system

Abstract

The system according to this embodiment aims to provide personalized advice and follow-up based on information about the user's physical condition. [Solution] The system according to the embodiment comprises a collection unit, a provision unit, and a follow-up unit. The collection unit collects information about the user's physical condition. The provision unit provides advice based on the information collected by the collection unit. The follow-up unit performs follow-up 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, personalized support in the user's physical condition management has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized advice based on information related to the user's physical condition and perform follow-up.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a provision unit, and a follow-up unit. The collection unit collects information about the user's physical condition. The provision unit provides advice based on the information collected by the collection unit. The follow-up unit performs follow-up based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized advice and follow up based on information about the user's physical condition. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable 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 platform according to an embodiment of the present invention is a system that provides personalized support to users. This system is divided into three categories: "health maintenance and promotion," "care for mental and physical troubles," and "rehabilitation and recovery support." By interacting with the system, users can obtain appropriate information according to their situation. This eliminates the need for users to search for information, and continuous health management becomes possible through follow-up. For example, if a user consults the system about their physical condition, the system provides appropriate solutions and reference information. For instance, if a user consults the system about a sore throat, the system advises rest and proper nutrition and provides information on nutritional supplementation when feeling unwell. As follow-up, it also provides information on daily health management and physical fitness improvement for prevention. Furthermore, it recommends consulting a specialist as needed and provides information on online medical consultations. In this way, the system supports users' health management, facilitates access to health information, and supports the continuation of health management. Thus, the health management platform can support users' health management and enable continuous health management.

[0029] The health management platform according to the embodiment comprises a collection unit, a provision unit, and a follow-up unit. The collection unit collects information about the user's physical condition. For example, the collection unit collects information about the user's physical condition entered by the user. For example, the collection unit can collect information entered by the user through an application. The collection unit can also collect information dictated by the user using voice input. For example, the collection unit uses speech recognition technology to convert the user's dictated content into text data and collects it as physical condition information. The collection unit can collect information such as heart rate, blood pressure, and body temperature entered by the user. The provision unit provides advice based on the information collected by the collection unit. For example, the provision unit provides appropriate advice based on the collected information. For example, the provision unit can provide advice supervised by a doctor. The provision unit can also provide personalized advice. For example, the provision unit makes dietary suggestions and recommends exercise based on the user's physical condition information. For example, the provision unit analyzes the collected information and provides the user with the most suitable advice. The follow-up unit performs follow-up based on the advice provided by the provision unit. The follow-up unit, for example, supports continuous health management based on the advice provided. The follow-up unit, for example, performs regular check-ins and monitors the user's physical condition. The follow-up unit, for example, sends reminders to encourage the user to manage their health. As a result, the health management platform according to the embodiment enables continuous health management by collecting the user's physical condition information, providing advice, and performing follow-up.

[0030] The data collection unit collects information about the user's physical condition. For example, the data collection unit collects information about the user's physical condition that the user inputs. Specifically, the data collection unit can collect information that the user inputs through an application. Users input information about their daily physical condition using devices such as smartphones and tablets. For example, users can input detailed physical condition information such as heart rate, blood pressure, body temperature, weight, sleep duration, exercise level, diet, and stress level through the application interface. This allows the data collection unit to comprehensively understand the user's health status. The data collection unit can also collect information that the user dictates using voice input. For example, the data collection unit uses speech recognition technology to convert the user's dictated content into text data and collects it as physical condition information. Users can easily record their physical condition information simply by speaking into the application. Voice input is particularly convenient for users who have difficulty typing, such as the elderly and visually impaired. Furthermore, the data collection unit can also collect data from external devices such as wearable devices and smartwatches. These devices monitor biometric data such as heart rate, blood pressure, body temperature, sleep patterns, and exercise level in real time and transmit it to the data collection unit. This allows the data collection unit to collect users' health information more accurately and in greater detail. The collected data is securely stored on a cloud server and used for subsequent analysis and advice provision. The data collection unit has implemented encryption technology and access control to ensure data privacy and security. This prevents unauthorized access to users' personal information and provides a safe and secure environment for use.

[0031] The service provider provides advice based on the information collected by the data collection unit. For example, the service provider can provide appropriate advice based on the collected information. Specifically, the service provider can provide advice supervised by a physician. The collected health information is analyzed using algorithms supervised by physicians and specialists to generate the most suitable advice for the user. For example, if a user has high blood pressure, the service provider may advise reducing salt intake or recommend moderate exercise. The service provider can also provide personalized advice. Based on the user's health information and lifestyle, it makes dietary suggestions and recommends exercise. For example, if a user wants to lose weight, the service provider may suggest calorie-restricted and balanced meal plans. It may also recommend simple exercises that are easy to incorporate into daily life for users who are not getting enough exercise. The service provider analyzes the collected information and provides the most suitable advice for the user. The analysis uses AI technology to process the user's health data in real time and generate optimal advice. The AI ​​can use past data and statistical information to predict the user's health status and assess future risks. This allows the service provider to support users' health management and contribute to disease prevention and early detection. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its advice. By having users report the results of following the advice, the service provider can evaluate the effectiveness of the advice and make adjustments as needed. This enables the service provider to provide users with more effective health management support.

[0032] The Follow-up Department provides follow-up based on the advice given by the Service Provider Department. For example, the Follow-up Department supports continuous health management based on the provided advice. Specifically, the Follow-up Department conducts regular check-ins to monitor the user's physical condition. Users regularly update their physical condition information through the application, and the Follow-up Department evaluates the user's health status based on this information. For example, it checks how the user's physical condition has changed as a result of improving their lifestyle habits according to the advice. The Follow-up Department also sends reminders to encourage users to manage their health. For example, it sends notifications to remind users about medication times and exercise times to help them not neglect their health management. Furthermore, the Follow-up Department analyzes the user's physical condition information and updates the advice as needed. For example, if the user's physical condition does not improve, the Follow-up Department works with the Service Provider Department to provide new advice. The Follow-up Department also supports coordination with doctors and specialists depending on the user's health condition. For example, if the user's physical condition suddenly changes, the Follow-up Department contacts a doctor and encourages appropriate action. In this way, the Follow-up Department can continuously support the user's health management and promote improvement in their health status. Furthermore, the follow-up team provides feedback on goal setting and achievement to maintain user motivation. For example, they visualize the progress towards the health goals set by the user and send rewards and encouraging messages according to the degree of achievement. This helps users increase their motivation for health management and encourages them to continue their efforts.

[0033] The collection unit can collect information about the user's physical condition. For example, the collection unit collects information entered by the user through an application. The collection unit can also collect information dictated by the user using voice input. For example, the collection unit uses speech recognition technology to convert the user's dictated content into text data and collects it as physical condition information. The collection unit can also collect information such as heart rate, blood pressure, and body temperature entered by the user. By collecting the physical condition information entered by the user, appropriate advice can be provided. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the physical condition information entered by the user into an AI, which can then analyze and collect the information.

