System

Abstract

To provide a system for tracking progress by providing individually customized advice on the basis of a health diagnosis result.SOLUTION: A system includes a reception section, an analysis section, a providing section, and a tracking section. The reception section receives input of a health diagnosis result. The analysis section analyzes the health diagnosis result received by the reception section. The providing section provides individually customized advice on the basis of the analysis result obtained by the analysis section. The tracking section tracks a user's progress on the basis of the advice provided by the providing section.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Previous technology has had issues with providing personalized advice based on health check results and tracking progress.

[0005] The system according to the embodiment aims to provide individually customized advice based on the results of health checkups and to track progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a tracking unit. The reception unit receives input of medical examination results. The analysis unit analyzes the medical examination results received by the reception unit. The provision unit provides individually customized advice based on the analysis results obtained by the analysis unit. The tracking unit tracks the user's progress based on the advice provided by the provision unit. Effect of the Invention

[0007] The system according to the embodiment can provide personalized advice based on the health check results and track progress. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health management system according to an embodiment of the present invention is a system that provides lifestyle advice that is individually customized based on the results of a health check. In this health management system, a user inputs the results of a health check, and a generation AI analyzes the results and provides advice that can be improved in everyday life, focusing on health such as nutrition, exercise, and sleep. In addition, progress is tracked through an app or the like to support the user in achieving their goals. For example, a user inputs the results of a health check. For example, data such as blood pressure, blood sugar level, and cholesterol level are input. This data is input into the generation AI. Next, the generation AI analyzes the input data. The generation AI evaluates the user's health condition and identifies areas that need improvement in each field such as nutrition, exercise, and sleep. For example, if blood pressure is high, advice to limit salt intake is provided. The generation AI provides individually customized advice based on the analysis results. For example, the system reviews the user's diet and suggests that the user eat a balanced diet. In addition, in the case of lack of exercise, an appropriate exercise plan is provided. In addition, advice to improve the quality of sleep is also provided. The user's progress is tracked through the app. The user records their daily activities and dietary content and inputs them into the app. The generative AI analyzes these data and supports the user in achieving their goals. For example, by setting exercise goals and tracking the progress of those goals, the user's motivation is maintained. This allows the user to understand his / her health condition and receive specific advice to improve lifestyle habits. For example, practical advice is provided in daily life, such as improving diet and establishing exercise habits. In addition, tracking progress through the app makes it easier for the user to maintain motivation to achieve their goals. This allows the health management system to understand the user's health condition and provide specific advice to improve lifestyle habits.

[0029] The health management system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a tracking unit. The reception unit receives the results of a medical checkup input by the user. The results of the medical checkup input by the user include, but are not limited to, blood pressure, blood glucose level, cholesterol level, and the like. The reception unit can, for example, receive data manually input by the user or directly submitted in digital form. The analysis unit uses a generation AI to analyze the results of the medical checkup received by the reception unit. The generation AI can, for example, use a text generation AI (for example, LLM) to analyze the results of the medical checkup and evaluate the health condition of the user. The analysis unit can also use the generation AI to evaluate the health condition of the user and identify areas that need improvement in each field, such as nutrition, exercise, and sleep. For example, the generation AI provides advice to limit salt intake when blood pressure is high. The provision unit provides individually customized advice based on the analysis results obtained by the analysis unit. For example, the provision unit recommends reviewing the user's diet and eating a balanced diet. The provision unit can also provide an appropriate exercise plan in the case of lack of exercise. Furthermore, the providing unit can also provide advice to improve the quality of sleep. For example, the providing unit suggests that the user review the dietary content and take a balanced diet. In the case of lack of exercise, an appropriate exercise plan can also be provided. Advice to improve the quality of sleep can also be provided. The tracking unit tracks the progress of the user based on the advice provided by the providing unit. For example, the tracking unit records the user's daily activities and dietary content, which are analyzed by the generating AI. The tracking unit can also track the user's goal achievement and provide feedback to maintain motivation. For example, the tracking unit maintains the user's motivation by setting an exercise goal and tracking the achievement level. As a result, the health management system according to the embodiment can grasp the user's health condition and provide specific advice to improve lifestyle habits. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI.For example, the tracking unit can input the user's daily activities and dietary details into the generation AI, which can then analyze them.

[0030] The reception unit allows the user to input the results of the medical checkup. The results of the medical checkup input by the user include, for example, blood pressure, blood glucose level, cholesterol level, etc., but are not limited to such examples. The reception unit can, for example, directly accept data manually input by the user or submitted in digital form. Specifically, the user can input the results of the medical checkup through a dedicated application or web portal. In the case of manual input, the user inputs a numerical value for each item, and the system automatically saves the data. In the case of submission in digital form, for example, an electronic medical record provided by a hospital or clinic or a PDF file of the medical checkup results can be uploaded. This allows the reception unit to enable the user to easily and quickly input the results of the medical checkup into the system. In addition, the reception unit also has a function for checking the accuracy of the input data. For example, it can detect abnormal values ​​or input errors and display an alert to prompt the user to recheck. Furthermore, in order to protect the privacy of the user, the reception unit encrypts and controls access to the data to prevent unauthorized access by a third party. This allows the reception unit to provide an environment in which the user can input the results of the medical checkup with peace of mind.

[0031] The analysis unit uses the generation AI to analyze the health check results received by the reception unit. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the health check results and evaluate the user's health condition. Specifically, the generation AI analyzes the input numerical data and determines the normal range and abnormal values ​​for each item. For example, if the blood pressure is beyond the normal range, it determines that there is a risk of high blood pressure and provides appropriate advice. The generation AI can also grasp the trend of the user's health condition and evaluate long-term health risks by comparing it with past data and data of other users. Furthermore, the generation AI generates individually customized advice taking into account the user's lifestyle and environmental factors. For example, if the user is at risk of high blood pressure, it recommends dietary advice such as limiting salt intake and moderate exercise. The generation AI can also comprehensively evaluate the user's health condition and identify areas that need improvement in each area, such as nutrition, exercise, and sleep. This allows the analysis unit to evaluate the user's health condition in detail and provide specific and actionable advice. In addition, the analysis department continuously updates the generative AI's learning data to reflect the latest medical information and research results, ensuring highly accurate analysis at all times.

[0032] The providing unit provides individually customized advice based on the analysis results obtained by the analyzing unit. For example, the providing unit may review the user's diet and suggest a balanced diet. In addition, the providing unit may provide an appropriate exercise plan in the case of lack of exercise. Specifically, the providing unit creates a meal plan according to the user's health condition and lifestyle habits, and suggests specific menus and recipes. For example, for a user with high blood pressure, a low-salt diet and foods high in potassium are recommended. In addition, the providing unit may provide an appropriate exercise plan in consideration of the user's exercise habits. For example, for a user who is lacking in exercise, walking several times a week and light muscle training are recommended, and a specific exercise method and time are suggested. In addition, the providing unit may provide advice for improving the quality of sleep. For example, a method of relaxing before going to bed and a method of preparing an appropriate sleeping environment are suggested. In order to provide these pieces of advice to the user in an easy-to-understand manner, the providing unit provides information through a dedicated application or web portal. The user can improve his / her health condition by incorporating the provided advice into his / her daily life. In addition, the providing unit may collect feedback from the user and continuously improve the content of the advice. This enables the providing unit to provide optimal advice to the user and support health management.

[0033] The tracking unit tracks the progress of the user based on the advice provided by the providing unit. For example, the tracking unit records the user's daily activities and dietary content, and the generating AI analyzes it. Specifically, the user can record the daily dietary content, amount of exercise, sleep time, etc. through a dedicated application. The tracking unit collects these data, and the generating AI analyzes it to evaluate the user's goal achievement level. For example, it evaluates the extent to which the actual amount of exercise has been achieved with respect to the exercise goal set by the user, and provides feedback according to the achievement level. In addition, the tracking unit can provide rewards and encouraging messages according to the achievement level in order to maintain the user's motivation. Furthermore, the tracking unit can track changes in the user's health condition over the long term and review the content of the advice as necessary. For example, if the user's health condition has improved, a new goal is set and further health improvement is supported. In addition, the tracking unit can collect the user's feedback and use it to improve the entire system. As a result, the tracking unit can continuously support the user's health management and promote the improvement of the health condition. Furthermore, some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's daily activities and dietary details into the generation AI, which can then analyze them. This allows the tracking unit to efficiently and effectively support the user's health management.

[0034] The providing unit can provide suggestions of nutritional ingredients and recipes. The providing unit can suggest low-calorie ingredients and vitamin-rich recipes based on the user's health condition, for example. For example, the providing unit can suggest low-calorie ingredients based on the user's health condition. The providing unit can also suggest vitamin-rich recipes. Furthermore, the providing unit can also suggest a balanced meal based on the user's health condition. For example, the providing unit can suggest a balanced meal based on the user's health condition. This makes it possible to provide specific nutritional advice to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input low-calorie ingredients and vitamin-rich recipes to the generating AI based on the user's health condition, and the generating AI can analyze and suggest them.

[0035] The providing unit can provide an exercise plan according to the physical fitness level of the user. The providing unit proposes an appropriate exercise plan based on, for example, the physical fitness level of the user. For example, the providing unit can propose an exercise plan to improve cardiopulmonary function based on the physical fitness level of the user. The providing unit can also propose an exercise plan to improve muscle strength. Furthermore, the providing unit can also propose an exercise plan to improve flexibility. For example, the providing unit can propose an exercise plan to improve cardiopulmonary function based on the physical fitness level of the user. An exercise plan to improve muscle strength can also be proposed. An exercise plan to improve flexibility can also be proposed. This makes it possible to provide an appropriate exercise plan for the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input an appropriate exercise plan to the generating AI based on the physical fitness level of the user, and the generating AI can analyze and propose it.

