system

Abstract

The system according to this embodiment aims to effectively collect and analyze user health data and provide appropriate advice. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects the user's health data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The monitoring unit continuously monitors the health status based on the advice provided by the provision 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=3⑤]]In the conventional technology, the user's health data has not been sufficiently collected and analyzed effectively, and appropriate advice has not been provided sufficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively collect and analyze the user's health data and provide appropriate advice.

Means for Solving the Problems

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

[0007] The system according to this embodiment can effectively collect and analyze user health data and provide appropriate advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is an innovative service that supports health management at home. This health management system allows users to input health-related questions and symptoms, and a generating AI analyzes the input information to provide appropriate advice and diagnoses. Furthermore, the generating AI continuously monitors the user's health status and updates health management advice as needed. The generating AI also securely manages the user's health data and protects privacy. This allows users to manage their health from the comfort of their homes. Specifically, it includes a collection unit for collecting user health data, an analysis unit where the generating AI performs analysis, a provision unit for providing advice, and a monitoring unit for continuously monitoring the user's health status. This enables the health management system to efficiently collect, analyze, provide advice on, and monitor user health data.

[0029] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a monitoring unit. The data collection unit collects the user's health data. For example, the data collection unit collects health-related questions and symptoms entered by the user. The data collection unit stores the information entered by the user in digital format and transmits it to the analysis unit. The data collection unit can also collect vital signs using sensors. For example, the data collection unit collects data such as heart rate, blood pressure, and body temperature. The data collection unit can also collect the user's dietary records and exercise data. For example, the data collection unit provides an interface for the user to input their dietary information and stores the entered data. The analysis unit analyzes the data collected by the data collection unit using generative AI. The analysis unit analyzes the data based on past medical data and the latest medical knowledge. For example, the analysis unit refers to past medical records and test results to evaluate the user's health status. The analysis unit can also analyze data patterns and detect abnormalities using generative AI. For example, the analysis unit analyzes heart rate fluctuation patterns and detects abnormal fluctuations. The service provider provides advice based on the analysis results obtained by the analysis unit. The service provider provides specific advice and diagnoses to the user. For example, the service provider may suggest dietary improvements or recommend exercise. The service provider can also use generative AI to provide personalized advice tailored to the user's health condition. For example, the service provider may provide a customized health management plan based on the user's health data. The monitoring unit continuously monitors the user's health condition based on the advice provided by the service provider. The monitoring unit periodically collects the user's health data and transmits it to the analysis unit. The monitoring unit can also collect data in real time and issue alerts if it detects an abnormality. For example, the monitoring unit may issue an alert if the heart rate is abnormally high. As a result, the health management system according to this embodiment can efficiently collect, analyze, provide advice on, and monitor the user's health data.

[0030] The data collection unit collects user health data. For example, it collects health-related questions and symptoms entered by the user. Specifically, the data collection unit provides an interface for users to input their daily health status and symptoms, making it easy for users to input information. The data collection unit stores the information entered by the user in digital format and transmits it to the analysis unit. Furthermore, the data collection unit can also collect vital signs using sensors. For example, the data collection unit collects data such as heart rate, blood pressure, and body temperature. Wearable devices and home medical devices are used for this purpose. Wearable devices continuously collect data when worn by the user daily and transmit the data to the data collection unit via Bluetooth® or Wi-Fi. Home medical devices are designed for easy use by users at home and have the function to automatically transmit measurement results to the data collection unit. The data collection unit can also collect user dietary records and exercise data. For example, the data collection unit provides an interface for users to input their dietary information and stores the entered data. Users can input details such as the type, amount, and timing of meals, allowing the data collection unit to understand the user's eating habits in detail. Regarding exercise data, data such as steps taken, exercise time, and calories burned are collected through smartphone apps and wearable devices. This allows the data collection unit to comprehensively understand the user's health status and gather a wealth of data to provide to the analysis unit.

[0031] The analysis unit uses generative AI to analyze data collected by the data collection unit. The analysis unit analyzes data based on past medical data and the latest medical knowledge. Specifically, the analysis unit refers to past medical records and test results to evaluate the user's health status. The generative AI refers to a vast medical database and performs analysis by comparing it with the user's data. For example, the analysis unit analyzes heart rate variability patterns and detects abnormal fluctuations. The generative AI analyzes heart rate data in time series and detects abnormalities if there are fluctuations that exceed the normal range. Similarly, it analyzes blood pressure and body temperature data and issues an alert if abnormal values ​​are detected. Furthermore, the analysis unit analyzes the user's dietary records and exercise data to evaluate their impact on health status. For example, it analyzes the nutritional balance and calorie intake of meals to evaluate whether there is excessive or insufficient intake. For exercise data, it analyzes the amount and frequency of exercise to evaluate whether appropriate exercise is being performed. The generative AI comprehensively analyzes this data to comprehensively evaluate the user's health status. Furthermore, the analysis unit can perform not only anomaly detection but also predictive analysis. For example, it can predict future health risks based on past data and provide information to enable early countermeasures. This allows the analysis unit to build a foundation for detailed analysis of the user's health status and provide appropriate advice and diagnoses.