[0034] The service provider can provide appropriate advice based on the collected information. For example, the service provider can provide appropriate advice based on the collected information. For example, the service provider can provide advice supervised by a doctor. The service provider can also provide personalized advice. For example, the service provider can suggest meals and recommend exercise based on the user's health information. For example, the service provider can analyze the collected information and provide the user with the most suitable advice. In this way, by providing appropriate advice based on the collected information, it supports the user's health management. 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 collected information into AI, and the AI ​​can analyze the information and provide advice.

[0035] The follow-up unit can support ongoing health management based on the advice provided by the service provider. For example, the follow-up unit can support ongoing health management based on the provided advice. For example, the follow-up unit can perform regular check-ins to monitor the user's physical condition. For example, the follow-up unit can send reminders to encourage the user to manage their health. This promotes the maintenance of the user's health by supporting ongoing health management based on the provided advice. Some or all of the above processes in the follow-up unit may be performed using AI, for example, or not using AI. For example, the follow-up unit can input the provided advice into AI, and the AI ​​can determine the follow-up method.

[0036] The data collection unit may include an analysis unit that analyzes the collected information. The data collection unit may, for example, include an analysis unit that analyzes the collected information. The analysis unit may, for example, analyze the collected information using data mining techniques. The analysis unit may also, for example, analyze the collected information using statistical analysis techniques. This allows for the provision of more accurate advice by analyzing the collected information. 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 collected information into an AI, which can then analyze the information.

[0037] The information provider may include a reliability assurance unit to ensure the reliability of the information provided. The information provider may include a reliability assurance unit to ensure the reliability of the information provided. For example, the reliability assurance unit may evaluate the accuracy of the data. The reliability assurance unit may also evaluate the reliability of the information source. By ensuring the reliability of the information provided, users can accept the advice with confidence. Some or all of the above-described processes in the reliability assurance unit may be performed using AI, for example, or without AI. For example, the reliability assurance unit may input the information to be provided into an AI, which may evaluate the reliability of the information.

[0038] The follow-up unit may include an update unit that updates the information provided. The follow-up unit may include an update unit that updates the information provided. The update unit may, for example, update the data. The update unit may also, for example, add information. This ensures that the user is always provided with the latest information by updating the information provided. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the information to be provided into the AI, and the AI ​​can update the information.

[0039] The data collection unit can analyze the user's past health history and select the optimal data collection method. For example, the data collection unit can collect health information during times when the user frequently complained of feeling unwell in the past. For example, if the data collection unit can analyze the user's past health history and find that their health tends to worsen during certain seasons, it can focus on collecting health information during those seasons. For example, if the data collection unit analyzes the user's past health history and finds that their health worsens before or after certain events, it can collect health information before and after those events. This allows the optimal data collection method to be selected by analyzing the user's past health history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health history into AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter health information based on the user's current lifestyle and areas of interest. For example, if the user is busy, the data collection unit can collect health information in the form of simple questions. For example, if the user is highly interested in health, the data collection unit can also collect detailed health information. For example, if the user is interested in a particular health problem, the data collection unit can prioritize collecting health information related to that problem. This allows for the collection of more relevant information by filtering health information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into the AI, which can then filter the information.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting health information. For example, if the user is at high altitude, the data collection unit can collect health information related to high altitude. For example, if the user is in an urban area, the data collection unit can also collect health information related to urban areas. For example, if the user is traveling, the data collection unit can also collect health information related to the environment of the travel destination. This allows for the collection of highly relevant health information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, and the AI ​​can collect the information.

[0042] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting health information. For example, if a user complains of feeling unwell on social media, the data collection unit can collect health information based on that content. For example, if a user mentions a specific health problem on social media, the data collection unit can also collect health information related to that problem. For example, if a user frequently shares health-related information on social media, the data collection unit can also collect health information by referring to that information. In this way, relevant health information 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 AI, which can then analyze and collect the information.

[0043] The service provider can adjust the level of detail in the advice based on the importance of the health information when providing advice. For example, the service provider can provide detailed advice for health information of high importance. For example, the service provider can also provide concise advice for health information of low importance. The service provider can also determine the priority of the advice according to the importance of the health information. This allows for the provision of appropriate advice by adjusting the level of detail according to the importance of the health information. 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 importance of the health information into the AI, and the AI ​​can adjust the level of detail in the advice.

[0044] The service provider can apply different advice algorithms depending on the category of health information when providing advice. For example, for health information related to stress, the service provider can apply an algorithm that advises on relaxation methods. For example, for health information related to fatigue, the service provider can also apply an algorithm that advises on rest methods. For example, for health information related to nutrition, the service provider can also apply an algorithm that provides dietary advice. By applying different advice algorithms depending on the category of health information, 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 information into the AI, and the AI ​​can apply an advice algorithm.

[0045] The service provider can determine the priority of advice based on when the health information was submitted. For example, the service provider can prioritize advice for recently submitted health information. For example, the service provider can also postpone advice for older health information. The service provider can also dynamically adjust the priority of advice according to the submission date. This allows advice to be provided at the appropriate time by determining the priority of advice based on when the health information was submitted. 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 submission date of the health information into the AI, and the AI ​​can determine the priority of advice.

[0046] The advice delivery unit can adjust the order of advice based on the relevance of health information when providing advice. For example, the delivery unit will prioritize providing advice when the health information is highly relevant. For example, the delivery unit may postpone providing advice when the health information is less relevant. The delivery unit can also dynamically adjust the order of advice according to the relevance of health information. This allows for the provision of more relevant advice by adjusting the order of advice based on the relevance of health information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance of health information into the AI, and the AI ​​can adjust the order of advice.

[0047] The follow-up unit can analyze the user's past health management history to select the optimal follow-up method during follow-up. For example, the follow-up unit can perform follow-up during times when the user frequently complained of feeling unwell in the past. For example, if the follow-up unit can analyze the user's past health management history and find that their health tends to worsen during a particular season, it can focus follow-up during that season. For example, if the follow-up unit analyzes the user's past health management history and finds that their health worsens before or after a particular event, it can perform follow-up before or after that event. In this way, the optimal follow-up method can be selected by analyzing the user's past health management history. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the user's past health management history into AI, and the AI ​​can select the optimal follow-up method.

[0048] The follow-up unit can customize the follow-up methods based on the user's current lifestyle. For example, if the user is busy, the follow-up unit can conduct follow-up in the form of simple questions. If the user is particularly interested in health, the follow-up unit can also conduct detailed follow-up. If the user is interested in a specific health issue, the follow-up unit can also conduct follow-up related to that issue. By customizing the follow-up methods based on the user's current lifestyle, more appropriate follow-up can be provided. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the user's lifestyle into the AI, which can then customize the follow-up methods.