[0036] The providing unit can provide advice for improving the quality of sleep. The providing unit provides advice for improving the quality of sleep, for example, based on the user's sleep state. For example, the providing unit can provide advice for increasing the user's sleep time. In addition, the providing unit can also provide advice for improving the depth of sleep. Furthermore, the providing unit can provide advice for improving the sleep environment. For example, the providing unit can provide advice for increasing the user's sleep time. Advice for improving the depth of sleep can also be provided. Advice for improving the sleep environment can also be provided. Thereby, advice for improving the quality of sleep can be provided to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input advice for improving the quality of sleep to the generating AI based on the user's sleep state, and the generating AI can analyze and propose it.

[0037] The tracking unit records the user's daily activities and dietary contents, and the generation AI can analyze them. The tracking unit records, for example, the user's daily activities and dietary contents. For example, the tracking unit can record the type and time of the user's exercise. The tracking unit can also record the calories and nutrients of the user's meals. Furthermore, the tracking unit can record the user's sleep time and quality. For example, the tracking unit can record the type and time of the user's exercise. The calories and nutrients of the meals can also be recorded. The sleep time and quality can also be recorded. The generation AI analyzes these data and evaluates the user's health condition. For example, the generation AI can analyze the user's exercise data and evaluate the effect of the exercise. The generation AI can also analyze the user's dietary data and evaluate the nutritional balance. Furthermore, the generation AI can analyze the user's sleep data and evaluate the quality of the sleep. For example, the generation AI can analyze the user's exercise data and evaluate the effect of the exercise. The dietary data can also be analyzed and the nutritional balance can also be evaluated. The sleep data can also be analyzed and the quality of the sleep can also be evaluated. In this way, the user's daily activities and dietary contents are recorded and analyzed by the generation AI, so that progress can be tracked. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's daily activities and meal contents to the generation AI, which can then analyze them.

[0038] The tracking unit can track the user's degree of goal achievement and provide feedback to maintain motivation. The tracking unit, for example, tracks the user's degree of goal achievement. For example, the tracking unit can track the user's weight loss goal. Also, the tracking unit can track the user's degree of exercise frequency achievement. Furthermore, the tracking unit can track the user's diet improvement goal. For example, the tracking unit can track the user's weight loss goal. Also, the tracking unit can track the user's degree of exercise frequency achievement. Also, the tracking unit can track the diet improvement goal. The tracking unit tracks the user's degree of goal achievement and provides feedback to maintain motivation. For example, the tracking unit can provide an encouraging message based on the user's degree of goal achievement. Also, the tracking unit can suggest specific improvements based on the user's degree of goal achievement. Furthermore, the tracking unit can visually display the progress based on the user's degree of goal achievement. For example, the tracking unit can provide an encouraging message based on the user's degree of goal achievement. Also, specific improvements can be suggested. Also, the progress can be visually displayed. This makes it possible to track the user's degree of goal achievement and provide feedback to maintain motivation. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's goal achievement level to the generating AI, which can analyze it and provide feedback.

[0039] The reception unit can analyze the user's past medical checkup results and select the optimal input method. The reception unit, for example, analyzes the user's past medical checkup results. For example, the reception unit can analyze the user's past blood test results. The reception unit can also analyze the user's past weight changes. Furthermore, the reception unit can analyze the user's past electrocardiogram data. For example, the reception unit can analyze the user's past blood test results. The weight changes can also be analyzed. The electrocardiogram data can also be analyzed. The reception unit analyzes the past medical checkup results and selects the optimal input method. For example, the reception unit can suggest input in a similar format based on the format of the medical checkup results input by the user in the past. The reception unit can also preferentially suggest an input method (manual input, voice input, etc.) used by the user in the past. Furthermore, the reception unit can analyze the user's tendency to input in a specific time period based on the user's past input history and suggest the optimal input timing. For example, the reception unit can suggest input in a similar format based on the format of the medical checkup results input by the user in the past. The reception unit can also preferentially suggest an input method used in the past. It is also possible to analyze the tendency to input data during specific time periods based on the past input history and to suggest the optimal input timing. This makes it possible to select the optimal input method by analyzing the user's past health checkup results. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past health checkup results to the generation AI, which then analyzes the results and selects the optimal input method.

[0040] The reception unit can filter the results of the medical checkup based on the user's current health condition and lifestyle habits when the results of the medical checkup are input. The reception unit, for example, evaluates the user's current health condition. For example, the reception unit can evaluate the current health condition based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The reception unit can also evaluate the user's lifestyle habits (eating, exercise, sleep, etc.). The reception unit can also compare the user's past medical checkup results with the current health condition. For example, the reception unit can evaluate the current health condition based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The lifestyle habits can also be evaluated. The past medical checkup results and the current health condition can also be compared. The reception unit filters the results of the medical checkup based on the user's current health condition and lifestyle habits when the results of the medical checkup are input. For example, the reception unit can urge the user to input important items with priority based on the user's current health condition. The reception unit can also urge the user to input related medical checkup results with priority based on the user's lifestyle habits. The reception unit can compare the user's past medical checkup results with the current health condition and filter and input necessary items. For example, the reception unit can urge the user to input important items with priority based on the user's current health condition. It can also urge the user to input related health check results with priority based on lifestyle habits. It can also compare past health check results with the current health condition and filter and input necessary items. In this way, important items can be input with priority by filtering based on the user's current health condition and lifestyle habits. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current health condition and lifestyle habits to the generation AI, which can analyze the data and perform filtering.

[0041] The reception unit can preferentially input data with high relevance based on the geographical location information of the user when inputting the medical examination result. The reception unit considers, for example, the geographical location information of the user. For example, when the user lives in a specific area, the reception unit can urge the user to preferentially input data related to health risks common to that area. In addition, when the user is traveling, the reception unit can also urge the user to preferentially input data related to health risks at the travel destination. In addition, when the user lives in a specific climate condition, the reception unit can also urge the user to preferentially input data related to health risks related to that climate condition. For example, when the user lives in a specific area, the reception unit can urge the user to preferentially input data related to health risks common to that area. When traveling, the reception unit can also urge the user to preferentially input data related to health risks at the travel destination. When living in a specific climate condition, the reception unit can also urge the user to preferentially input data related to health risks related to that climate condition. In this way, by considering the geographical location information of the user, it is possible to preferentially input data with high relevance. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI, which can then analyze it and input highly relevant data preferentially.

[0042] The reception unit can analyze the social media activity of the user and input related data when inputting the medical examination result. The reception unit, for example, analyzes the social media activity of the user. For example, the reception unit can urge the user to input related medical examination results based on health information shared by the user on social media. The reception unit can also urge the user to input related medical examination results based on health-related accounts that the user follows on social media. Furthermore, the reception unit can urge the user to input related medical examination results based on health-related groups that the user participates in on social media. For example, the reception unit can urge the user to input related medical examination results based on health information shared by the user on social media. The reception unit can also urge the user to input related medical examination results based on health-related accounts that the user follows. The reception unit can also urge the user to input related medical examination results based on health-related groups that the user participates in. In this way, the user's social media activity can be analyzed to input related data. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity to the generation AI, which can analyze it and input related data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health checkup result during the analysis. The analysis unit, for example, evaluates the importance of the health checkup result. For example, the analysis unit can evaluate the importance of the health checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The analysis unit can also evaluate the importance of the health checkup result based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the analysis unit can compare the user's past health checkup result with the current health condition. For example, the analysis unit can evaluate the importance of the health checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The importance of the health checkup result can also be evaluated based on the lifestyle. The analysis unit can also compare the past health checkup result with the current health condition. The analysis unit adjusts the level of detail of the analysis based on the importance of the health checkup result. For example, the analysis unit can perform a detailed analysis on important health checkup results. The analysis unit can also perform a concise analysis on general health checkup results. Furthermore, the analysis unit can perform a particularly detailed analysis on results that may have a significant impact on the user's health condition. For example, the analysis unit can perform a detailed analysis of important health checkup results. A concise analysis can also be performed on general health checkup results. A particularly detailed analysis can also be performed on results that may have a significant impact on the user's health condition. This allows a detailed analysis of important results to be performed by adjusting the level of detail of the analysis based on the importance of the health checkup results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the health checkup results to the generation AI, which can analyze it and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the health check result during analysis. The analysis unit, for example, evaluates the category of the health check result. For example, the analysis unit can evaluate the category of the health check result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The analysis unit can also evaluate the category of the health check result based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the analysis unit can compare the user's past health check result with the current health condition. For example, the analysis unit can evaluate the category of the health check result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The health check result category can also be evaluated based on the lifestyle. The past health check result can also be compared with the current health condition. The analysis unit applies different analysis algorithms depending on the category of the health check result. For example, the analysis unit can apply a nutrition-specialized analysis algorithm to the health check result related to nutrition. The analysis unit can also apply an exercise-specialized analysis algorithm to the health check result related to exercise. Furthermore, the analysis unit can apply a sleep-specialized analysis algorithm to the health check result related to sleep. For example, the analysis unit can apply a nutrition-specific analysis algorithm to health check results related to nutrition. A physical exercise-specific analysis algorithm can be applied to health check results related to exercise. A sleep-specific analysis algorithm can be applied to health check results related to sleep. This allows for more accurate analysis results to be provided by applying different analysis algorithms depending on the category of the health check results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the health check results to the generation AI, which analyzes it and applies different analysis algorithms.