[0032] The service provider provides advice based on the analysis results obtained by the analysis unit. The service provider provides users with specific advice and diagnoses. For example, the service provider makes suggestions for improving diet and recommends exercise. Specifically, the service provider makes suggestions for improving nutritional balance based on the user's food diary. For example, if a deficiency in vitamins or minerals is detected, it will suggest specific foods and recipes to supplement them. It will also provide an appropriate exercise plan based on exercise data. For example, if a lack of exercise is detected, it will suggest an exercise menu that is easy to incorporate into daily life and encourage its implementation. The service provider can also use generative AI to provide individualized advice tailored to the user's health condition. The generative AI analyzes the user's data and generates a customized health management plan that meets individual needs. For example, for users with a high risk of certain health risks, it will suggest specific measures to mitigate those risks. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can collect the results of users implementing the advice provided and evaluate its effectiveness. This allows the service provider to provide users with optimal advice and support their health management.

[0033] The monitoring unit continuously monitors the user's health status based on the advice provided by the service provider. The monitoring unit periodically collects the user's health data and transmits it to the analysis unit. Specifically, the monitoring unit continuously collects vital signs such as heart rate, blood pressure, and body temperature through wearable devices and home medical equipment. This allows for real-time monitoring of the user's health status. Furthermore, the monitoring unit can issue alerts if it detects an abnormality. For example, if the heart rate is abnormally high or blood pressure rises sharply, the monitoring unit immediately issues an alert to draw the user's attention. This enables early detection of abnormalities and prompt response. The monitoring unit also monitors the implementation of the advice provided by the service provider and evaluates its effectiveness. For example, it verifies whether the user is following the suggested exercise plan and transmits the results to the analysis unit. This allows the monitoring unit to continuously monitor the user's health status and, in collaboration with the service provider and analysis unit, support optimal health management. Additionally, the monitoring unit can collect user feedback and use it to improve the overall system. For example, it provides an interface for users to input opinions and requests regarding the advice, which can be used to improve the system's functions and advice content. This allows the monitoring unit to continuously monitor the user's health status and improve the overall system performance.

[0034] The data collection unit can collect health-related questions and symptoms entered by users. For example, the data collection unit can store the user-entered information digitally and transmit it to the analysis unit. The data collection unit securely manages the user-entered information and protects privacy. The data collection unit encrypts the user-entered information to prevent unauthorized access by third parties. The data collection unit anonymizes the user-entered information to prevent the leakage of personal information. This allows for the collection of health-related questions and symptoms entered by users, thereby obtaining more accurate health data.

[0035] The analysis unit can analyze data based on past medical data and the latest medical knowledge. For example, the analysis unit can refer to past medical records and test results to assess the user's health status. The analysis unit can also use generative AI to analyze data patterns and detect anomalies. For example, the analysis unit can analyze heart rate variability patterns and detect abnormal fluctuations. The analysis unit can also assess the user's health status based on the latest medical knowledge. For example, the analysis unit can refer to the latest research papers and guidelines to assess the user's health status. By analyzing data based on past medical data and the latest medical knowledge, more accurate analysis results can be obtained.

[0036] The service provider can offer specific advice and diagnoses based on the analysis results. For example, it can suggest dietary improvements or recommend exercise. The service provider can also use generative AI to provide personalized advice tailored to the user's health condition. For example, it can provide a customized health management plan based on the user's health data. The service provider can also continuously monitor the user's health condition and update the advice as needed. For example, if the user's health condition improves, the service provider can update the advice and suggest the next steps. In this way, by providing specific advice and diagnoses based on the analysis results, it can support the user's health management.

[0037] The monitoring unit can continuously evaluate the user's health status and update advice as needed. For example, the monitoring unit periodically collects the user's health data and transmits it to the analysis unit. The monitoring unit can also collect data in real time and issue alerts if an anomaly is detected. For example, the monitoring unit will issue an alert if the heart rate is abnormally high. The monitoring unit can continuously evaluate the user's health status and update advice as needed. For example, if the user's health status improves, the monitoring unit will update the advice and suggest the next steps. This allows for the optimization of user health management by continuously evaluating the user's health status and updating advice as needed.

[0038] The data collection unit can securely manage users' health data and protect their privacy. For example, the data collection unit can encrypt users' health data to prevent unauthorized access by third parties. The data collection unit can anonymize users' health data to prevent the leakage of personal information. The data collection unit can securely store users' health data and prevent data loss. For example, the data collection unit can regularly back up data to prevent data loss. The data collection unit can develop and adhere to policies to securely manage users' health data and protect their privacy. By doing so, the data collection unit can gain users' trust by securely managing their health data and protecting their privacy.

[0039] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can identify the most effective time of day for data collection from the user's past data. The data collection unit can also select the optimal data collection method for a specific symptom from the user's past data. The data collection unit can also adjust the data collection frequency from the user's past 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 processes 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.