[0049] The follow-up unit can select the optimal follow-up method by considering the user's geographical location information during follow-up. For example, if the user is at high altitude, the follow-up unit will perform follow-up activities related to high altitude. If the user is in an urban area, the follow-up unit can also perform follow-up activities related to urban areas. If the user is traveling, the follow-up unit can also perform follow-up activities related to the environment of the travel destination. In this way, the optimal follow-up method can be selected by considering the user's geographical location information. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the user's geographical location information into AI, and the AI ​​can collect the information and select a follow-up method.

[0050] The follow-up unit can analyze the user's social media activity during follow-up and propose follow-up measures. For example, if the user complains of poor health on social media, the follow-up unit will perform follow-up based on that content. For example, if the user mentions a specific health problem on social media, the follow-up unit can also perform follow-up related to that problem. For example, if the user frequently shares health-related information on social media, the follow-up unit can also perform follow-up based on that information. In this way, by analyzing the user's social media activity, it is possible to propose relevant follow-up measures. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or not using AI. For example, the follow-up unit can input the user's social media activity into AI, which can then analyze the information and propose follow-up measures.

[0051] The analysis unit can optimize its analysis algorithm by referring to past health data during analysis. For example, the analysis unit analyzes the user's current health data based on the user's past health data. The analysis unit can also optimize its analysis algorithm by referring to past health data and finding specific patterns. The analysis unit can also improve the accuracy of its analysis algorithm by analyzing the user's past health data. This allows the analysis algorithm to be optimized by referring to past 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 past health data into AI, and the AI ​​can optimize the analysis algorithm.

[0052] The analysis unit can weight the analysis data based on when the health information was submitted. For example, the analysis unit can give higher weight to recently submitted health information. For example, the analysis unit can also give lower weight to older health information. The analysis unit can also dynamically adjust the weighting of the analysis data according to the submission date. This allows for more accurate analysis by weighting the analysis data based on when the health information was submitted. 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 submission date of the health information into the AI, and the AI ​​can weight the analysis data.

[0053] The reliability assurance unit can optimize the reliability evaluation algorithm by referring to past reliability data when ensuring reliability. For example, the reliability assurance unit evaluates the reliability of current information based on past reliability data. The reliability assurance unit can also optimize the reliability evaluation algorithm by referring to past reliability data and finding specific patterns. For example, the reliability assurance unit can improve the accuracy of the reliability evaluation algorithm by analyzing past reliability data. This allows the reliability evaluation algorithm to be optimized by referring to past reliability data. Some or all of the above processes in the reliability assurance unit may be performed using AI, for example, or without AI. For example, the reliability assurance unit can input past reliability data into AI, and the AI ​​can optimize the reliability evaluation algorithm.

[0054] The reliability assurance unit can weight reliability evaluation data based on the submission date of health information when ensuring reliability. For example, the reliability assurance unit may give higher weight to recently submitted health information. For example, the reliability assurance unit may also give lower weight to older health information. The reliability assurance unit can also dynamically adjust the weighting of reliability evaluation data according to the submission date. This allows for a more accurate reliability evaluation by weighting reliability evaluation data based on the submission date of health information. Some or all of the above processing in the reliability assurance unit may be performed using AI, for example, or without AI. For example, the reliability assurance unit can input the submission date of health information into the AI, and the AI ​​can weight the reliability evaluation data.

[0055] The update unit can optimize the update algorithm by referring to past update data during the update process. For example, the update unit updates the current information based on past update data. The update unit can also optimize the update algorithm by referring to past update data and finding specific patterns. The update unit can also improve the accuracy of the update algorithm by analyzing past update data. This allows the update algorithm to be optimized by referring to past update data. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update data into AI, and the AI ​​can optimize the update algorithm.

[0056] The update unit can weight the updated data based on when the health information was submitted. For example, the update unit can give a higher weight to recently submitted health information. For example, the update unit can also give a lower weight to older health information. The update unit can also dynamically adjust the weighting of the updated data according to the submission date. This allows for more accurate information updates by weighting the updated data based on when the health information was submitted. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the submission date of the health information into the AI, and the AI ​​can weight the updated data.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The health management platform comprises a collection unit that collects user health information, a provision unit that provides advice based on the collected information, and a follow-up unit that provides follow-up based on the provided advice. The collection unit not only collects health information entered by the user, but can also analyze the user's lifestyle and behavioral patterns to optimize the timing of health information collection. For example, if a user has a habit of jogging every morning, collecting health information after jogging can accurately grasp the effects of exercise. Also, if a user tends to stay up late, collecting health information at night can evaluate the effects of sleep deprivation. Furthermore, by collecting health information after the user consumes a specific meal, the effects of food can be analyzed. In this way, the collection unit can optimize the timing of health information collection based on the user's lifestyle and behavioral patterns, and provide more accurate information.

[0059] The data collection unit not only collects user health information but can also analyze the user's past health history to select the optimal collection method. For example, by collecting health information during times when the user frequently complained of feeling unwell in the past, changes in health can be detected early. Furthermore, if the user's past health history shows a tendency for their health to worsen during a particular season, the data collection unit can focus on collecting health information during that season. In addition, if the user's past health history shows a tendency for their health to worsen before or after a particular event, the data collection unit can collect health information before and after that event. In this way, the data collection unit can select the optimal collection method by analyzing the user's past health history and provide more accurate information.

[0060] The service provider can not only provide appropriate advice based on the collected information, but also customize the content of the advice based on the user's current lifestyle and areas of interest. For example, if the user is busy, the service provider can provide advice in the form of simple questions and suggest health management methods that can be implemented in a short amount of time. If the user is highly interested in health, the service provider can provide detailed advice and suggest a concrete action plan. Furthermore, if the user is interested in a specific health problem, the service provider can prioritize providing advice related to that problem. In this way, the service provider can customize the content of the advice based on the user's lifestyle and areas of interest, and provide more relevant information.

[0061] The follow-up department not only provides ongoing health management support based on the advice offered by the service provider, but can also select the most appropriate follow-up method considering the user's geographical location. For example, if the user is at high altitude, the follow-up department can provide high-altitude-related support and suggest health management methods for high altitudes. Similarly, if the user is in an urban area, the follow-up department can provide urban-related support and suggest health management methods for urban areas. Furthermore, if the user is traveling, the follow-up department can provide support related to the environment of their travel destination and suggest health management methods for their trip. This allows the follow-up department to select the most appropriate follow-up method and provide more effective support by considering the user's geographical location.

[0062] The data collection unit not only collects user health information but also analyzes users' social media activity to collect relevant information. For example, if a user complains of feeling unwell on social media, the unit can collect health information based on that content. Furthermore, if a user mentions a specific health issue on social media, the unit can collect health information related to that issue. Additionally, if a user frequently shares health-related information on social media, the unit can collect health information based on that information. This allows the data collection unit to collect relevant health information by analyzing users' social media activity and provide more accurate information.