[0045] The analysis unit can determine the priority of the analysis based on the time of submission of the health checkup result at the time of analysis. The analysis unit, for example, evaluates the time of submission of the health checkup result. For example, the analysis unit can prioritize the analysis of the latest result based on the time of submission of the health checkup result of the user. The analysis unit can also prioritize the analysis of the health checkup result that is submitted periodically. Furthermore, the analysis unit can prioritize the analysis of the result that may have a significant impact on the health condition of the user. For example, the analysis unit can prioritize the analysis of the latest result based on the time of submission of the health checkup result of the user. The analysis unit can also prioritize the analysis of the health checkup result that is submitted periodically. Furthermore, the analysis unit can prioritize the analysis of the result that may have a significant impact on the health condition of the user. For example, the analysis unit can prioritize the analysis of the latest result based on the time of submission of the health checkup result of the user. The analysis unit can also prioritize the analysis of the health checkup result that is submitted periodically. Furthermore, the analysis unit can prioritize the analysis of the result that may have a significant impact on the health condition of the user. For example, the analysis unit can prioritize the analysis of the health checkup result that is submitted recently. The analysis unit can also prioritize the analysis of the health checkup result that is submitted periodically. Results that may have a significant impact on the health status can also be analyzed with priority. This allows the latest results to be analyzed with priority by determining the priority of analysis based on the time of submission of the health check results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the time of submission of the health check results to the generation AI, which can analyze it and determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the health checkup results during analysis. The analysis unit, for example, evaluates the relevance of the health checkup results. For example, the analysis unit can evaluate the relevance of the health checkup results based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The analysis unit can also evaluate the relevance of the health checkup results based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the analysis unit can compare the user's past health checkup results with the current health condition. For example, the analysis unit can evaluate the relevance of the health checkup results based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The relevance of the health checkup results can also be evaluated based on the lifestyle. The analysis unit can also compare the past health checkup results with the current health condition. The analysis unit adjusts the order of analysis based on the relevance of the health checkup results. For example, the analysis unit can prioritize analysis of important health checkup results. The analysis unit can also prioritize analysis of results that may have a significant impact on the user's health condition. Furthermore, the analysis unit can prioritize analysis of health checkup results related to the user's lifestyle. For example, the analysis unit can prioritize analysis of important health checkup results. Results that may have a significant impact on the health status can also be analyzed with priority. Health checkup results related to lifestyle habits can also be analyzed with priority. This allows important results to be analyzed with priority by adjusting the order of analysis based on the relevance of the health checkup results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the relevance of the health checkup results to the generation AI, which analyzes it and adjusts the order of analysis.

[0047] The providing unit can adjust the level of detail of the advice based on the importance of the medical checkup result when providing the advice. The providing unit, for example, evaluates the importance of the medical checkup result. For example, the providing unit can evaluate the importance of the medical checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The providing unit can also evaluate the importance of the medical checkup result based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the providing unit can compare the user's past medical checkup result with the current health condition. For example, the providing unit can evaluate the importance of the medical checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The importance of the medical checkup result can also be evaluated based on the lifestyle. The providing unit can also compare the past medical checkup result with the current health condition. The providing unit adjusts the level of detail of the advice based on the importance of the medical checkup result. For example, the providing unit can provide detailed advice for important medical checkup results. The providing unit can also provide concise advice for general medical checkup results. Furthermore, the providing unit can provide particularly detailed advice for results that may have a significant impact on the user's health condition. For example, the providing unit can provide detailed advice for important health check results. For general health check results, concise advice can also be provided. For results that may have a significant impact on the user's health condition, particularly detailed advice can be provided. This makes it possible to provide detailed advice for important results by adjusting the level of detail of the advice based on the importance of the health check results. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the importance of the health check results to the generating AI, which can analyze it and adjust the level of detail of the advice.

[0048] The providing unit can apply different advice algorithms according to the category of the medical checkup result when providing advice. The providing unit, for example, evaluates the category of the medical checkup result. For example, the providing unit can evaluate the category of the medical checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The providing unit can also evaluate the category of the medical checkup result based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the providing unit can compare the user's past medical checkup result with the current health condition. For example, the providing unit can evaluate the category of the medical checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The health checkup result category can also be evaluated based on the lifestyle. The past medical checkup result can also be compared with the current health condition. The providing unit applies different advice algorithms according to the category of the medical checkup result. For example, the providing unit can apply a nutrition-specialized advice algorithm to the medical checkup result related to nutrition. The providing unit can also apply an exercise-specialized advice algorithm to the medical checkup result related to exercise. Furthermore, the providing unit can apply a sleep-specialized advice algorithm to the medical checkup result related to sleep. For example, the providing unit can apply a nutrition-specialized advice algorithm to a medical checkup result related to nutrition. A physical exercise-specialized advice algorithm can be applied to a medical checkup result related to exercise. A sleep-specialized advice algorithm can be applied to a medical checkup result related to sleep. This allows more accurate advice to be provided by applying different advice algorithms depending on the category of the medical checkup result. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the category of the medical checkup result to the generating AI, which can analyze it and apply different advice algorithms.

[0049] The providing unit can determine the priority of advice based on the time of submission of the health check result when providing advice. The providing unit, for example, evaluates the time of submission of the health check result. For example, the providing unit can give priority to advice of the latest result based on the time of submission of the health check result of the user. Also, the providing unit can give priority to advice of the health check result that is submitted periodically. Furthermore, the providing unit can give priority to advice of the result that may have a significant impact on the health condition of the user. For example, the providing unit can give priority to advice of the latest result based on the time of submission of the health check result of the user. Also, the providing unit can give priority to advice of the health check result that is submitted periodically. Also, the providing unit can give priority to advice of the result that may have a significant impact on the health condition of the user. The providing unit determines the priority of advice based on the time of submission of the health check result. For example, the providing unit can give priority to advice of the health check result that was submitted recently. Also, the providing unit can give priority to advice of the health check result that is submitted periodically. Furthermore, the providing unit can give priority to advice of the result that may have a significant impact on the health condition of the user. For example, the providing unit can give priority to advice of the health check result that was submitted recently. Also, the providing unit can give priority to advice of the health check result that is submitted periodically. It is also possible to give priority to advice on results that may have a significant impact on the health condition. This allows advice based on the latest results to be provided by determining the priority of advice based on the time of submission of the health check results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the time of submission of the health check results to the generating AI, which can analyze it and determine the priority of advice.

[0050] The providing unit can adjust the order of advice based on the relevance of the medical checkup results when providing advice. The providing unit, for example, evaluates the relevance of the medical checkup results. For example, the providing unit can evaluate the relevance of the medical checkup results based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The providing unit can also evaluate the relevance of the medical checkup results based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the providing unit can compare the user's past medical checkup results with the current health condition. For example, the providing unit can evaluate the relevance of the medical checkup results based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The relevance of the medical checkup results can also be evaluated based on the lifestyle. The providing unit can also compare the past medical checkup results with the current health condition. The providing unit adjusts the order of advice based on the relevance of the medical checkup results. For example, the providing unit can provide advice based on important medical checkup results. The providing unit can also provide advice based on results that may have a significant impact on the user's health condition. Furthermore, the providing unit can provide advice based on medical checkup results related to the user's lifestyle. For example, the providing unit can provide advice based on important medical checkup results. Advice can also be provided based on results that may have a significant impact on the health status. Advice can also be provided based on health checkup results related to lifestyle habits. This allows advice to be provided preferentially for important results by adjusting the order of advice based on the relevance of the health checkup results. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the relevance of the health checkup results to the generating AI, which can analyze it and adjust the order of advice.

[0051] The tracking unit can select an optimal tracking method by referring to the user's past activity history when tracking progress. The tracking unit, for example, refers to the user's past activity history. For example, the tracking unit can refer to the user's past exercise records. The tracking unit can also refer to the user's past meal records. Furthermore, the tracking unit can refer to the user's past sleep records. For example, the tracking unit can refer to the user's past exercise records. The tracking unit can also refer to the meal records. The tracking unit can also refer to the sleep records. The tracking unit selects an optimal tracking method by referring to the past activity history. For example, the tracking unit can suggest a similar method based on a progress tracking method used by the user in the past. The tracking unit can also select the most effective progress tracking method from the user's past activity history. Furthermore, the tracking unit can analyze the user's past activity history and suggest an optimal tracking method. For example, the tracking unit can suggest a similar method based on a progress tracking method used by the user in the past. The most effective progress tracking method can be selected from the past activity history. The past activity history can also be analyzed and an optimal tracking method can be proposed. In this way, the optimal progress tracking method can be selected by referring to the user's past activity history. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's past activity history to the generation AI, which can analyze it and select the optimal tracking method.

[0052] The tracking unit can customize the tracking means based on the user's current living situation when tracking progress. The tracking unit, for example, evaluates the user's current living situation. For example, the tracking unit can evaluate the user's work schedule. The tracking unit can also evaluate the user's home environment. Furthermore, the tracking unit can evaluate the user's lifestyle habits (eating, exercise, sleep, etc.). For example, the tracking unit can evaluate the user's work schedule. The home environment can also be evaluated. The lifestyle habits can also be evaluated. The tracking unit customizes the tracking means based on the current living situation. For example, the tracking unit can provide a simple and quick progress tracking method when the user is busy. The tracking unit can also provide a detailed progress tracking method when the user is relaxed. Furthermore, the tracking unit can customize the optimal progress tracking means according to the user's living situation. For example, the tracking unit can provide a simple and quick progress tracking method when the user is busy. The tracking unit can also provide a detailed progress tracking method when the user is relaxed. The optimal progress tracking means can also be customized according to the living situation. Thereby, by customizing the tracking means based on the user's current living situation, appropriate progress tracking can be provided. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's current living situation to the generating AI, which can analyze it and customize the optimal progress tracking means.