[0040] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is interested in exercise, the data collection unit will prioritize collecting exercise-related data. If the user is interested in diet, the data collection unit can also prioritize collecting diet-related data. If the user is interested in sleep, the data collection unit can also prioritize collecting sleep-related data. By filtering the data based on the user's lifestyle and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information when collecting health data. For example, if the user is at high altitude, the data collection unit will collect health data specific to high altitude. If the user is in an urban area, the data collection unit can also collect health data specific to urban areas. If the user is at the beach, the data collection unit can also collect health data specific to beach areas. This allows for the collection of more appropriate data by collecting highly relevant data based on 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.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, if the user posts about exercise on social media, the data collection unit can collect exercise-related data. If the user posts about food on social media, the data collection unit can also collect food-related data. If the user posts about sleep on social media, the data collection unit can also collect sleep-related data. In this way, relevant health data can be collected 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 into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. The analysis unit can also perform a simplified analysis on general health data. The analysis unit can also perform a rapid analysis on health data that is of high urgency. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. The analysis unit can also apply a diet analysis algorithm to diet data. The analysis unit can also apply a sleep analysis algorithm to sleep data. By applying different analysis algorithms depending on the category of health data, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit can also analyze current data while referring to past data. The analysis unit can also prioritize the analysis of data with high urgency. This allows for the prioritization of more important data by determining the priority of analysis based on the timing of health data collection. 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 timing of health data collection into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the health data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the health data. 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 relevance of the health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The service provider can adjust the level of detail of advice based on the importance of the health data when providing advice. For example, the service provider can provide detailed advice for important health data. For general health data, it can also provide simplified advice. For urgent health data, it can provide advice quickly. This allows for detailed advice to be provided for important data by adjusting the level of detail of the advice based on the importance of the health data. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0048] The service provider can apply different advice algorithms depending on the category of health data when providing advice. For example, the service provider can apply an exercise advice algorithm to exercise data. The service provider can also apply a diet advice algorithm to diet data. The service provider can also apply a sleep advice algorithm to sleep data. By applying different advice algorithms depending on the category of health data, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the categories of health data into a generating AI and have the generating AI execute the application of different advice algorithms.

[0049] The service provider can prioritize advice based on when health data is collected. For example, it can provide advice based on recently collected data. It can also provide advice based on current data, while referring to past data. It can also provide advice based on data of high urgency. By prioritizing advice based on when health data is collected, it is possible to provide advice based on more important data. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the timing of health data collection into a generating AI and have the generating AI determine the priority of advice.

[0050] The service provider can adjust the order of advice based on the relevance of health data when providing advice. For example, the service provider can provide advice based on highly relevant data. The service provider can also postpone less relevant data. The service provider can also dynamically adjust the order of advice based on the relevance of the data. This allows for more efficient advice delivery by adjusting the order of advice based on the relevance of health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0051] The monitoring unit can analyze the user's past health data during monitoring to select the optimal monitoring method. For example, the monitoring unit can identify the most effective monitoring time period from the user's past data. The monitoring unit can also select the optimal monitoring method for a specific symptom from the user's past data. The monitoring unit can also adjust the monitoring frequency from the user's past data. This allows the optimal monitoring method to be selected by analyzing the user's past health data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past health data into a generating AI and have the generating AI select the optimal monitoring method.

[0052] The monitoring unit can customize the monitoring methods based on the user's current living situation during monitoring. For example, if the user is busy, the monitoring unit can provide simplified monitoring methods. If the user is relaxed, the monitoring unit can also provide detailed monitoring methods. If the user is traveling, the monitoring unit can also provide monitoring methods suitable for travel. This allows for more appropriate monitoring by customizing the monitoring methods based on the user's current living situation. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's current living situation into a generating AI and have the generating AI perform the customization of the monitoring methods.

[0053] The monitoring unit can select the optimal monitoring method based on the user's geographical location information during monitoring. For example, if the user is at high altitude, the monitoring unit will monitor health indicators specific to high altitude. If the user is in an urban area, the monitoring unit can also monitor health indicators specific to urban areas. If the user is at the coast, the monitoring unit can also monitor health indicators specific to coastal areas. By selecting the optimal monitoring method based on the user's geographical location information, more appropriate monitoring can be performed. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal monitoring method.

[0054] The monitoring unit can analyze the user's social media activity during monitoring and propose monitoring methods. For example, if the user posts about exercise on social media, the monitoring unit can propose exercise-related monitoring methods. If the user posts about food on social media, the monitoring unit can also propose meal-related monitoring methods. If the user posts about sleep on social media, the monitoring unit can also propose sleep-related monitoring methods. In this way, relevant monitoring methods can be proposed by analyzing the user's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's social media activity into a generating AI and have the generating AI execute the proposal of monitoring methods.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] A health management system can also include a learning unit that learns the user's lifestyle and provides personalized health management plans. The learning unit analyzes the user's past behavioral and health data to understand their lifestyle. For example, if a user has a habit of exercising at the same time every day, the learning unit can provide an exercise plan tailored to that time. Furthermore, if a user has a specific eating pattern, the learning unit can provide dietary advice based on that pattern. In addition, the learning unit can analyze the user's sleep patterns and suggest the optimal amount of sleep. This allows for more effective health management by providing personalized health management plans based on the user's lifestyle.

[0057] The health management system can also include a social support section that leverages the user's social network to support health management. This social support section collaborates with the user's friends and family to provide health management support. For example, it can suggest exercise plans with friends to help the user maintain their exercise routine. It can also suggest plans for preparing healthy meals with family to help the user improve their diet. Furthermore, if the user is experiencing stress, the social support section can help them request support from friends and family. This leverages the user's social network to support health management, leading to more effective health management.