[0063] The follow-up department not only provides ongoing health management support based on the advice given by the service provider, but can also analyze the user's past health management history to select the most appropriate follow-up method. For example, by conducting follow-ups during times when the user frequently reported feeling unwell in the past, changes in their health can be detected early. Furthermore, if the user's past health management history shows a tendency for their health to worsen during a particular season, follow-ups can be focused on that season. In addition, if the user's past health management history shows a tendency for their health to worsen before or after a specific event, follow-ups can be conducted before and after that event. In this way, the follow-up department can select the most appropriate follow-up method by analyzing the user's past health management history and provide more effective support.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The collection unit collects information about the user's physical condition. For example, the collection unit collects information entered by the user through the application or dictated using voice input. Using speech recognition technology, the user's dictated content can be converted into text data, and physical condition information such as heart rate, blood pressure, and body temperature can be collected. Step 2: The service provider provides advice based on the information collected by the data collection provider. The service provider analyzes the collected information and provides medically supervised and personalized advice. For example, it may suggest meals or recommend exercise based on the user's health information. Step 3: The Follow-up Department conducts follow-up based on the advice provided by the Service Provider Department. The Follow-up Department supports ongoing health management based on the provided advice and monitors the user's health by sending regular check-ins and reminders.

[0066] (Example of form 2) The health management platform according to an embodiment of the present invention is a system that provides personalized support to users. This system is divided into three categories: "health maintenance and promotion," "care for physical and mental troubles," and "rehabilitation and recovery support." By interacting with the system, users can obtain appropriate information according to their situation. This eliminates the need for users to search for information, and continuous health management becomes possible through follow-up. For example, if a user consults the system about their physical condition, the system provides appropriate solutions and reference information. For instance, if a user consults the system about a sore throat, the system advises rest and proper nutrition and provides information on nutritional supplementation when feeling unwell. As follow-up, it also provides information on daily health management and physical fitness improvement for prevention. Furthermore, it recommends consulting a specialist as needed and provides information on online medical consultations. In this way, the system supports users' health management, facilitates access to health information, and supports the continuation of health management. Thus, the health management platform can support users' health management and enable continuous health management.

[0067] The health management platform according to the embodiment comprises a collection unit, a provision unit, and a follow-up unit. The collection unit collects information about the user's physical condition. For example, the collection unit collects information about the user's physical condition entered by the user. For example, the collection unit can collect information entered by the user through an application. The collection unit can also collect information dictated by the user using voice input. For example, the collection unit uses speech recognition technology to convert the user's dictated content into text data and collects it as physical condition information. The collection unit can collect information such as heart rate, blood pressure, and body temperature entered by the user. The provision unit provides advice based on the information collected by the collection unit. For example, the provision unit provides appropriate advice based on the collected information. For example, the provision unit can provide advice supervised by a doctor. The provision unit can also provide personalized advice. For example, the provision unit makes dietary suggestions and recommends exercise based on the user's physical condition information. For example, the provision unit analyzes the collected information and provides the user with the most suitable advice. The follow-up unit performs follow-up based on the advice provided by the provision unit. The follow-up unit, for example, supports continuous health management based on the advice provided. The follow-up unit, for example, performs regular check-ins and monitors the user's physical condition. The follow-up unit, for example, sends reminders to encourage the user to manage their health. As a result, the health management platform according to the embodiment enables continuous health management by collecting the user's physical condition information, providing advice, and performing follow-up.

[0068] The data collection unit collects information about the user's physical condition. For example, the data collection unit collects information about the user's physical condition that the user inputs. Specifically, the data collection unit can collect information that the user inputs through an application. Users input information about their daily physical condition using devices such as smartphones and tablets. For example, users can input detailed physical condition information such as heart rate, blood pressure, body temperature, weight, sleep duration, exercise level, diet, and stress level through the application interface. This allows the data collection unit to comprehensively understand the user's health status. The data collection unit can also collect information that the user dictates using voice input. For example, the data collection unit uses speech recognition technology to convert the user's dictated content into text data and collects it as physical condition information. Users can easily record their physical condition information simply by speaking into the application. Voice input is particularly convenient for users who have difficulty typing, such as the elderly and visually impaired. Furthermore, the data collection unit can also collect data from external devices such as wearable devices and smartwatches. These devices monitor biometric data such as heart rate, blood pressure, body temperature, sleep patterns, and exercise level in real time and transmit it to the data collection unit. This allows the data collection unit to collect users' health information more accurately and in greater detail. The collected data is securely stored on a cloud server and used for subsequent analysis and advice provision. The data collection unit has implemented encryption technology and access control to ensure data privacy and security. This prevents unauthorized access to users' personal information and provides a safe and secure environment for use.

[0069] The service provider provides advice based on the information collected by the data collection unit. For example, the service provider can provide appropriate advice based on the collected information. Specifically, the service provider can provide advice supervised by a physician. The collected health information is analyzed using algorithms supervised by physicians and specialists to generate the most suitable advice for the user. For example, if a user has high blood pressure, the service provider may advise reducing salt intake or recommend moderate exercise. The service provider can also provide personalized advice. Based on the user's health information and lifestyle, it makes dietary suggestions and recommends exercise. For example, if a user wants to lose weight, the service provider may suggest calorie-restricted and balanced meal plans. It may also recommend simple exercises that are easy to incorporate into daily life for users who are not getting enough exercise. The service provider analyzes the collected information and provides the most suitable advice for the user. The analysis uses AI technology to process the user's health data in real time and generate optimal advice. The AI ​​can use past data and statistical information to predict the user's health status and assess future risks. This allows the service provider to support users' health management and contribute to disease prevention and early detection. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its advice. By having users report the results of following the advice, the service provider can evaluate the effectiveness of the advice and make adjustments as needed. This enables the service provider to provide users with more effective health management support.

[0070] The Follow-up Department provides follow-up based on the advice given by the Service Provider Department. For example, the Follow-up Department supports continuous health management based on the provided advice. Specifically, the Follow-up Department conducts regular check-ins to monitor the user's physical condition. Users regularly update their physical condition information through the application, and the Follow-up Department evaluates the user's health status based on this information. For example, it checks how the user's physical condition has changed as a result of improving their lifestyle habits according to the advice. The Follow-up Department also sends reminders to encourage users to manage their health. For example, it sends notifications to remind users about medication times and exercise times to help them not neglect their health management. Furthermore, the Follow-up Department analyzes the user's physical condition information and updates the advice as needed. For example, if the user's physical condition does not improve, the Follow-up Department works with the Service Provider Department to provide new advice. The Follow-up Department also supports coordination with doctors and specialists depending on the user's health condition. For example, if the user's physical condition suddenly changes, the Follow-up Department contacts a doctor and encourages appropriate action. In this way, the Follow-up Department can continuously support the user's health management and promote improvement in their health status. Furthermore, the follow-up team provides feedback on goal setting and achievement to maintain user motivation. For example, they visualize the progress towards the health goals set by the user and send rewards and encouraging messages according to the degree of achievement. This helps users increase their motivation for health management and encourages them to continue their efforts.