[0053] The tracking unit can select an optimal tracking method in consideration of the geographical location information of the user when tracking progress. The tracking unit, for example, considers the geographical location information of the user. For example, if the user lives in a specific area, the tracking unit can provide a progress tracking method according to the characteristics of the area. In addition, if the user is traveling, the tracking unit can also provide a progress tracking method according to the characteristics of the travel destination. In addition, if the user lives in a specific climate condition, the tracking unit can also provide a progress tracking method according to the climate condition. For example, if the user lives in a specific area, the tracking unit can provide a progress tracking method according to the characteristics of the area. In the case of traveling, the tracking unit can also provide a progress tracking method according to the characteristics of the travel destination. In the case of living in a specific climate condition, the tracking unit can also provide a progress tracking method according to the climate condition. In this way, the optimal progress tracking method can be selected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit inputs the geographical location information of the user to the generation AI, which can analyze it to select the optimal progress tracking method.

[0054] The tracking unit can analyze the user's social media activity and suggest a tracking method when tracking progress. The tracking unit, for example, analyzes the user's social media activity. For example, the tracking unit can suggest a related progress tracking method based on activity information shared by the user on social media. The tracking unit can also suggest a related progress tracking method based on health-related accounts that the user follows on social media. Furthermore, the tracking unit can also suggest a related progress tracking method based on health-related groups that the user participates in on social media. For example, the tracking unit can suggest a related progress tracking method based on activity information shared by the user on social media. The tracking unit can also suggest a related progress tracking method based on health-related accounts that the user follows. The tracking unit can also suggest a related progress tracking method based on health-related groups that the user participates in. In this way, the tracking unit can suggest a related progress tracking method by analyzing the user's social media activity. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's social media activity to the generation AI, which can analyze it and suggest a tracking method.

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

[0056] When inputting the user's health checkup results, the reception unit can compare the user's past health checkup results with the user's current health condition to check the consistency of the input contents. For example, the reception unit can compare the user's past blood pressure data with the user's current blood pressure data to check whether there is any abnormal fluctuation. The reception unit can also compare the user's past weight data with the user's current weight data to check whether there is any sudden weight fluctuation. Furthermore, the reception unit can compare the user's past blood glucose level data with the user's current blood glucose level data to check whether there is any abnormal fluctuation. This can improve the reliability of the data by checking the consistency of the input contents of the user's health checkup results. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past health checkup results and current health condition data to the generation AI, which can analyze the data to check the consistency of the input contents.

[0057] The providing unit can monitor the intake of a specific nutrient in real time based on the user's health checkup results and provide advice as necessary. For example, the providing unit can monitor the user's vitamin D intake and, if there is a deficiency, suggest food ingredients that are rich in vitamin D. The providing unit can also monitor the user's iron intake and, if there is a deficiency, suggest food ingredients that are rich in iron. Furthermore, the providing unit can monitor the user's calcium intake and, if there is a deficiency, suggest food ingredients that are rich in calcium. This makes it possible to maintain nutritional balance by monitoring the user's intake of a specific nutrient in real time and providing advice as necessary. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the intake of a specific nutrient to the generating AI based on the user's health checkup results, and the generating AI can analyze it and provide advice.

[0058] When providing the user's exercise plan, the providing unit can propose optimal exercise locations and routes taking into account the user's geographical location information. For example, the providing unit can provide information on parks and gyms in the area where the user lives, and propose an exercise plan that can be done there. In addition, when the user is traveling, the providing unit can also provide information on exercise facilities and running courses at the travel destination. Furthermore, when the user lives in a specific climate condition, the providing unit can also propose an exercise plan suitable for that climate condition. For example, the providing unit can provide information on parks and gyms in the area where the user lives, and propose an exercise plan that can be done there. When traveling, the providing unit can also provide information on exercise facilities and running courses at the travel destination. When living in a specific climate condition, the providing unit can also propose an exercise plan suitable for that climate condition. This makes it possible to maximize the effect of exercise by proposing optimal exercise locations and routes taking into account the user's geographical location information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generating AI, which can analyze it and propose optimal exercise locations and routes.

[0059] The providing unit can monitor the user's sleep environment and suggest improvements as necessary to improve the quality of the user's sleep. For example, the providing unit can monitor the temperature and humidity of the user's bedroom and suggest adjusting the temperature and humidity to an appropriate level. The providing unit can also monitor the lighting and sound levels of the user's bedroom and suggest adjusting the lighting and sound levels to an appropriate level. Furthermore, the providing unit can monitor the state of the user's bedding and suggest using appropriate bedding. For example, the providing unit can monitor the temperature and humidity of the user's bedroom and suggest adjusting the temperature and humidity to an appropriate level. The providing unit can also monitor the lighting and sound levels of the bedroom and suggest adjusting the lighting and sound levels to an appropriate level. The providing unit can also monitor the state of the bedding and suggest using appropriate bedding. In this way, the quality of sleep can be improved by monitoring the user's sleep environment and suggesting improvements as necessary. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input data of the user's sleep environment to the generating AI, which can analyze the data and suggest improvements.

[0060] The tracking unit can analyze the user's social media activities and automatically import related data when recording the user's daily activities and dietary contents. For example, the tracking unit can automatically import photos of meals and records of exercise shared by the user on social media, and reflect them in the records of daily activities and dietary contents. The tracking unit can also import information from health-related accounts that the user follows on social media, and reflect them in the records of daily activities and dietary contents. The tracking unit can also import information from health-related groups that the user participates in on social media, and reflect them in the records of daily activities and dietary contents. For example, the tracking unit can automatically import photos of meals and records of exercise shared by the user on social media, and reflect them in the records of daily activities and dietary contents. Information from health-related accounts that the user follows can also be imported and reflected in the records of daily activities and dietary contents. Information from health-related groups that the user participates in can also be imported and reflected in the records of daily activities and dietary contents. In this way, the user's social media activities can be analyzed and related data can be automatically imported, and the recording of daily activities and dietary contents can be made more efficient. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input data about a user's social media activity into the generation AI, which can then analyze it and automatically import relevant data.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit receives the results of a medical checkup input by the user. The results of the medical checkup input by the user include, but are not limited to, blood pressure, blood glucose level, cholesterol level, and the like. The reception unit can directly receive data input manually by the user or submitted in digital form, for example. Step 2: The analysis unit uses the generation AI to analyze the health checkup results received by the reception unit. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the health checkup results and evaluate the user's health condition. The analysis unit can also use the generation AI to evaluate the user's health condition and identify areas that need improvement in areas such as nutrition, exercise, and sleep. For example, the generation AI provides advice to limit salt intake if blood pressure is high. Step 3: The providing unit provides individually customized advice based on the analysis results obtained by the analyzing unit. For example, the providing unit may suggest that the user review their diet and eat a balanced diet. The providing unit may also provide an appropriate exercise plan in the case of lack of exercise. Furthermore, the providing unit may provide advice for improving the quality of sleep. Step 4: The tracking unit tracks the user's progress based on the advice provided by the providing unit. For example, the tracking unit records the user's daily activities and dietary content, which are analyzed by the generating AI. The tracking unit can also track the user's goal achievement and provide feedback to maintain motivation. For example, the tracking unit maintains the user's motivation by setting exercise goals and tracking the achievement of those goals.

[0063] (Example 2) A health management system according to an embodiment of the present invention is a system that provides lifestyle advice that is individually customized based on the results of a health check. In this health management system, a user inputs the results of a health check, and a generation AI analyzes the results and provides advice that can be improved in everyday life, focusing on health such as nutrition, exercise, and sleep. In addition, progress is tracked through an app or the like to support the user in achieving their goals. For example, a user inputs the results of a health check. For example, data such as blood pressure, blood sugar level, and cholesterol level are input. This data is input into the generation AI. Next, the generation AI analyzes the input data. The generation AI evaluates the user's health condition and identifies areas that need improvement in each field such as nutrition, exercise, and sleep. For example, if blood pressure is high, advice to limit salt intake is provided. The generation AI provides individually customized advice based on the analysis results. For example, the system reviews the user's diet and suggests that the user eat a balanced diet. In addition, in the case of lack of exercise, an appropriate exercise plan is provided. In addition, advice to improve the quality of sleep is also provided. The user's progress is tracked through the app. The user records their daily activities and dietary content and inputs them into the app. The generative AI analyzes these data and supports the user in achieving their goals. For example, by setting exercise goals and tracking the progress of those goals, the user's motivation is maintained. This allows the user to understand his / her health condition and receive specific advice to improve lifestyle habits. For example, practical advice is provided in daily life, such as improving diet and establishing exercise habits. In addition, tracking progress through the app makes it easier for the user to maintain motivation to achieve their goals. This allows the health management system to understand the user's health condition and provide specific advice to improve lifestyle habits.

[0064] The health management system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a tracking unit. The reception unit receives the results of a medical checkup input by the user. The results of the medical checkup input by the user include, but are not limited to, blood pressure, blood glucose level, cholesterol level, and the like. The reception unit can, for example, receive data manually input by the user or directly submitted in digital form. The analysis unit uses a generation AI to analyze the results of the medical checkup received by the reception unit. The generation AI can, for example, use a text generation AI (for example, LLM) to analyze the results of the medical checkup and evaluate the health condition of the user. The analysis unit can also use the generation AI to evaluate the health condition of the user and identify areas that need improvement in each field, such as nutrition, exercise, and sleep. For example, the generation AI provides advice to limit salt intake when blood pressure is high. The provision unit provides individually customized advice based on the analysis results obtained by the analysis unit. For example, the provision unit recommends reviewing the user's diet and eating a balanced diet. The provision unit can also provide an appropriate exercise plan in the case of lack of exercise. Furthermore, the providing unit can also provide advice to improve the quality of sleep. For example, the providing unit suggests that the user review the dietary content and take a balanced diet. In the case of lack of exercise, an appropriate exercise plan can also be provided. Advice to improve the quality of sleep can also be provided. The tracking unit tracks the progress of the user based on the advice provided by the providing unit. For example, the tracking unit records the user's daily activities and dietary content, which are analyzed by the generating AI. The tracking unit can also track the user's goal achievement and provide feedback to maintain motivation. For example, the tracking unit maintains the user's motivation by setting an exercise goal and tracking the achievement level. As a result, the health management system according to the embodiment can grasp the user's health condition and provide specific advice to improve lifestyle habits. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI.For example, the tracking unit can input the user's daily activities and dietary details into the generation AI, which can then analyze them.