[0058] The health management system can also include a liaison unit that shares user health data with other medical institutions. The liaison unit shares user health data with medical institutions only after obtaining the user's consent. For example, the liaison unit can send health data to a medical institution the user regularly visits. The liaison unit can also quickly provide health data to a medical institution in case of an emergency. Furthermore, the liaison unit can provide past health data when the user seeks a diagnosis from a specialist. This allows users to receive more appropriate medical services by sharing their health data with other medical institutions.

[0059] A health management system can also include a prevention unit that performs proactive health management based on the user's health data. The prevention unit analyzes the user's health data and predicts future health risks. For example, it can predict the risk of future illnesses based on the user's past health data. It can also analyze the user's lifestyle and provide advice to reduce health risks. Furthermore, the prevention unit can suggest a schedule for regular health checkups based on the user's health data. This allows for proactive health management based on the user's health data, thereby reducing future health risks.

[0060] The health management system can also include a goal-setting unit that sets personalized health goals based on the user's health data. The goal-setting unit analyzes the user's health data and sets individual health goals. For example, it can set appropriate exercise goals based on the user's current health status. It can also set healthy dietary goals based on the user's dietary data. Furthermore, it can set optimal sleep goals based on the user's sleep data. This allows for more effective health management by setting personalized health goals based on the user's health data.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects the user's health data. The data collection unit collects health-related questions and symptoms entered by the user, vital signs (heart rate, blood pressure, body temperature, etc.), dietary records, and exercise data. This data is stored in digital format and transmitted to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to analyze the data based on past medical data and the latest medical knowledge to evaluate the user's health status. The analysis unit can also analyze data patterns and detect anomalies. Step 3: The service provider provides advice based on the analysis results obtained by the analysis unit. The service provider provides users with specific advice and diagnoses, such as suggestions for dietary improvements and recommendations for exercise. The service provider can also use generative AI to provide personalized advice tailored to the user's health condition. Step 4: The monitoring unit continuously monitors the user's health status based on the advice provided by the service provider. The monitoring unit periodically collects the user's health data and transmits it to the analysis unit. The monitoring unit can also collect data in real time and issue alerts if an anomaly is detected.

[0063] (Example of form 2) The health management system according to an embodiment of the present invention is an innovative service that supports health management at home. This health management system allows users to input health-related questions and symptoms, and a generating AI analyzes the input information to provide appropriate advice and diagnoses. Furthermore, the generating AI continuously monitors the user's health status and updates health management advice as needed. The generating AI also securely manages the user's health data and protects privacy. This allows users to manage their health from the comfort of their homes. Specifically, it includes a collection unit for collecting user health data, an analysis unit where the generating AI performs analysis, a provision unit for providing advice, and a monitoring unit for continuously monitoring the user's health status. This enables the health management system to efficiently collect, analyze, provide advice on, and monitor user health data.

[0064] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a monitoring unit. The data collection unit collects the user's health data. For example, the data collection unit collects health-related questions and symptoms entered by the user. The data collection unit stores the information entered by the user in digital format and transmits it to the analysis unit. The data collection unit can also collect vital signs using sensors. For example, the data collection unit collects data such as heart rate, blood pressure, and body temperature. The data collection unit can also collect the user's dietary records and exercise data. For example, the data collection unit provides an interface for the user to input their dietary information and stores the entered data. The analysis unit analyzes the data collected by the data collection unit using generative AI. The analysis unit analyzes the data based on past medical data and the latest medical knowledge. For example, the analysis unit refers to past medical records and test results to evaluate the user's health status. The analysis unit can also analyze data patterns and detect abnormalities using generative AI. For example, the analysis unit analyzes heart rate fluctuation patterns and detects abnormal fluctuations. The service provider provides advice based on the analysis results obtained by the analysis unit. The service provider provides specific advice and diagnoses to the user. For example, the service provider may suggest dietary improvements or recommend exercise. The service provider can also use generative AI to provide personalized advice tailored to the user's health condition. For example, the service provider may provide a customized health management plan based on the user's health data. The monitoring unit continuously monitors the user's health condition based on the advice provided by the service provider. The monitoring unit periodically collects the user's health data and transmits it to the analysis unit. The monitoring unit can also collect data in real time and issue alerts if it detects an abnormality. For example, the monitoring unit may issue an alert if the heart rate is abnormally high. As a result, the health management system according to this embodiment can efficiently collect, analyze, provide advice on, and monitor the user's health data.

[0065] The data collection unit collects user health data. For example, it collects health-related questions and symptoms entered by the user. Specifically, the data collection unit provides an interface for users to input their daily health status and symptoms, making it easy for users to input information. The data collection unit stores the information entered by the user in digital format and transmits it to the analysis unit. Furthermore, the data collection unit can also collect vital signs using sensors. For example, the data collection unit collects data such as heart rate, blood pressure, and body temperature. Wearable devices and home medical devices are used for this purpose. Wearable devices continuously collect data when worn by the user daily and transmit the data to the data collection unit via Bluetooth or Wi-Fi. Home medical devices are designed for easy use by users at home and have the function to automatically transmit measurement results to the data collection unit. The data collection unit can also collect user dietary records and exercise data. For example, the data collection unit provides an interface for users to input their meal details and stores the entered data. Users can input details such as the type, amount, and timing of meals, which allows the data collection unit to understand the user's eating habits in detail. Regarding exercise data, data such as steps taken, exercise time, and calories burned are collected through smartphone apps and wearable devices. This allows the data collection unit to comprehensively understand the user's health status and gather a wealth of data to provide to the analysis unit.