[0071] The collection unit can collect information about the user's physical condition. For example, the collection unit collects information entered by the user through an application. The collection unit can also collect information dictated by the user using voice input. For example, the collection unit uses speech recognition technology to convert the user's dictated content into text data and collects it as physical condition information. The collection unit can also collect information such as heart rate, blood pressure, and body temperature entered by the user. By collecting the physical condition information entered by the user, appropriate advice can be provided. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the physical condition information entered by the user into an AI, which can then analyze and collect the information.

[0072] The service provider can provide appropriate advice based on the collected information. For example, the service provider can provide appropriate advice based on the collected information. For example, the service provider can provide advice supervised by a doctor. The service provider can also provide personalized advice. For example, the service provider can suggest meals and recommend exercise based on the user's health information. For example, the service provider can analyze the collected information and provide the user with the most suitable advice. In this way, by providing appropriate advice based on the collected information, it supports the user's health management. 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 collected information into AI, and the AI ​​can analyze the information and provide advice.

[0073] The follow-up unit can support ongoing health management based on the advice provided by the service provider. For example, the follow-up unit can support ongoing health management based on the provided advice. For example, the follow-up unit can perform regular check-ins to monitor the user's physical condition. For example, the follow-up unit can send reminders to encourage the user to manage their health. This promotes the maintenance of the user's health by supporting ongoing health management based on the provided advice. Some or all of the above processes in the follow-up unit may be performed using AI, for example, or not using AI. For example, the follow-up unit can input the provided advice into AI, and the AI ​​can determine the follow-up method.

[0074] The data collection unit may include an analysis unit that analyzes the collected information. The data collection unit may, for example, include an analysis unit that analyzes the collected information. The analysis unit may, for example, analyze the collected information using data mining techniques. The analysis unit may also, for example, analyze the collected information using statistical analysis techniques. This allows for the provision of more accurate advice by analyzing the collected information. 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 collected information into an AI, which can then analyze the information.

[0075] The information provider may include a reliability assurance unit to ensure the reliability of the information provided. The information provider may include a reliability assurance unit to ensure the reliability of the information provided. For example, the reliability assurance unit may evaluate the accuracy of the data. The reliability assurance unit may also evaluate the reliability of the information source. By ensuring the reliability of the information provided, users can accept the advice with confidence. Some or all of the above-described processes in the reliability assurance unit may be performed using AI, for example, or without AI. For example, the reliability assurance unit may input the information to be provided into an AI, which may evaluate the reliability of the information.

[0076] The follow-up unit may include an update unit that updates the information provided. The follow-up unit may include an update unit that updates the information provided. The update unit may, for example, update the data. The update unit may also, for example, add information. This ensures that the user is always provided with the latest information by updating the information provided. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the information to be provided into the AI, and the AI ​​can update the information.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of collecting physical condition information based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect physical condition information during times when the user is relaxed. For example, if the user is tired, the data collection unit can also collect physical condition information after rest. For example, if the user is excited, the data collection unit can also collect physical condition information after the user has calmed down. By adjusting the timing of collecting physical condition information according to the user's emotions, information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into an AI, which can estimate the emotions and adjust the collection timing.

[0078] The data collection unit can analyze the user's past health history and select the optimal data collection method. For example, the data collection unit can collect health information during times when the user frequently complained of feeling unwell in the past. For example, if the data collection unit can analyze the user's past health history and find that their health tends to worsen during certain seasons, it can focus on collecting health information during those seasons. For example, if the data collection unit analyzes the user's past health history and finds that their health worsens before or after certain events, it can collect health information before and after those events. This allows the optimal data collection method to be selected by analyzing the user's past health history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health history into AI, which can then select the optimal data collection method.

[0079] The data collection unit can filter health information based on the user's current lifestyle and areas of interest. For example, if the user is busy, the data collection unit can collect health information in the form of simple questions. For example, if the user is highly interested in health, the data collection unit can also collect detailed health information. For example, if the user is interested in a particular health problem, the data collection unit can prioritize collecting health information related to that problem. This allows for the collection of more relevant information by filtering health information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into the AI, which can then filter the information.

[0080] The data collection unit can estimate the user's emotions and determine the priority of health information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related health information. For example, if the user is tired, the data collection unit may also prioritize collecting fatigue-related health information. For example, if the user is relaxed, the data collection unit may also collect health information regarding their overall health status. This allows for the priority collection of important information by prioritizing health information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of health information to collect.

[0081] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting health information. For example, if the user is at high altitude, the data collection unit can collect health information related to high altitude. For example, if the user is in an urban area, the data collection unit can also collect health information related to urban areas. For example, if the user is traveling, the data collection unit can also collect health information related to the environment of the travel destination. This allows for the collection of highly relevant health information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, and the AI ​​can collect the information.

[0082] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting health information. For example, if a user complains of feeling unwell on social media, the data collection unit can collect health information based on that content. For example, if a user mentions a specific health problem on social media, the data collection unit can also collect health information related to that problem. For example, if a user frequently shares health-related information on social media, the data collection unit can also collect health information by referring to that information. In this way, relevant health information 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 AI, which can then analyze and collect the information.

[0083] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is stressed, the service provider will provide advice in gentle language. For example, if the user is relaxed, the service provider may provide detailed advice. For example, if the user is in a hurry, the service provider may provide concise advice. This allows for more effective advice by adjusting the way advice is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI, which can estimate the emotions and adjust the way advice is expressed.

[0084] The service provider can adjust the level of detail in the advice based on the importance of the health information when providing advice. For example, the service provider can provide detailed advice for health information of high importance. For example, the service provider can also provide concise advice for health information of low importance. The service provider can also determine the priority of the advice according to the importance of the health information. This allows for the provision of appropriate advice by adjusting the level of detail according to the importance of the health information. 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 importance of the health information into the AI, and the AI ​​can adjust the level of detail in the advice.

[0085] The service provider can apply different advice algorithms depending on the category of health information when providing advice. For example, for health information related to stress, the service provider can apply an algorithm that advises on relaxation methods. For example, for health information related to fatigue, the service provider can also apply an algorithm that advises on rest methods. For example, for health information related to nutrition, the service provider can also apply an algorithm that provides dietary advice. By applying different advice algorithms depending on the category of health information, 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 information into the AI, and the AI ​​can apply an advice algorithm.