[0065] The reception unit allows the user to input the results of the medical checkup. The results of the medical checkup input by the user include, for example, blood pressure, blood glucose level, cholesterol level, etc., but are not limited to such examples. The reception unit can, for example, directly accept data manually input by the user or submitted in digital form. Specifically, the user can input the results of the medical checkup through a dedicated application or web portal. In the case of manual input, the user inputs a numerical value for each item, and the system automatically saves the data. In the case of submission in digital form, for example, an electronic medical record provided by a hospital or clinic or a PDF file of the medical checkup results can be uploaded. This allows the reception unit to enable the user to easily and quickly input the results of the medical checkup into the system. In addition, the reception unit also has a function for checking the accuracy of the input data. For example, it can detect abnormal values ​​or input errors and display an alert to prompt the user to recheck. Furthermore, in order to protect the privacy of the user, the reception unit encrypts and controls access to the data to prevent unauthorized access by a third party. This allows the reception unit to provide an environment in which the user can input the results of the medical checkup with peace of mind.

[0066] The analysis unit uses the generation AI to analyze the health check results received by the reception unit. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze the health check results and evaluate the user's health condition. Specifically, the generation AI analyzes the input numerical data and determines the normal range and abnormal values ​​for each item. For example, if the blood pressure is beyond the normal range, it determines that there is a risk of high blood pressure and provides appropriate advice. The generation AI can also grasp the trend of the user's health condition and evaluate long-term health risks by comparing it with past data and data of other users. Furthermore, the generation AI generates individually customized advice taking into account the user's lifestyle and environmental factors. For example, if the user is at risk of high blood pressure, it recommends dietary advice such as limiting salt intake and moderate exercise. The generation AI can also comprehensively evaluate the user's health condition and identify areas that need improvement in each area, such as nutrition, exercise, and sleep. This allows the analysis unit to evaluate the user's health condition in detail and provide specific and actionable advice. In addition, the analysis department continuously updates the generative AI's learning data to reflect the latest medical information and research results, ensuring highly accurate analysis at all times.

[0067] The providing unit provides individually customized advice based on the analysis results obtained by the analyzing unit. For example, the providing unit may review the user's diet and suggest a balanced diet. In addition, the providing unit may provide an appropriate exercise plan in the case of lack of exercise. Specifically, the providing unit creates a meal plan according to the user's health condition and lifestyle habits, and suggests specific menus and recipes. For example, for a user with high blood pressure, a low-salt diet and foods high in potassium are recommended. In addition, the providing unit may provide an appropriate exercise plan in consideration of the user's exercise habits. For example, for a user who is lacking in exercise, walking several times a week and light muscle training are recommended, and a specific exercise method and time are suggested. In addition, the providing unit may provide advice for improving the quality of sleep. For example, a method of relaxing before going to bed and a method of preparing an appropriate sleeping environment are suggested. In order to provide these pieces of advice to the user in an easy-to-understand manner, the providing unit provides information through a dedicated application or web portal. The user can improve his / her health condition by incorporating the provided advice into his / her daily life. In addition, the providing unit may collect feedback from the user and continuously improve the content of the advice. This enables the providing unit to provide optimal advice to the user and support health management.

[0068] The tracking unit tracks the progress of the user based on the advice provided by the providing unit. For example, the tracking unit records the user's daily activities and dietary content, and the generating AI analyzes it. Specifically, the user can record the daily dietary content, amount of exercise, sleep time, etc. through a dedicated application. The tracking unit collects these data, and the generating AI analyzes it to evaluate the user's goal achievement level. For example, it evaluates the extent to which the actual amount of exercise has been achieved with respect to the exercise goal set by the user, and provides feedback according to the achievement level. In addition, the tracking unit can provide rewards and encouraging messages according to the achievement level in order to maintain the user's motivation. Furthermore, the tracking unit can track changes in the user's health condition over the long term and review the content of the advice as necessary. For example, if the user's health condition has improved, a new goal is set and further health improvement is supported. In addition, the tracking unit can collect the user's feedback and use it to improve the entire system. As a result, the tracking unit can continuously support the user's health management and promote the improvement of the health condition. Furthermore, some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's daily activities and dietary details into the generation AI, which can then analyze them. This allows the tracking unit to efficiently and effectively support the user's health management.

[0069] The providing unit can provide suggestions of nutritional ingredients and recipes. The providing unit can suggest low-calorie ingredients and vitamin-rich recipes based on the user's health condition, for example. For example, the providing unit can suggest low-calorie ingredients based on the user's health condition. The providing unit can also suggest vitamin-rich recipes. Furthermore, the providing unit can also suggest a balanced meal based on the user's health condition. For example, the providing unit can suggest a balanced meal based on the user's health condition. This makes it possible to provide specific nutritional advice to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input low-calorie ingredients and vitamin-rich recipes to the generating AI based on the user's health condition, and the generating AI can analyze and suggest them.

[0070] The providing unit can provide an exercise plan according to the physical fitness level of the user. The providing unit proposes an appropriate exercise plan based on, for example, the physical fitness level of the user. For example, the providing unit can propose an exercise plan to improve cardiopulmonary function based on the physical fitness level of the user. The providing unit can also propose an exercise plan to improve muscle strength. Furthermore, the providing unit can also propose an exercise plan to improve flexibility. For example, the providing unit can propose an exercise plan to improve cardiopulmonary function based on the physical fitness level of the user. An exercise plan to improve muscle strength can also be proposed. An exercise plan to improve flexibility can also be proposed. This makes it possible to provide an appropriate exercise plan for the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input an appropriate exercise plan to the generating AI based on the physical fitness level of the user, and the generating AI can analyze and propose it.

[0071] The providing unit can provide advice for improving the quality of sleep. The providing unit provides advice for improving the quality of sleep, for example, based on the user's sleep state. For example, the providing unit can provide advice for increasing the user's sleep time. In addition, the providing unit can also provide advice for improving the depth of sleep. Furthermore, the providing unit can provide advice for improving the sleep environment. For example, the providing unit can provide advice for increasing the user's sleep time. Advice for improving the depth of sleep can also be provided. Advice for improving the sleep environment can also be provided. Thereby, advice for improving the quality of sleep can be provided to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input advice for improving the quality of sleep to the generating AI based on the user's sleep state, and the generating AI can analyze and propose it.

[0072] The tracking unit records the user's daily activities and dietary contents, and the generation AI can analyze them. The tracking unit records, for example, the user's daily activities and dietary contents. For example, the tracking unit can record the type and time of the user's exercise. The tracking unit can also record the calories and nutrients of the user's meals. Furthermore, the tracking unit can record the user's sleep time and quality. For example, the tracking unit can record the type and time of the user's exercise. The calories and nutrients of the meals can also be recorded. The sleep time and quality can also be recorded. The generation AI analyzes these data and evaluates the user's health condition. For example, the generation AI can analyze the user's exercise data and evaluate the effect of the exercise. The generation AI can also analyze the user's dietary data and evaluate the nutritional balance. Furthermore, the generation AI can analyze the user's sleep data and evaluate the quality of the sleep. For example, the generation AI can analyze the user's exercise data and evaluate the effect of the exercise. The dietary data can also be analyzed and the nutritional balance can also be evaluated. The sleep data can also be analyzed and the quality of the sleep can also be evaluated. In this way, the user's daily activities and dietary contents are recorded and analyzed by the generation AI, so that progress can be tracked. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's daily activities and meal contents to the generation AI, which can then analyze them.

[0073] The tracking unit can track the user's degree of goal achievement and provide feedback to maintain motivation. The tracking unit, for example, tracks the user's degree of goal achievement. For example, the tracking unit can track the user's weight loss goal. Also, the tracking unit can track the user's degree of exercise frequency achievement. Furthermore, the tracking unit can track the user's diet improvement goal. For example, the tracking unit can track the user's weight loss goal. Also, the tracking unit can track the user's degree of exercise frequency achievement. Also, the tracking unit can track the diet improvement goal. The tracking unit tracks the user's degree of goal achievement and provides feedback to maintain motivation. For example, the tracking unit can provide an encouraging message based on the user's degree of goal achievement. Also, the tracking unit can suggest specific improvements based on the user's degree of goal achievement. Furthermore, the tracking unit can visually display the progress based on the user's degree of goal achievement. For example, the tracking unit can provide an encouraging message based on the user's degree of goal achievement. Also, specific improvements can be suggested. Also, the progress can be visually displayed. This makes it possible to track the user's degree of goal achievement and provide feedback to maintain motivation. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's goal achievement level to the generating AI, which can analyze it and provide feedback.

[0074] The reception unit can estimate the user's emotion and adjust the input timing of the medical examination result based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also record the voice and estimate the emotion using a voice analysis technique. The reception unit can also collect the biometric data with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The reception unit adjusts the input timing of the medical examination result based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit can prompt the user to input the medical examination results during a time when the user can relax. Furthermore, when the user is relaxed, the reception unit can prompt the user to input the medical examination results immediately. Furthermore, when the user is busy, the reception unit can prompt the user to input the medical examination results during a time when the user is free. For example, when the user is feeling stressed, the reception unit can prompt the user to input the medical examination results during a time when the user can relax. When the user is relaxed, the reception unit can prompt the user to input the medical examination results immediately. When the user is busy, the reception unit can prompt the user to input the medical examination results during a time when the user is free. This allows the input efficiency to be improved by adjusting the input timing of the medical examination results according to the user's emotions. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data to the generation AI, which can analyze the data and adjust the input timing.

