine learning algorithms to analyze the data and evaluate the health status. Furthermore, the analysis unit can visualize the data to provide a visual understanding of the health status. Step 3: The prediction unit predicts health risks based on the data obtained by the analysis unit. The prediction unit can predict future health risks, for example, using a statistical model. The prediction unit can also predict health risks, for example, using a machine learning model. The prediction unit can also predict the risk of developing a disease by combining genetic information with daily health data, for example. Step 4: The suggestion unit proposes preventive measures based on the health risks predicted by the prediction unit. The suggestion unit proposes, for example, personalized preventive measures. The suggestion unit may propose preventive measures such as exercise recommendations or dietary improvements. The suggestion unit may also provide, for example, personalized feedback to support the user in taking health measures. Step 5: The Coordination Unit coordinates telemedicine based on the preventive measures proposed by the Proposal Unit. The Coordination Unit coordinates telemedicine, for example, in collaboration with medical professionals. The Coordination Unit can coordinate, for example, video consultations or online counseling. The Coordination Unit can also coordinate, for example, physician diagnoses or nurse follow-ups. Step 6: The management department securely manages the data collected by the collection department. The management department can manage the data using, for example, blockchain technology. The management department can ensure data security by, for example, encrypting the data. The management department can also maintain data integrity using, for example, distributed ledger technology.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, proposal unit, adjustment unit, and management unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data using the sensors and cameras of the smart device 14 and integrates and analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the health status. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts health risks based on the analyzed data. The proposal unit is implemented, for example, by the control unit 46A of the smart device 14 and proposes personalized preventive measures based on the predicted health risks. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adjusts telemedicine based on the proposed preventive measures. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and securely manages the collected data. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In 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.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, proposal unit, adjustment unit, and management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data using the sensors and cameras of the smart glasses 214 and integrates and analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the health status. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts health risks based on the analyzed data. The proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214 and proposes personalized preventive measures based on the predicted health risks. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adjusts telemedicine based on the proposed preventive measures. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and securely manages the collected data. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In 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.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 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.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, proposal unit, adjustment unit, and management unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the sensors and camera of the headset terminal 314 and integrates and analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to evaluate the health status. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and predicts health risks based on the analyzed data. The proposal unit is implemented, for example, by the control unit 46A of the headset terminal 314, and proposes personalized preventive measures based on the predicted health risks. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and adjusts telemedicine based on the proposed preventive measures. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and securely manages the collected data. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0176] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, proposal unit, adjustment unit, and management unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the sensors and cameras of the robot 414 and integrates and analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the health status. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts health risks based on the analyzed data. The proposal unit is implemented, for example, by the control unit 46A of the robot 414 and proposes personalized preventive measures based on the predicted health risks. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adjusts telemedicine based on the proposed preventive measures. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and securely manages the collected data. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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."

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A prediction unit that predicts health risks based on the data obtained by the analysis unit, A proposal unit that proposes preventive measures based on the health risks predicted by the prediction unit, A coordination unit that adjusts telemedicine based on the preventive measures proposed by the aforementioned proposal unit, The system includes a management unit that securely manages the data collected by the aforementioned collection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The data collected by the aforementioned collection unit is integrated and analyzed to understand the individual's health status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Based on the data obtained by the aforementioned analysis unit, future health risks are predicted. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the health risks predicted by the prediction unit, personalized preventive measures are proposed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, Based on the preventive measures proposed by the aforementioned proposal department, we will collaborate with medical professionals and coordinate telemedicine. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, Blockchain technology is used to securely manage the data collected by the aforementioned collection unit. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, Provide users with the option to contribute their own data for research purposes. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, It estimates the user's emotions and adjusts the prediction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, When making predictions, consider the interrelationships between data to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, the attribute information of the data submitters is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, It estimates the user's sentiment and adjusts the order in which the prediction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, When making predictions, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, When making predictions, refer to relevant literature to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the perceived importance of the predicted health risks. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the health risk category. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the health risk is likely to occur. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of health risks. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, The system estimates the user's emotions and modifies the telemedicine adjustment method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, When scheduling a telemedicine consultation, the system selects the most appropriate treatment method by referring to the user's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, When scheduling a telemedicine consultation, the consultation method is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, The system estimates the user's emotions and prioritizes telemedicine based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The adjustment unit is, When scheduling telemedicine consultations, the optimal consultation method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The adjustment unit is, When coordinating telemedicine consultations, we analyze the user's social media activity to suggest appropriate treatment methods. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned management department, We estimate user sentiment and adjust data management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned management department, When managing data, refer to past data management history to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned management department, When managing data, customize the management methods based on the user's current data usage. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned management department, It estimates user sentiment and prioritizes data management based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned management department, When managing data, the optimal management method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned management department, When managing data, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A prediction unit that predicts health risks based on the data obtained by the analysis unit, A proposal unit that proposes preventive measures based on the health risks predicted by the prediction unit, A coordination unit that adjusts telemedicine based on the preventive measures proposed by the aforementioned proposal unit, The system includes a management unit that securely manages the data collected by the aforementioned collection unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects genetic information, real-time data from wearable devices, electronic health records, and environmental sensor information. The system according to feature 1.

3. The aforementioned analysis unit is The data collected by the aforementioned collection unit is integrated and analyzed to understand the individual's health status. The system according to feature 1.

4. The prediction unit, Based on the data obtained by the aforementioned analysis unit, future health risks are predicted. The system according to feature 1.

5. The aforementioned proposal section is, Based on the health risks predicted by the prediction unit, personalized preventive measures are proposed. The system according to feature 1.

6. The adjustment unit is, Based on the preventive measures proposed by the aforementioned proposal department, we will collaborate with medical professionals and coordinate telemedicine. The system according to feature 1.

7. The aforementioned management department, Blockchain technology is used to securely manage the data collected by the aforementioned collection unit. The system according to feature 1.

8. The aforementioned management department, Provide users with the option to contribute their own data for research purposes. The system according to feature 1.

Images