[0066] The analysis unit uses generative AI to analyze data collected by the data collection unit. The analysis unit analyzes data based on past medical data and the latest medical knowledge. Specifically, the analysis unit refers to past medical records and test results to evaluate the user's health status. The generative AI refers to a vast medical database and performs analysis by comparing it with the user's data. For example, the analysis unit analyzes heart rate variability patterns and detects abnormal fluctuations. The generative AI analyzes heart rate data in time series and detects abnormalities if there are fluctuations that exceed the normal range. Similarly, it analyzes blood pressure and body temperature data and issues an alert if abnormal values ​​are detected. Furthermore, the analysis unit analyzes the user's dietary records and exercise data to evaluate their impact on health status. For example, it analyzes the nutritional balance and calorie intake of meals to evaluate whether there is excessive or insufficient intake. For exercise data, it analyzes the amount and frequency of exercise to evaluate whether appropriate exercise is being performed. The generative AI comprehensively analyzes this data to comprehensively evaluate the user's health status. Furthermore, the analysis unit can perform not only anomaly detection but also predictive analysis. For example, it can predict future health risks based on past data and provide information to enable early countermeasures. This allows the analysis unit to build a foundation for detailed analysis of the user's health status and provide appropriate advice and diagnoses.

[0067] The service provider provides advice based on the analysis results obtained by the analysis unit. The service provider provides users with specific advice and diagnoses. For example, the service provider makes suggestions for improving diet and recommends exercise. Specifically, the service provider makes suggestions for improving nutritional balance based on the user's food diary. For example, if a deficiency in vitamins or minerals is detected, it will suggest specific foods and recipes to supplement them. It will also provide an appropriate exercise plan based on exercise data. For example, if a lack of exercise is detected, it will suggest an exercise menu that is easy to incorporate into daily life and encourage its implementation. The service provider can also use generative AI to provide individualized advice tailored to the user's health condition. The generative AI analyzes the user's data and generates a customized health management plan that meets individual needs. For example, for users with a high risk of certain health risks, it will suggest specific measures to mitigate those risks. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can collect the results of users implementing the advice provided and evaluate its effectiveness. This allows the service provider to provide users with optimal advice and support their health management.

[0068] The monitoring unit continuously monitors the user's health status based on the advice provided by the service provider. The monitoring unit periodically collects the user's health data and transmits it to the analysis unit. Specifically, the monitoring unit continuously collects vital signs such as heart rate, blood pressure, and body temperature through wearable devices and home medical equipment. This allows for real-time monitoring of the user's health status. Furthermore, the monitoring unit can issue alerts if it detects an abnormality. For example, if the heart rate is abnormally high or blood pressure rises sharply, the monitoring unit immediately issues an alert to draw the user's attention. This enables early detection of abnormalities and prompt response. The monitoring unit also monitors the implementation of the advice provided by the service provider and evaluates its effectiveness. For example, it verifies whether the user is following the suggested exercise plan and transmits the results to the analysis unit. This allows the monitoring unit to continuously monitor the user's health status and, in collaboration with the service provider and analysis unit, support optimal health management. Additionally, the monitoring unit can collect user feedback and use it to improve the overall system. For example, it provides an interface for users to input opinions and requests regarding the advice, which can be used to improve the system's functions and advice content. This allows the monitoring unit to continuously monitor the user's health status and improve the overall system performance.

[0069] The data collection unit can collect health-related questions and symptoms entered by users. For example, the data collection unit can store the user-entered information digitally and transmit it to the analysis unit. The data collection unit securely manages the user-entered information and protects privacy. The data collection unit encrypts the user-entered information to prevent unauthorized access by third parties. The data collection unit anonymizes the user-entered information to prevent the leakage of personal information. This allows for the collection of health-related questions and symptoms entered by users, thereby obtaining more accurate health data.

[0070] The analysis unit can analyze data based on past medical data and the latest medical knowledge. For example, the analysis unit can refer to past medical records and test results to assess the user's health status. The analysis unit can also use generative AI to analyze data patterns and detect anomalies. For example, the analysis unit can analyze heart rate variability patterns and detect abnormal fluctuations. The analysis unit can also assess the user's health status based on the latest medical knowledge. For example, the analysis unit can refer to the latest research papers and guidelines to assess the user's health status. By analyzing data based on past medical data and the latest medical knowledge, more accurate analysis results can be obtained.

[0071] The service provider can offer specific advice and diagnoses based on the analysis results. For example, it can suggest dietary improvements or recommend exercise. The service provider can also use generative AI to provide personalized advice tailored to the user's health condition. For example, it can provide a customized health management plan based on the user's health data. The service provider can also continuously monitor the user's health condition and update the advice as needed. For example, if the user's health condition improves, the service provider can update the advice and suggest the next steps. In this way, by providing specific advice and diagnoses based on the analysis results, it can support the user's health management.