[0075] The reception unit can analyze the user's past medical checkup results and select the optimal input method. The reception unit, for example, analyzes the user's past medical checkup results. For example, the reception unit can analyze the user's past blood test results. The reception unit can also analyze the user's past weight changes. Furthermore, the reception unit can analyze the user's past electrocardiogram data. For example, the reception unit can analyze the user's past blood test results. The weight changes can also be analyzed. The electrocardiogram data can also be analyzed. The reception unit analyzes the past medical checkup results and selects the optimal input method. For example, the reception unit can suggest input in a similar format based on the format of the medical checkup results input by the user in the past. The reception unit can also preferentially suggest an input method (manual input, voice input, etc.) used by the user in the past. Furthermore, the reception unit can analyze the user's tendency to input in a specific time period based on the user's past input history and suggest the optimal input timing. For example, the reception unit can suggest input in a similar format based on the format of the medical checkup results input by the user in the past. The reception unit can also preferentially suggest an input method used in the past. It is also possible to analyze the tendency to input data during specific time periods based on the past input history and to suggest the optimal input timing. This makes it possible to select the optimal input method by analyzing the user's past health checkup results. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past health checkup results to the generation AI, which then analyzes the results and selects the optimal input method.

[0076] The reception unit can filter the results of the medical checkup based on the user's current health condition and lifestyle habits when the results of the medical checkup are input. The reception unit, for example, evaluates the user's current health condition. For example, the reception unit can evaluate the current health condition based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The reception unit can also evaluate the user's lifestyle habits (eating, exercise, sleep, etc.). The reception unit can also compare the user's past medical checkup results with the current health condition. For example, the reception unit can evaluate the current health condition based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The lifestyle habits can also be evaluated. The past medical checkup results and the current health condition can also be compared. The reception unit filters the results of the medical checkup based on the user's current health condition and lifestyle habits when the results of the medical checkup are input. For example, the reception unit can urge the user to input important items with priority based on the user's current health condition. The reception unit can also urge the user to input related medical checkup results with priority based on the user's lifestyle habits. The reception unit can compare the user's past medical checkup results with the current health condition and filter and input necessary items. For example, the reception unit can urge the user to input important items with priority based on the user's current health condition. It can also urge the user to input related health check results with priority based on lifestyle habits. It can also compare past health check results with the current health condition and filter and input necessary items. In this way, important items can be input with priority by filtering based on the user's current health condition and lifestyle habits. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current health condition and lifestyle habits to the generation AI, which can analyze the data and perform filtering.

[0077] The reception unit can estimate the user's emotion and determine the priority of the medical examination results to be input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The voice can also be recorded and the emotion can be estimated using a voice analysis technique. The biometric data can also be collected with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The reception unit determines the priority of the medical examination results to be input based on the estimated user's emotion. For example, when the user is stressed, the reception unit can urge the user to input important items with priority. Furthermore, when the user is relaxed, the reception unit can urge the user to input detailed items. Furthermore, when the user is busy, the reception unit can urge the user to input only the most important items. For example, when the user is stressed, the reception unit can urge the user to input important items with priority. When the user is relaxed, the reception unit can urge the user to input detailed items. When the user is busy, the reception unit can urge the user to input only the most important items. In this way, the priority of the medical examination results can be determined according to the user's emotion, so that important items can be input with priority. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data to the generation AI, which can analyze the data and determine the priority of the medical examination results to be input.

[0078] The reception unit can preferentially input data with high relevance based on the geographical location information of the user when inputting the medical examination result. The reception unit considers, for example, the geographical location information of the user. For example, when the user lives in a specific area, the reception unit can urge the user to preferentially input data related to health risks common to that area. In addition, when the user is traveling, the reception unit can also urge the user to preferentially input data related to health risks at the travel destination. In addition, when the user lives in a specific climate condition, the reception unit can also urge the user to preferentially input data related to health risks related to that climate condition. For example, when the user lives in a specific area, the reception unit can urge the user to preferentially input data related to health risks common to that area. When traveling, the reception unit can also urge the user to preferentially input data related to health risks at the travel destination. When living in a specific climate condition, the reception unit can also urge the user to preferentially input data related to health risks related to that climate condition. In this way, by considering the geographical location information of the user, it is possible to preferentially input data with high relevance. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI, which can then analyze it and input highly relevant data preferentially.

[0079] The reception unit can analyze the social media activity of the user and input related data when inputting the medical examination result. The reception unit, for example, analyzes the social media activity of the user. For example, the reception unit can urge the user to input related medical examination results based on health information shared by the user on social media. The reception unit can also urge the user to input related medical examination results based on health-related accounts that the user follows on social media. Furthermore, the reception unit can urge the user to input related medical examination results based on health-related groups that the user participates in on social media. For example, the reception unit can urge the user to input related medical examination results based on health information shared by the user on social media. The reception unit can also urge the user to input related medical examination results based on health-related accounts that the user follows. The reception unit can also urge the user to input related medical examination results based on health-related groups that the user participates in. In this way, the user's social media activity can be analyzed to input related data. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity to the generation AI, which can analyze it and input related data.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The voice can also be recorded and the emotion can be estimated using a voice analysis technology. The biometric data can also be collected with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The analysis unit adjusts the expression method of the analysis based on the estimated user's emotion. For example, the analysis unit can use a simple and easy-to-understand expression method when the user is stressed. In addition, the analysis unit can provide detailed analysis results when the user is relaxed. In addition, the analysis unit can use a visually attractive expression method when the user is excited. For example, the analysis unit can use a simple and easy-to-understand expression method when the user is stressed. In the case of relaxation, detailed analysis results can be provided. In the case of excitement, a visually attractive expression method can be used. In this way, by adjusting the expression method of the analysis according to the user's emotions, it is possible to provide an easy-to-understand analysis result. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data to the generation AI, which can analyze it and adjust the expression method of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the health checkup result during the analysis. The analysis unit, for example, evaluates the importance of the health checkup result. For example, the analysis unit can evaluate the importance of the health checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The analysis unit can also evaluate the importance of the health checkup result based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the analysis unit can compare the user's past health checkup result with the current health condition. For example, the analysis unit can evaluate the importance of the health checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The importance of the health checkup result can also be evaluated based on the lifestyle. The analysis unit can also compare the past health checkup result with the current health condition. The analysis unit adjusts the level of detail of the analysis based on the importance of the health checkup result. For example, the analysis unit can perform a detailed analysis on important health checkup results. The analysis unit can also perform a concise analysis on general health checkup results. Furthermore, the analysis unit can perform a particularly detailed analysis on results that may have a significant impact on the user's health condition. For example, the analysis unit can perform a detailed analysis of important health checkup results. A concise analysis can also be performed on general health checkup results. A particularly detailed analysis can also be performed on results that may have a significant impact on the user's health condition. This allows a detailed analysis of important results to be performed by adjusting the level of detail of the analysis based on the importance of the health checkup results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the health checkup results to the generation AI, which can analyze it and adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the health check result during analysis. The analysis unit, for example, evaluates the category of the health check result. For example, the analysis unit can evaluate the category of the health check result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The analysis unit can also evaluate the category of the health check result based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the analysis unit can compare the user's past health check result with the current health condition. For example, the analysis unit can evaluate the category of the health check result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The health check result category can also be evaluated based on the lifestyle. The past health check result can also be compared with the current health condition. The analysis unit applies different analysis algorithms depending on the category of the health check result. For example, the analysis unit can apply a nutrition-specialized analysis algorithm to the health check result related to nutrition. The analysis unit can also apply an exercise-specialized analysis algorithm to the health check result related to exercise. Furthermore, the analysis unit can apply a sleep-specialized analysis algorithm to the health check result related to sleep. For example, the analysis unit can apply a nutrition-specific analysis algorithm to health check results related to nutrition. A physical exercise-specific analysis algorithm can be applied to health check results related to exercise. A sleep-specific analysis algorithm can be applied to health check results related to sleep. This allows for more accurate analysis results to be provided by applying different analysis algorithms depending on the category of the health check results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the health check results to the generation AI, which analyzes it and applies different analysis algorithms.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The voice can also be recorded and the emotion can be estimated using a voice analysis technique. The biometric data can also be collected with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The analysis unit adjusts the length of the analysis based on the estimated user's emotion. For example, the analysis unit can provide a short and to the point analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually appealing analysis result when the user is excited. For example, the analysis unit can provide a short and to the point analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually appealing analysis result when the user is excited. This allows the analysis result of an appropriate length to be provided by adjusting the length of the analysis according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's emotion data to the generation AI, which can analyze it and adjust the length of the analysis.