[0086] 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 in a hurry, the service provider can provide short, concise advice. If the user is relaxed, the service provider can also provide longer advice with detailed explanations. If the user is excited, the service provider can also provide advice with visually stimulating effects. By adjusting the length of the advice according to the user's emotions, more effective advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI, which can estimate the emotion and adjust the length of the advice.

[0087] The service provider can determine the priority of advice based on when the health information was submitted. For example, the service provider can prioritize advice for recently submitted health information. For example, the service provider can also postpone advice for older health information. The service provider can also dynamically adjust the priority of advice according to the submission date. This allows advice to be provided at the appropriate time by determining the priority of advice based on when the health information was submitted. 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 submission date of the health information into the AI, and the AI ​​can determine the priority of advice.

[0088] The advice delivery unit can adjust the order of advice based on the relevance of health information when providing advice. For example, the delivery unit will prioritize providing advice when the health information is highly relevant. For example, the delivery unit may postpone providing advice when the health information is less relevant. The delivery unit can also dynamically adjust the order of advice according to the relevance of health information. This allows for the provision of more relevant advice by adjusting the order of advice based on the relevance of health information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance of health information into the AI, and the AI ​​can adjust the order of advice.

[0089] The follow-up unit can estimate the user's emotions and adjust the follow-up method based on the estimated emotions. For example, if the user is stressed, the follow-up unit will use gentle words. If the user is relaxed, the follow-up unit may also provide a detailed follow-up. If the user is in a hurry, the follow-up unit may provide a concise follow-up. By adjusting the follow-up method according to the user's emotions, more effective follow-up can be achieved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the follow-up unit may be performed using AI or not using AI. For example, the follow-up unit can input user emotion data into AI, which can estimate the emotions and adjust the follow-up method.

[0090] The follow-up unit can analyze the user's past health management history to select the optimal follow-up method during follow-up. For example, the follow-up unit can perform follow-up during times when the user frequently complained of feeling unwell in the past. For example, if the follow-up unit can analyze the user's past health management history and find that their health tends to worsen during a particular season, it can focus follow-up during that season. For example, if the follow-up unit analyzes the user's past health management history and finds that their health worsens before or after a particular event, it can perform follow-up before or after that event. In this way, the optimal follow-up method can be selected by analyzing the user's past health management history. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the user's past health management history into AI, and the AI ​​can select the optimal follow-up method.

[0091] The follow-up unit can customize the follow-up methods based on the user's current lifestyle. For example, if the user is busy, the follow-up unit can conduct follow-up in the form of simple questions. If the user is particularly interested in health, the follow-up unit can also conduct detailed follow-up. If the user is interested in a specific health issue, the follow-up unit can also conduct follow-up related to that issue. By customizing the follow-up methods based on the user's current lifestyle, more appropriate follow-up can be provided. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the user's lifestyle into the AI, which can then customize the follow-up methods.

[0092] The follow-up unit can estimate the user's emotions and determine the priority of follow-ups based on the estimated emotions. For example, if the user is stressed, the follow-up unit will prioritize stress-related follow-ups. For example, if the user is tired, the follow-up unit may also prioritize fatigue-related follow-ups. For example, if the user is relaxed, the follow-up unit may also perform follow-ups regarding the user's overall health. This allows important follow-ups to be prioritized by determining the priority of follow-ups according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the follow-up unit may be performed using AI or not using AI. For example, the follow-up unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of follow-ups.

[0093] The follow-up unit can select the optimal follow-up method by considering the user's geographical location information during follow-up. For example, if the user is at high altitude, the follow-up unit will perform follow-up activities related to high altitude. If the user is in an urban area, the follow-up unit can also perform follow-up activities related to urban areas. If the user is traveling, the follow-up unit can also perform follow-up activities related to the environment of the travel destination. In this way, the optimal follow-up method can be selected by considering the user's geographical location information. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the user's geographical location information into AI, and the AI ​​can collect the information and select a follow-up method.

[0094] The follow-up unit can analyze the user's social media activity during follow-up and propose follow-up measures. For example, if the user complains of poor health on social media, the follow-up unit will perform follow-up based on that content. For example, if the user mentions a specific health problem on social media, the follow-up unit can also perform follow-up related to that problem. For example, if the user frequently shares health-related information on social media, the follow-up unit can also perform follow-up based on that information. In this way, by analyzing the user's social media activity, it is possible to propose relevant follow-up measures. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or not using AI. For example, the follow-up unit can input the user's social media activity into AI, which can then analyze the information and propose follow-up measures.

[0095] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit will focus on analyzing stress-related data. For example, if the user is relaxed, the analysis unit can also analyze data related to overall health. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into an AI, which can estimate the emotions and adjust the analysis method.

[0096] The analysis unit can optimize its analysis algorithm by referring to past health data during analysis. For example, the analysis unit analyzes the user's current health data based on the user's past health data. The analysis unit can also optimize its analysis algorithm by referring to past health data and finding specific patterns. The analysis unit can also improve the accuracy of its analysis algorithm by analyzing the user's past health data. This allows the analysis algorithm to be optimized by referring to past 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 past health data into AI, and the AI ​​can optimize the analysis algorithm.

[0097] The analysis unit can estimate the user's emotions and adjust the frequency of analysis based on the estimated emotions. For example, the analysis unit may perform analyses more frequently if the user is stressed. For example, the analysis unit may reduce the frequency of analysis if the user is relaxed. For example, the analysis unit may perform analyses quickly if the user is in a hurry. By adjusting the frequency of analysis according to the user's emotions, analyses can be performed at a more appropriate frequency. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI, which can estimate the emotions and adjust the frequency of analysis.

[0098] The analysis unit can weight the analysis data based on when the health information was submitted. For example, the analysis unit can give higher weight to recently submitted health information. For example, the analysis unit can also give lower weight to older health information. The analysis unit can also dynamically adjust the weighting of the analysis data according to the submission date. This allows for more accurate analysis by weighting the analysis data based on when the health information was submitted. 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 submission date of the health information into the AI, and the AI ​​can weight the analysis data.

[0099] The reliability assurance unit can estimate the user's emotions and adjust the method for evaluating the reliability of information based on the estimated user emotions. For example, if the user is stressed, the reliability assurance unit will prioritize providing reliable information. For example, if the user is relaxed, the reliability assurance unit may also provide detailed information. For example, if the user is in a hurry, the reliability assurance unit may also provide concise and reliable information. This allows for the provision of more reliable information by adjusting the method for evaluating the reliability of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reliability assurance unit may be performed using AI or not using AI. For example, the reliability assurance unit can input user emotion data into an AI and adjust the method by which the AI ​​estimates emotions and evaluates the reliability of information.