[0072] The monitoring unit can continuously evaluate the user's health status and update advice as needed. For example, the monitoring unit periodically collects the user's health data and transmits it to the analysis unit. The monitoring unit can also collect data in real time and issue alerts if an anomaly is detected. For example, the monitoring unit will issue an alert if the heart rate is abnormally high. The monitoring unit can continuously evaluate the user's health status and update advice as needed. For example, if the user's health status improves, the monitoring unit will update the advice and suggest the next steps. This allows for the optimization of user health management by continuously evaluating the user's health status and updating advice as needed.

[0073] The data collection unit can securely manage users' health data and protect their privacy. For example, the data collection unit can encrypt users' health data to prevent unauthorized access by third parties. The data collection unit can anonymize users' health data to prevent the leakage of personal information. The data collection unit can securely store users' health data and prevent data loss. For example, the data collection unit can regularly back up data to prevent data loss. The data collection unit can develop and adhere to policies to securely manage users' health data and protect their privacy. By doing so, the data collection unit can gain users' trust by securely managing their health data and protecting their privacy.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect health data during relaxed periods. If the user is relaxed, the data collection unit can also collect detailed health data. If the user is in a hurry, the data collection unit can also collect simplified health data. This allows for data collection at a more appropriate time by adjusting the timing of health data collection based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can identify the most effective time of day for data collection from the user's past data. The data collection unit can also select the optimal data collection method for a specific symptom from the user's past data. The data collection unit can also adjust the data collection frequency from the user's past 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 processes 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.

[0076] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is interested in exercise, the data collection unit will prioritize collecting exercise-related data. If the user is interested in diet, the data collection unit can also prioritize collecting diet-related data. If the user is interested in sleep, the data collection unit can also prioritize collecting sleep-related data. By filtering the data based on the user's lifestyle and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate the user's emotions and prioritize the health data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. If the user is relaxed, the data collection unit can also collect general health data. If the user is in a hurry, the data collection unit can prioritize collecting only the most important data. This allows for the priority collection of important data by prioritizing health data based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information when collecting health data. For example, if the user is at high altitude, the data collection unit will collect health data specific to high altitude. If the user is in an urban area, the data collection unit can also collect health data specific to urban areas. If the user is at the beach, the data collection unit can also collect health data specific to beach areas. This allows for the collection of more appropriate data by collecting highly relevant data based on 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.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, if the user posts about exercise on social media, the data collection unit can collect exercise-related data. If the user posts about food on social media, the data collection unit can also collect food-related data. If the user posts about sleep on social media, the data collection unit can also collect sleep-related data. In this way, relevant health data can be collected 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 into a generating AI and have the generating AI perform the collection of relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides concise and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easier 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.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. The analysis unit can also perform a simplified analysis on general health data. The analysis unit can also perform a rapid analysis on health data that is of high urgency. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. The analysis unit can also apply a diet analysis algorithm to diet data. The analysis unit can also apply a sleep analysis algorithm to sleep data. By applying different analysis algorithms depending on the category of health data, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is in a hurry, the analysis unit can perform a rapid analysis. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit can also analyze current data while referring to past data. The analysis unit can also prioritize the analysis of data with high urgency. This allows for the prioritization of more important data by determining the priority of analysis based on the timing of health data collection. 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 timing of health data collection into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the health data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the health data. 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 relevance of the health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The service provider can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is stressed, the service provider will provide concise and easy-to-understand advice. If the user is relaxed, the service provider may also provide detailed advice. If the user is in a hurry, the service provider may also provide concise advice. By adjusting the way advice is presented based on the user's emotions, the service provider can provide advice that is easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The service provider can adjust the level of detail of advice based on the importance of the health data when providing advice. For example, the service provider can provide detailed advice for important health data. For general health data, it can also provide simplified advice. For urgent health data, it can provide advice quickly. This allows for detailed advice to be provided for important data by adjusting the level of detail of the advice based on the importance of the health data. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0088] The service provider can apply different advice algorithms depending on the category of health data when providing advice. For example, the service provider can apply an exercise advice algorithm to exercise data. The service provider can also apply a diet advice algorithm to diet data. The service provider can also apply a sleep advice algorithm to sleep data. By applying different advice algorithms depending on the category of health data, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the categories of health data into a generating AI and have the generating AI execute the application of different advice algorithms.

[0089] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the service provider will provide short, concise advice. If the user is relaxed, the service provider can also provide detailed advice. If the user is in a hurry, the service provider can provide advice quickly. By adjusting the length of the advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The service provider can prioritize advice based on when health data is collected. For example, it can provide advice based on recently collected data. It can also provide advice based on current data, while referring to past data. It can also provide advice based on data of high urgency. By prioritizing advice based on when health data is collected, it is possible to provide advice based on more important data. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the timing of health data collection into a generating AI and have the generating AI determine the priority of advice.