[0084] The analysis unit can determine the priority of the analysis based on the time of submission of the health checkup result at the time of analysis. The analysis unit, for example, evaluates the time of submission of the health checkup result. For example, the analysis unit can prioritize the analysis of the latest result based on the time of submission of the health checkup result of the user. The analysis unit can also prioritize the analysis of the health checkup result that is submitted periodically. Furthermore, the analysis unit can prioritize the analysis of the result that may have a significant impact on the health condition of the user. For example, the analysis unit can prioritize the analysis of the latest result based on the time of submission of the health checkup result of the user. The analysis unit can also prioritize the analysis of the health checkup result that is submitted periodically. Furthermore, the analysis unit can prioritize the analysis of the result that may have a significant impact on the health condition of the user. For example, the analysis unit can prioritize the analysis of the latest result based on the time of submission of the health checkup result of the user. The analysis unit can also prioritize the analysis of the health checkup result that is submitted periodically. Furthermore, the analysis unit can prioritize the analysis of the result that may have a significant impact on the health condition of the user. For example, the analysis unit can prioritize the analysis of the health checkup result that is submitted recently. The analysis unit can also prioritize the analysis of the health checkup result that is submitted periodically. Results that may have a significant impact on the health status can also be analyzed with priority. This allows the latest results to be analyzed with priority by determining the priority of analysis based on the time of submission of the health check results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the time of submission of the health check results to the generation AI, which can analyze it and determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the health checkup results during analysis. The analysis unit, for example, evaluates the relevance of the health checkup results. For example, the analysis unit can evaluate the relevance of the health checkup results based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The analysis unit can also evaluate the relevance of the health checkup results based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the analysis unit can compare the user's past health checkup results with the current health condition. For example, the analysis unit can evaluate the relevance of the health checkup results based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The relevance of the health checkup results can also be evaluated based on the lifestyle. The analysis unit can also compare the past health checkup results with the current health condition. The analysis unit adjusts the order of analysis based on the relevance of the health checkup results. For example, the analysis unit can prioritize analysis of important health checkup results. The analysis unit can also prioritize analysis of results that may have a significant impact on the user's health condition. Furthermore, the analysis unit can prioritize analysis of health checkup results related to the user's lifestyle. For example, the analysis unit can prioritize analysis of important health checkup results. Results that may have a significant impact on the health status can also be analyzed with priority. Health checkup results related to lifestyle habits can also be analyzed with priority. This allows important results to be analyzed with priority by adjusting the order of analysis based on the relevance of the health checkup results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the relevance of the health checkup results to the generation AI, which analyzes it and adjusts the order of analysis.

[0086] The providing unit can estimate the user's emotion and adjust the way of expressing advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The voice can also be recorded and the emotion can be estimated using a voice analysis technology. The biometric data can also be collected with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The providing unit adjusts the way of expressing advice based on the estimated user's emotion. For example, the providing unit can provide simple and easy-to-understand advice when the user is stressed. In addition, the providing unit can provide detailed advice when the user is relaxed. In addition, the providing unit can provide visually attractive advice when the user is excited. For example, the providing unit can provide simple and easy-to-understand advice when the user is stressed. In addition, the providing unit can provide detailed advice when the user is relaxed. In addition, the providing unit can provide visually attractive advice when the user is excited. In this way, by adjusting the way of expressing advice according to the user's emotions, it is possible to provide easy-to-understand advice. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data to the generating AI, which can analyze the data and adjust the way of expressing advice.

[0087] The providing unit can adjust the level of detail of the advice based on the importance of the medical checkup result when providing the advice. The providing unit, for example, evaluates the importance of the medical checkup result. For example, the providing unit can evaluate the importance of the medical checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The providing unit can also evaluate the importance of the medical checkup result based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the providing unit can compare the user's past medical checkup result with the current health condition. For example, the providing unit can evaluate the importance of the medical checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The importance of the medical checkup result can also be evaluated based on the lifestyle. The providing unit can also compare the past medical checkup result with the current health condition. The providing unit adjusts the level of detail of the advice based on the importance of the medical checkup result. For example, the providing unit can provide detailed advice for important medical checkup results. The providing unit can also provide concise advice for general medical checkup results. Furthermore, the providing unit can provide particularly detailed advice for results that may have a significant impact on the user's health condition. For example, the providing unit can provide detailed advice for important health check results. For general health check results, concise advice can also be provided. For results that may have a significant impact on the user's health condition, particularly detailed advice can be provided. This makes it possible to provide detailed advice for important results by adjusting the level of detail of the advice based on the importance of the health check results. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the importance of the health check results to the generating AI, which can analyze it and adjust the level of detail of the advice.

[0088] The providing unit can apply different advice algorithms according to the category of the medical checkup result when providing advice. The providing unit, for example, evaluates the category of the medical checkup result. For example, the providing unit can evaluate the category of the medical checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The providing unit can also evaluate the category of the medical checkup result based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the providing unit can compare the user's past medical checkup result with the current health condition. For example, the providing unit can evaluate the category of the medical checkup result based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The health checkup result category can also be evaluated based on the lifestyle. The past medical checkup result can also be compared with the current health condition. The providing unit applies different advice algorithms according to the category of the medical checkup result. For example, the providing unit can apply a nutrition-specialized advice algorithm to the medical checkup result related to nutrition. The providing unit can also apply an exercise-specialized advice algorithm to the medical checkup result related to exercise. Furthermore, the providing unit can apply a sleep-specialized advice algorithm to the medical checkup result related to sleep. For example, the providing unit can apply a nutrition-specialized advice algorithm to a medical checkup result related to nutrition. A physical exercise-specialized advice algorithm can be applied to a medical checkup result related to exercise. A sleep-specialized advice algorithm can be applied to a medical checkup result related to sleep. This allows more accurate advice to be provided by applying different advice algorithms depending on the category of the medical checkup result. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the category of the medical checkup result to the generating AI, which can analyze it and apply different advice algorithms.

[0089] The providing unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The voice can also be recorded and the emotion can be estimated using a voice analysis technology. The biometric data can also be collected with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The providing unit adjusts the length of the advice based on the estimated user's emotion. For example, the providing unit can provide advice that is short and to the point when the user is in a hurry. The providing unit can also provide detailed advice when the user is relaxed. Furthermore, the providing unit can provide visually attractive advice when the user is excited. For example, the providing unit can provide advice that is short and to the point when the user is in a hurry. Detailed advice can be provided when the user is relaxed. Visually attractive advice can be provided when the user is excited. This allows the length of advice to be adjusted according to the user's emotions, so that advice of an appropriate length can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data to the generating AI, which can analyze the data and adjust the length of the advice.

[0090] The providing unit can determine the priority of advice based on the time of submission of the health check result when providing advice. The providing unit, for example, evaluates the time of submission of the health check result. For example, the providing unit can give priority to advice of the latest result based on the time of submission of the health check result of the user. Also, the providing unit can give priority to advice of the health check result that is submitted periodically. Furthermore, the providing unit can give priority to advice of the result that may have a significant impact on the health condition of the user. For example, the providing unit can give priority to advice of the latest result based on the time of submission of the health check result of the user. Also, the providing unit can give priority to advice of the health check result that is submitted periodically. Also, the providing unit can give priority to advice of the result that may have a significant impact on the health condition of the user. The providing unit determines the priority of advice based on the time of submission of the health check result. For example, the providing unit can give priority to advice of the health check result that was submitted recently. Also, the providing unit can give priority to advice of the health check result that is submitted periodically. Furthermore, the providing unit can give priority to advice of the result that may have a significant impact on the health condition of the user. For example, the providing unit can give priority to advice of the health check result that was submitted recently. Also, the providing unit can give priority to advice of the health check result that is submitted periodically. It is also possible to give priority to advice on results that may have a significant impact on the health condition. This allows advice based on the latest results to be provided by determining the priority of advice based on the time of submission of the health check results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the time of submission of the health check results to the generating AI, which can analyze it and determine the priority of advice.

[0091] The providing unit can adjust the order of advice based on the relevance of the medical checkup results when providing advice. The providing unit, for example, evaluates the relevance of the medical checkup results. For example, the providing unit can evaluate the relevance of the medical checkup results based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The providing unit can also evaluate the relevance of the medical checkup results based on the user's lifestyle (diet, exercise, sleep, etc.). Furthermore, the providing unit can compare the user's past medical checkup results with the current health condition. For example, the providing unit can evaluate the relevance of the medical checkup results based on data such as the user's blood pressure, blood glucose level, and cholesterol level. The relevance of the medical checkup results can also be evaluated based on the lifestyle. The providing unit can also compare the past medical checkup results with the current health condition. The providing unit adjusts the order of advice based on the relevance of the medical checkup results. For example, the providing unit can provide advice based on important medical checkup results. The providing unit can also provide advice based on results that may have a significant impact on the user's health condition. Furthermore, the providing unit can provide advice based on medical checkup results related to the user's lifestyle. For example, the providing unit can provide advice based on important medical checkup results. Advice can also be provided based on results that may have a significant impact on the health status. Advice can also be provided based on health checkup results related to lifestyle habits. This allows advice to be provided preferentially for important results by adjusting the order of advice based on the relevance of the health checkup results. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the relevance of the health checkup results to the generating AI, which can analyze it and adjust the order of advice.

[0092] The tracking unit can estimate the user's emotion and adjust the progress tracking method based on the estimated user's emotion. The tracking unit, for example, estimates the user's emotion. For example, the tracking unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The tracking unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the tracking unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the tracking unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The voice can also be recorded and the emotion can be estimated using a voice analysis technology. The biometric data can also be collected with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The tracking unit adjusts the progress tracking method based on the estimated user's emotion. For example, the tracking unit can provide a simple and easy-to-understand progress tracking method when the user is stressed. The tracking unit can also provide a detailed progress tracking method when the user is relaxed. The tracking unit can also provide a visually appealing progress tracking method when the user is excited. For example, the tracking unit can provide a simple and easy-to-understand progress tracking method when the user is stressed. The tracking unit can also provide a detailed progress tracking method when the user is relaxed. The tracking unit can also provide a visually appealing progress tracking method when the user is excited. This makes it possible to provide easy-to-understand progress tracking by adjusting the progress tracking method according to the user's emotions. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the user's emotion data to the generation AI, which can analyze the data and adjust the progress tracking method.

[0093] The tracking unit can select an optimal tracking method by referring to the user's past activity history when tracking progress. The tracking unit, for example, refers to the user's past activity history. For example, the tracking unit can refer to the user's past exercise records. The tracking unit can also refer to the user's past meal records. Furthermore, the tracking unit can refer to the user's past sleep records. For example, the tracking unit can refer to the user's past exercise records. The tracking unit can also refer to the meal records. The tracking unit can also refer to the sleep records. The tracking unit selects an optimal tracking method by referring to the past activity history. For example, the tracking unit can suggest a similar method based on a progress tracking method used by the user in the past. The tracking unit can also select the most effective progress tracking method from the user's past activity history. Furthermore, the tracking unit can analyze the user's past activity history and suggest an optimal tracking method. For example, the tracking unit can suggest a similar method based on a progress tracking method used by the user in the past. The most effective progress tracking method can be selected from the past activity history. The past activity history can also be analyzed and an optimal tracking method can be proposed. In this way, the optimal progress tracking method can be selected by referring to the user's past activity history. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's past activity history to the generation AI, which can analyze it and select the optimal tracking method.