[0100] The reliability assurance unit can optimize the reliability evaluation algorithm by referring to past reliability data when ensuring reliability. For example, the reliability assurance unit evaluates the reliability of current information based on past reliability data. The reliability assurance unit can also optimize the reliability evaluation algorithm by referring to past reliability data and finding specific patterns. For example, the reliability assurance unit can improve the accuracy of the reliability evaluation algorithm by analyzing past reliability data. This allows the reliability evaluation algorithm to be optimized by referring to past reliability data. Some or all of the above processes in the reliability assurance unit may be performed using AI, for example, or without AI. For example, the reliability assurance unit can input past reliability data into AI, and the AI ​​can optimize the reliability evaluation algorithm.

[0101] The reliability assurance unit can estimate the user's emotions and adjust the frequency of reliability evaluations based on the estimated user emotions. For example, the reliability assurance unit may perform reliability evaluations more frequently if the user is stressed. For example, the reliability assurance unit may reduce the frequency of reliability evaluations if the user is relaxed. For example, the reliability assurance unit may perform reliability evaluations quickly if the user is in a hurry. By adjusting the frequency of reliability evaluations according to the user's emotions, reliability evaluations can be performed at a more appropriate frequency. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reliability assurance unit may be performed using AI or not using AI. For example, the reliability assurance unit can input user emotion data into an AI, which can estimate emotions and adjust the frequency of reliability evaluations.

[0102] The reliability assurance unit can weight reliability evaluation data based on the submission date of health information when ensuring reliability. For example, the reliability assurance unit may give higher weight to recently submitted health information. For example, the reliability assurance unit may also give lower weight to older health information. The reliability assurance unit can also dynamically adjust the weighting of reliability evaluation data according to the submission date. This allows for a more accurate reliability evaluation by weighting reliability evaluation data based on the submission date of health information. Some or all of the above processing in the reliability assurance unit may be performed using AI, for example, or without AI. For example, the reliability assurance unit can input the submission date of health information into the AI, and the AI ​​can weight the reliability evaluation data.

[0103] The update unit can estimate the user's emotions and adjust how information is updated based on the estimated emotions. For example, if the user is stressed, the update unit will update the information in gentle language. For example, if the user is relaxed, the update unit may also update detailed information. For example, if the user is in a hurry, the update unit may also update concise information. This allows for more appropriate information updates by adjusting how information is updated according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input user emotion data into an AI, which can estimate the emotions and adjust how information is updated.

[0104] The update unit can optimize the update algorithm by referring to past update data during the update process. For example, the update unit updates the current information based on past update data. The update unit can also optimize the update algorithm by referring to past update data and finding specific patterns. The update unit can also improve the accuracy of the update algorithm by analyzing past update data. This allows the update algorithm to be optimized by referring to past update data. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update data into AI, and the AI ​​can optimize the update algorithm.

[0105] The update unit can estimate the user's emotions and adjust the frequency of information updates based on the estimated emotions. For example, if the user is stressed, the update unit will update the information more frequently. For example, if the user is relaxed, the update unit can reduce the frequency of updates. For example, if the user is in a hurry, the update unit can update the information quickly. This allows for more appropriate updates by adjusting the frequency of information updates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input user emotion data into an AI, which can estimate the emotions and adjust the frequency of information updates.

[0106] The update unit can weight the updated data based on when the health information was submitted. For example, the update unit can give a higher weight to recently submitted health information. For example, the update unit can also give a lower weight to older health information. The update unit can also dynamically adjust the weighting of the updated data according to the submission date. This allows for more accurate information updates by weighting the updated data based on when the health information was submitted. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the submission date of the health information into the AI, and the AI ​​can weight the updated data.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The health management platform comprises a collection unit that collects user health information, a provision unit that provides advice based on the collected information, and a follow-up unit that provides follow-up based on the provided advice. The collection unit not only collects health information entered by the user, but can also analyze the user's lifestyle and behavioral patterns to optimize the timing of health information collection. For example, if a user has a habit of jogging every morning, collecting health information after jogging can accurately grasp the effects of exercise. Also, if a user tends to stay up late, collecting health information at night can evaluate the effects of sleep deprivation. Furthermore, by collecting health information after the user consumes a specific meal, the effects of food can be analyzed. In this way, the collection unit can optimize the timing of health information collection based on the user's lifestyle and behavioral patterns, and provide more accurate information.

[0109] The service provider can not only provide appropriate advice based on the collected information, but also estimate the user's emotions and adjust the content and expression of the advice based on those emotions. For example, if the user is feeling stressed, it can provide advice to help them relax and express it in gentle language. If the user is relaxed, it can provide detailed advice and suggest a concrete action plan. Furthermore, if the user is in a hurry, it can provide concise and to-the-point advice. In this way, the service provider can adjust the content and expression of advice according to the user's emotions, providing more effective support.

[0110] The follow-up department not only supports ongoing health management based on the advice provided by the service provider, but can also estimate the user's emotions and adjust the follow-up method based on those estimates. For example, if the user is stressed, the follow-up can be conducted with gentle words and advice to help them relax. If the user is relaxed, the follow-up can be conducted in detail and a concrete action plan can be proposed. Furthermore, if the user is in a hurry, the follow-up can be conducted concisely and to the point. In this way, the follow-up department can adjust the follow-up method according to the user's emotions and provide more effective support.

[0111] The data collection unit not only collects user health information but can also analyze the user's past health history to select the optimal collection method. For example, by collecting health information during times when the user frequently complained of feeling unwell in the past, changes in health can be detected early. Furthermore, if the user's past health history shows a tendency for their health to worsen during a particular season, the data collection unit can focus on collecting health information during that season. In addition, if the user's past health history shows a tendency for their health to worsen before or after a particular event, the data collection unit can collect health information before and after that event. In this way, the data collection unit can select the optimal collection method by analyzing the user's past health history and provide more accurate information.

[0112] The service provider can not only provide appropriate advice based on the collected information, but also customize the content of the advice based on the user's current lifestyle and areas of interest. For example, if the user is busy, the service provider can provide advice in the form of simple questions and suggest health management methods that can be implemented in a short amount of time. If the user is highly interested in health, the service provider can provide detailed advice and suggest a concrete action plan. Furthermore, if the user is interested in a specific health problem, the service provider can prioritize providing advice related to that problem. In this way, the service provider can customize the content of the advice based on the user's lifestyle and areas of interest, and provide more relevant information.

[0113] The follow-up department not only provides ongoing health management support based on the advice offered by the service provider, but can also select the most appropriate follow-up method considering the user's geographical location. For example, if the user is at high altitude, the follow-up department can provide high-altitude-related support and suggest health management methods for high altitudes. Similarly, if the user is in an urban area, the follow-up department can provide urban-related support and suggest health management methods for urban areas. Furthermore, if the user is traveling, the follow-up department can provide support related to the environment of their travel destination and suggest health management methods for their trip. This allows the follow-up department to select the most appropriate follow-up method and provide more effective support by considering the user's geographical location.