[0091] The service provider can adjust the order of advice based on the relevance of health data when providing advice. For example, the service provider can provide advice based on highly relevant data. The service provider can also postpone less relevant data. The service provider can also dynamically adjust the order of advice based on the relevance of the data. This allows for more efficient advice delivery by adjusting the order of advice based on the relevance of health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0092] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit will focus on stress reduction. If the user is relaxed, the monitoring unit can also monitor their overall health. If the user is in a hurry, the monitoring unit can monitor only key health indicators. This allows for more appropriate monitoring by adjusting the monitoring method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The monitoring unit can analyze the user's past health data during monitoring to select the optimal monitoring method. For example, the monitoring unit can identify the most effective monitoring time period from the user's past data. The monitoring unit can also select the optimal monitoring method for a specific symptom from the user's past data. The monitoring unit can also adjust the monitoring frequency from the user's past data. This allows the optimal monitoring method to be selected by analyzing the user's past health data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past health data into a generating AI and have the generating AI select the optimal monitoring method.

[0094] The monitoring unit can customize the monitoring methods based on the user's current living situation during monitoring. For example, if the user is busy, the monitoring unit can provide simplified monitoring methods. If the user is relaxed, the monitoring unit can also provide detailed monitoring methods. If the user is traveling, the monitoring unit can also provide monitoring methods suitable for travel. This allows for more appropriate monitoring by customizing the monitoring methods based on the user's current living situation. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's current living situation into a generating AI and have the generating AI perform the customization of the monitoring methods.

[0095] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is stressed, the monitoring unit will prioritize monitoring stress-related indicators. If the user is relaxed, the monitoring unit can also monitor general health indicators. If the user is in a hurry, the monitoring unit can prioritize monitoring only important health indicators. This allows for prioritizing monitoring of important health indicators by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The monitoring unit can select the optimal monitoring method based on the user's geographical location information during monitoring. For example, if the user is at high altitude, the monitoring unit will monitor health indicators specific to high altitude. If the user is in an urban area, the monitoring unit can also monitor health indicators specific to urban areas. If the user is at the coast, the monitoring unit can also monitor health indicators specific to coastal areas. By selecting the optimal monitoring method based on the user's geographical location information, more appropriate monitoring can be performed. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal monitoring method.

[0097] The monitoring unit can analyze the user's social media activity during monitoring and propose monitoring methods. For example, if the user posts about exercise on social media, the monitoring unit can propose exercise-related monitoring methods. If the user posts about food on social media, the monitoring unit can also propose meal-related monitoring methods. If the user posts about sleep on social media, the monitoring unit can also propose sleep-related monitoring methods. In this way, relevant monitoring methods can be proposed by analyzing the user's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's social media activity into a generating AI and have the generating AI execute the proposal of monitoring methods.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] A health management system can also include a learning unit that learns the user's lifestyle and provides personalized health management plans. The learning unit analyzes the user's past behavioral and health data to understand their lifestyle. For example, if a user has a habit of exercising at the same time every day, the learning unit can provide an exercise plan tailored to that time. Furthermore, if a user has a specific eating pattern, the learning unit can provide dietary advice based on that pattern. In addition, the learning unit can analyze the user's sleep patterns and suggest the optimal amount of sleep. This allows for more effective health management by providing personalized health management plans based on the user's lifestyle.

[0100] The health management system may also include an emotion adjustment unit that estimates the user's emotions and adjusts the health management plan based on those emotions. If the user is feeling stressed, the emotion adjustment unit provides advice to help them relax. For example, if the emotion adjustment unit estimates the user is stressed, it can suggest breathing exercises or meditation to help them relax. Furthermore, if the user is relaxed, the emotion adjustment unit can provide a more proactive health management plan. For example, if the emotion adjustment unit estimates the user is relaxed, it can suggest a new exercise or diet plan. Additionally, if the user is in a hurry, the emotion adjustment unit can provide a simplified health management plan. This allows for more appropriate health management by adjusting the health management plan based on the user's emotions.

[0101] The health management system can also include a social support section that leverages the user's social network to support health management. This social support section collaborates with the user's friends and family to provide health management support. For example, it can suggest exercise plans with friends to help the user maintain their exercise routine. It can also suggest plans for preparing healthy meals with family to help the user improve their diet. Furthermore, if the user is experiencing stress, the social support section can help them request support from friends and family. This leverages the user's social network to support health management, leading to more effective health management.

[0102] The health management system can also include a motivation unit that estimates the user's emotions and improves their motivation for health management based on those emotions. The motivation unit provides encouraging messages when the user is feeling stressed. For example, if the motivation unit estimates the user is stressed, it can offer words of encouragement or share success stories. Furthermore, if the user is relaxed, the motivation unit can suggest challenges to achieve goals. For example, if the motivation unit estimates the user is relaxed, it can set new exercise or dietary goals. Additionally, if the user is in a hurry, the motivation unit can suggest goals that can be achieved in a short time. This allows for more effective health management by improving motivation based on the user's emotions.

[0103] The health management system can also include a liaison unit that shares user health data with other medical institutions. The liaison unit shares user health data with medical institutions only after obtaining the user's consent. For example, the liaison unit can send health data to a medical institution the user regularly visits. The liaison unit can also quickly provide health data to a medical institution in case of an emergency. Furthermore, the liaison unit can provide past health data when the user seeks a diagnosis from a specialist. This allows users to receive more appropriate medical services by sharing their health data with other medical institutions.

[0104] The health management system may also include a feedback unit that estimates the user's emotions and adjusts the health management feedback based on those emotions. The feedback unit provides positive feedback when the user is stressed. For example, if the feedback unit estimates the user is stressed, it can send a message praising the goals achieved and the progress made. The feedback unit can also provide detailed feedback when the user is relaxed. For example, if the feedback unit estimates the user is relaxed, it can provide a detailed analysis of the health data. Furthermore, if the user is in a hurry, the feedback unit can provide concise feedback. This allows for more appropriate feedback by adjusting health management feedback based on the user's emotions.

[0105] A health management system can also include a prevention unit that performs proactive health management based on the user's health data. The prevention unit analyzes the user's health data and predicts future health risks. For example, it can predict the risk of future illnesses based on the user's past health data. It can also analyze the user's lifestyle and provide advice to reduce health risks. Furthermore, the prevention unit can suggest a schedule for regular health checkups based on the user's health data. This allows for proactive health management based on the user's health data, thereby reducing future health risks.

[0106] The health management system may also include a communication unit that estimates the user's emotions and adjusts health management communications based on those emotions. If the user is feeling stressed, the communication unit will communicate using gentle language. For example, if the communication unit estimates the user is stressed, it can offer words of encouragement and advice to help them relax. If the user is relaxed, the communication unit can also provide detailed information. For example, if the communication unit estimates the user is relaxed, it can provide detailed explanations and advice regarding health data. Furthermore, if the user is in a hurry, the communication unit can provide concise communication. This allows for more appropriate communication by adjusting health management communications based on the user's emotions.

[0107] The health management system can also include a goal-setting unit that sets personalized health goals based on the user's health data. The goal-setting unit analyzes the user's health data and sets individual health goals. For example, it can set appropriate exercise goals based on the user's current health status. It can also set healthy dietary goals based on the user's dietary data. Furthermore, it can set optimal sleep goals based on the user's sleep data. This allows for more effective health management by setting personalized health goals based on the user's health data.

[0108] The health management system may also include an interface unit that estimates the user's emotions and adjusts the health management interface based on those emotions. The interface unit provides a simple and intuitive interface when the user is stressed. For example, if the interface unit estimates the user is stressed, it can provide an easy-to-use interface. The interface unit can also provide an interface that displays detailed information when the user is relaxed. For example, if the interface unit estimates the user is relaxed, it can display detailed health data and advice. Furthermore, if the interface unit is in a hurry, it can provide an interface that quickly delivers information. This allows for a more appropriate interface to be provided by adjusting the health management interface based on the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects the user's health data. The data collection unit collects health-related questions and symptoms entered by the user, vital signs (heart rate, blood pressure, body temperature, etc.), dietary records, and exercise data. This data is stored in digital format and transmitted to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to analyze the data based on past medical data and the latest medical knowledge to evaluate the user's health status. The analysis unit can also analyze data patterns and detect anomalies. Step 3: The service provider provides advice based on the analysis results obtained by the analysis unit. The service provider provides users with specific advice and diagnoses, such as suggestions for dietary improvements and recommendations for exercise. The service provider can also use generative AI to provide personalized advice tailored to the user's health condition. Step 4: The monitoring unit continuously monitors the user's health status based on the advice provided by the service provider. The monitoring unit periodically collects the user's health data and transmits it to the analysis unit. The monitoring unit can also collect data in real time and issue alerts if an anomaly is detected.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user health data using the sensors and input interfaces of the smart device 14 and transmits it to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented in the control unit 46A of the smart device 14 and provides advice based on the analysis results. The monitoring unit continuously monitors the user's health status using the sensors of the smart device 14 and transmits it to the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, data provision unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user health data using the sensors and input interface of the smart glasses 214 and transmits it to the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The data provision unit is implemented in the control unit 46A of the smart glasses 214 and provides advice based on the analysis results. The monitoring unit continuously monitors the user's health status using the sensors of the smart glasses 214 and transmits it to the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user health data using the sensors and input interface of the headset terminal 314 and transmits it to the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides advice based on the analysis results. The monitoring unit continuously monitors the user's health status using the sensors of the headset terminal 314 and transmits it to the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and monitoring unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user health data using the sensors and input interfaces of the robot 414 and transmits it to the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides advice based on the analysis results. The monitoring unit continuously monitors the user's health status using the sensors of the robot 414 and transmits it to the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection unit that collects user health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit that provides advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a monitoring unit that continuously monitors the health status based on the advice provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect health-related questions and symptoms entered by users. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze data based on past medical data and the latest medical knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide specific advice and diagnoses based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, We continuously assess the user's health status and update advice as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Securely manage user health data and protect privacy. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, We estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, the system analyzes the user's past health data to select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, During monitoring, the monitoring methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, the optimal monitoring method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The monitoring unit, During monitoring, we analyze users' social media activity and propose monitoring methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit that provides advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a monitoring unit that continuously monitors the health status based on the advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect health-related questions and symptoms entered by users. The system according to feature 1.

3. The aforementioned analysis unit, Analyze data based on past medical data and the latest medical knowledge. The system according to feature 1.

4. The aforementioned supply unit is, We provide specific advice and diagnoses based on the analysis results. The system according to feature 1.

5. The monitoring unit, We continuously assess the user's health status and update advice as needed. The system according to feature 1.

6. The aforementioned collection unit is Securely manage user health data and protect privacy. The system according to feature 1.

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

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

9. The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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

Images