[0114] The data collection unit not only collects user health information but also analyzes users' social media activity to collect relevant information. For example, if a user complains of feeling unwell on social media, the unit can collect health information based on that content. Furthermore, if a user mentions a specific health issue on social media, the unit can collect health information related to that issue. Additionally, if a user frequently shares health-related information on social media, the unit can collect health information based on that information. This allows the data collection unit to collect relevant health information by analyzing users' social media activity and provide more accurate information.

[0115] The service provider can not only provide appropriate advice based on the collected information, but also estimate the user's emotions and adjust the way the advice is expressed based on those emotions. For example, if the user is feeling stressed, it can provide advice in gentle language and suggest ways to relax. If the user is relaxed, it can provide detailed advice and suggest a concrete action plan. Furthermore, if the user is in a hurry, it can provide concise and to-the-point advice. In this way, the service provider can adjust the way advice is expressed according to the user's emotions and provide more effective support.

[0116] The follow-up department not only provides ongoing health management support based on the advice given by the service provider, but can also analyze the user's past health management history to select the most appropriate follow-up method. For example, by conducting follow-ups during times when the user frequently reported feeling unwell in the past, changes in their health can be detected early. Furthermore, if the user's past health management history shows a tendency for their health to worsen during a particular season, follow-ups can be focused on that season. In addition, if the user's past health management history shows a tendency for their health to worsen before or after a specific event, follow-ups can be conducted before and after that event. In this way, the follow-up department can select the most appropriate follow-up method by analyzing the user's past health management history and provide more effective support.

[0117] The data collection unit not only collects user health information but can also estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if a user is stressed, collecting health information during a relaxed period can provide more accurate data. If a user is tired, data can be collected after they have rested. Furthermore, if a user is excited, data can be collected after they have calmed down. In this way, the data collection unit can adjust the timing of data collection according to the user's emotions, allowing for data to be collected at a more appropriate time.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The collection unit collects information about the user's physical condition. For example, the collection unit collects information entered by the user through the application or dictated using voice input. Using speech recognition technology, the user's dictated content can be converted into text data, and physical condition information such as heart rate, blood pressure, and body temperature can be collected. Step 2: The service provider provides advice based on the information collected by the data collection provider. The service provider analyzes the collected information and provides medically supervised and personalized advice. For example, it may suggest meals or recommend exercise based on the user's health information. Step 3: The Follow-up Department conducts follow-up based on the advice provided by the Service Provider Department. The Follow-up Department supports ongoing health management based on the provided advice and monitors the user's health by sending regular check-ins and reminders.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the collection unit, provision unit, and follow-up unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect information entered by the user through an application on the smart device 14. The collection unit can also collect information dictated by the user using the voice input of the smart device 14. The provision unit can analyze the information collected by the specific processing unit 290 of the data processing unit 12 and provide the user with optimal advice. The follow-up unit can support continuous health management based on the advice provided by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the collection unit, provision unit, and follow-up unit, can be implemented, for example, in at least one of the smart glasses 224 and the data processing unit 12. For example, the collection unit can collect information entered by the user through the application of the smart glasses 224. The collection unit can also collect information dictated by the user using the voice input of the smart glasses 224. The provision unit can analyze the information collected by the specific processing unit 290 of the data processing unit 12 and provide the user with optimal advice. The follow-up unit can support continuous health management based on the advice provided by the control unit 46A of the smart glasses 224. 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.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements, including the collection unit, provision unit, and follow-up unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect information entered by the user through the application of the headset terminal 314. The collection unit can also collect information dictated by the user using the voice input of the headset terminal 314. The provision unit can analyze the information collected by the specific processing unit 290 of the data processing unit 12 and provide the user with optimal advice. The follow-up unit can support continuous health management based on the advice provided by the control unit 46A of the headset terminal 334. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements, including the collection unit, provision unit, and follow-up unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect information entered by the user through the application of the robot 414. The collection unit can also collect information dictated by the user using the voice input of the robot 414. The provision unit can analyze the information collected by the specific processing unit 290 of the data processing unit 12 and provide the user with optimal advice. The follow-up unit can support continuous health management based on the advice provided by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) A collection unit that collects information about the user's physical condition, A provision unit provides advice based on the information collected by the aforementioned collection unit, The system includes a follow-up unit that performs follow-up 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 information about the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide appropriate advice based on the information we collect. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned follow-up unit is, Supporting ongoing health management based on advice provided by the service provider. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It includes an analysis unit that analyzes the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It includes a reliability assurance unit to ensure the reliability of the information provided. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned follow-up unit is, It includes an update unit that updates the information provided. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting health information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past health history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting health information, 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 11) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the health information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting health information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the health information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of health information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the health information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, adjust the order of advice based on the relevance of the health information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned follow-up unit is, It estimates the user's emotions and adjusts the follow-up method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned follow-up unit is, During follow-up, the system analyzes the user's past health management history to select the most appropriate follow-up method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned follow-up unit is, During follow-up, customize the follow-up methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned follow-up unit is, The system estimates the user's emotions and determines the priority of follow-up based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned follow-up unit is, During follow-up, the optimal follow-up method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned follow-up unit is, During follow-up, we analyze the user's social media activity and suggest follow-up methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past health data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, It estimates the user's emotions and adjusts the frequency of analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, During analysis, the analysis data is weighted based on when health information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The reliability assurance unit is, Adjust the method for estimating user sentiment and evaluating the reliability of information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The reliability assurance unit is, When ensuring reliability, the reliability evaluation algorithm is optimized by referring to past reliability data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The reliability assurance unit is, The system estimates the user's sentiment and adjusts the frequency of reliability evaluations based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The reliability assurance unit is, When ensuring reliability, the reliability evaluation data is weighted based on the timing of submission of health information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned update unit is, It estimates the user's emotions and adjusts how information is updated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned update unit is, During updates, the update algorithm is optimized by referring to past update data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned update unit is, It estimates the user's emotions and adjusts the frequency of information updates based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned update unit is, When updating, the updated data will be weighted based on when the health information was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0192] 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 information about the user's physical condition, A provision unit provides advice based on the information collected by the aforementioned collection unit, The system includes a follow-up unit that performs follow-up based on the advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned follow-up unit is, Support for continuous health management based on the advice provided by the aforementioned service provider. The system according to feature 1.

3. The aforementioned collection unit is It includes an analysis unit that analyzes the collected information. The system according to feature 1.

4. The aforementioned supply unit is, It includes a reliability assurance unit to ensure the reliability of the information provided. The system according to feature 1.

5. The aforementioned follow-up unit is, It includes an update unit that updates the information provided. The system according to feature 1.

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

Images