[0094] The tracking unit can customize the tracking means based on the user's current living situation when tracking progress. The tracking unit, for example, evaluates the user's current living situation. For example, the tracking unit can evaluate the user's work schedule. The tracking unit can also evaluate the user's home environment. Furthermore, the tracking unit can evaluate the user's lifestyle habits (eating, exercise, sleep, etc.). For example, the tracking unit can evaluate the user's work schedule. The home environment can also be evaluated. The lifestyle habits can also be evaluated. The tracking unit customizes the tracking means based on the current living situation. For example, the tracking unit can provide a simple and quick progress tracking method when the user is busy. The tracking unit can also provide a detailed progress tracking method when the user is relaxed. Furthermore, the tracking unit can customize the optimal progress tracking means according to the user's living situation. For example, the tracking unit can provide a simple and quick progress tracking method when the user is busy. The tracking unit can also provide a detailed progress tracking method when the user is relaxed. The optimal progress tracking means can also be customized according to the living situation. Thereby, by customizing the tracking means based on the user's current living situation, appropriate progress tracking can be provided. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's current living situation to the generating AI, which can analyze it and customize the optimal progress tracking means.

[0095] The tracking unit can estimate the user's emotion and determine the priority of progress based on the estimated user's emotion. The tracking unit, for example, estimates the user's emotion. For example, the tracking unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The tracking unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the tracking unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the tracking unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The voice can also be recorded and the emotion can be estimated using a voice analysis technology. The biometric data can also be collected with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The tracking unit determines the priority of progress based on the estimated user's emotion. For example, the tracking unit can prioritize tracking of important progress when the user is stressed. In addition, the tracking unit can also track detailed progress when the user is relaxed. In addition, the tracking unit can track only the most important progress when the user is busy. For example, the tracking unit can prioritize tracking of important progress when the user is stressed. In addition, the tracking unit can also track detailed progress when the user is relaxed. In addition, the tracking unit can track only the most important progress when the user is busy. In this way, important progress can be prioritized by determining the priority of progress according to the user's emotions. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the user's emotion data to the generation AI, which can analyze the data and determine the priority of progress.

[0096] The tracking unit can select an optimal tracking method in consideration of the geographical location information of the user when tracking progress. The tracking unit, for example, considers the geographical location information of the user. For example, if the user lives in a specific area, the tracking unit can provide a progress tracking method according to the characteristics of the area. In addition, if the user is traveling, the tracking unit can also provide a progress tracking method according to the characteristics of the travel destination. In addition, if the user lives in a specific climate condition, the tracking unit can also provide a progress tracking method according to the climate condition. For example, if the user lives in a specific area, the tracking unit can provide a progress tracking method according to the characteristics of the area. In the case of traveling, the tracking unit can also provide a progress tracking method according to the characteristics of the travel destination. In the case of living in a specific climate condition, the tracking unit can also provide a progress tracking method according to the climate condition. In this way, the optimal progress tracking method can be selected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit inputs the geographical location information of the user to the generation AI, which can analyze it to select the optimal progress tracking method.

[0097] The tracking unit can analyze the user's social media activity and suggest a tracking method when tracking progress. The tracking unit, for example, analyzes the user's social media activity. For example, the tracking unit can suggest a related progress tracking method based on activity information shared by the user on social media. The tracking unit can also suggest a related progress tracking method based on health-related accounts that the user follows on social media. Furthermore, the tracking unit can also suggest a related progress tracking method based on health-related groups that the user participates in on social media. For example, the tracking unit can suggest a related progress tracking method based on activity information shared by the user on social media. The tracking unit can also suggest a related progress tracking method based on health-related accounts that the user follows. The tracking unit can also suggest a related progress tracking method based on health-related groups that the user participates in. In this way, the tracking unit can suggest a related progress tracking method by analyzing the user's social media activity. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's social media activity to the generation AI, which can analyze it and suggest a tracking method.

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

[0099] When inputting the user's health checkup results, the reception unit can compare the user's past health checkup results with the user's current health condition to check the consistency of the input contents. For example, the reception unit can compare the user's past blood pressure data with the user's current blood pressure data to check whether there is any abnormal fluctuation. The reception unit can also compare the user's past weight data with the user's current weight data to check whether there is any sudden weight fluctuation. Furthermore, the reception unit can compare the user's past blood glucose level data with the user's current blood glucose level data to check whether there is any abnormal fluctuation. This can improve the reliability of the data by checking the consistency of the input contents of the user's health checkup results. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past health checkup results and current health condition data to the generation AI, which can analyze the data to check the consistency of the input contents.

[0100] The providing unit can monitor the intake of a specific nutrient in real time based on the user's health checkup results and provide advice as necessary. For example, the providing unit can monitor the user's vitamin D intake and, if there is a deficiency, suggest food ingredients that are rich in vitamin D. The providing unit can also monitor the user's iron intake and, if there is a deficiency, suggest food ingredients that are rich in iron. Furthermore, the providing unit can monitor the user's calcium intake and, if there is a deficiency, suggest food ingredients that are rich in calcium. This makes it possible to maintain nutritional balance by monitoring the user's intake of a specific nutrient in real time and providing advice as necessary. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the intake of a specific nutrient to the generating AI based on the user's health checkup results, and the generating AI can analyze it and provide advice.

[0101] When providing the user's exercise plan, the providing unit can propose optimal exercise locations and routes taking into account the user's geographical location information. For example, the providing unit can provide information on parks and gyms in the area where the user lives, and propose an exercise plan that can be done there. In addition, when the user is traveling, the providing unit can also provide information on exercise facilities and running courses at the travel destination. Furthermore, when the user lives in a specific climate condition, the providing unit can also propose an exercise plan suitable for that climate condition. For example, the providing unit can provide information on parks and gyms in the area where the user lives, and propose an exercise plan that can be done there. When traveling, the providing unit can also provide information on exercise facilities and running courses at the travel destination. When living in a specific climate condition, the providing unit can also propose an exercise plan suitable for that climate condition. This makes it possible to maximize the effect of exercise by proposing optimal exercise locations and routes taking into account the user's geographical location information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generating AI, which can analyze it and propose optimal exercise locations and routes.

[0102] The providing unit can monitor the user's sleep environment and suggest improvements as necessary to improve the quality of the user's sleep. For example, the providing unit can monitor the temperature and humidity of the user's bedroom and suggest adjusting the temperature and humidity to an appropriate level. The providing unit can also monitor the lighting and sound levels of the user's bedroom and suggest adjusting the lighting and sound levels to an appropriate level. Furthermore, the providing unit can monitor the state of the user's bedding and suggest using appropriate bedding. For example, the providing unit can monitor the temperature and humidity of the user's bedroom and suggest adjusting the temperature and humidity to an appropriate level. The providing unit can also monitor the lighting and sound levels of the bedroom and suggest adjusting the lighting and sound levels to an appropriate level. The providing unit can also monitor the state of the bedding and suggest using appropriate bedding. In this way, the quality of sleep can be improved by monitoring the user's sleep environment and suggesting improvements as necessary. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input data of the user's sleep environment to the generating AI, which can analyze the data and suggest improvements.

[0103] The tracking unit can analyze the user's social media activities and automatically import related data when recording the user's daily activities and dietary contents. For example, the tracking unit can automatically import photos of meals and records of exercise shared by the user on social media, and reflect them in the records of daily activities and dietary contents. The tracking unit can also import information from health-related accounts that the user follows on social media, and reflect them in the records of daily activities and dietary contents. The tracking unit can also import information from health-related groups that the user participates in on social media, and reflect them in the records of daily activities and dietary contents. For example, the tracking unit can automatically import photos of meals and records of exercise shared by the user on social media, and reflect them in the records of daily activities and dietary contents. Information from health-related accounts that the user follows can also be imported and reflected in the records of daily activities and dietary contents. Information from health-related groups that the user participates in can also be imported and reflected in the records of daily activities and dietary contents. In this way, the user's social media activities can be analyzed and related data can be automatically imported, and the recording of daily activities and dietary contents can be made more efficient. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input data about a user's social media activity into the generation AI, which can then analyze it and automatically import relevant data.

[0104] The reception unit can estimate the user's emotion and customize the input method of the medical examination result based on the estimated user's emotion. For example, the reception unit can provide a simple and quick input method when the user is stressed. Furthermore, the reception unit can provide a detailed input method when the user is relaxed. Furthermore, the reception unit can provide a visually attractive input method when the user is excited. For example, the reception unit can provide a simple and quick input method when the user is stressed. When the user is relaxed, a detailed input method can be provided. When the user is excited, a visually attractive input method can be provided. This allows the input method of the medical examination result to be customized according to the user's emotion, thereby improving the efficiency of input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's emotion data to the generation AI, which can analyze the data and customize the input method.

[0105] The analysis unit can estimate the user's emotion and adjust the notification method of the analysis result based on the estimated user's emotion. For example, the analysis unit can provide a simple and easy-to-understand notification method when the user is stressed. Furthermore, the analysis unit can provide a detailed notification method when the user is relaxed. Furthermore, the analysis unit can provide a visually attractive notification method when the user is excited. For example, the analysis unit can provide a simple and easy-to-understand notification method when the user is stressed. When the user is relaxed, a detailed notification method can be provided. When the user is excited, a visually attractive notification method can be provided. In this way, by adjusting the notification method of the analysis result according to the user's emotion, it is possible to provide an easy-to-understand notification. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data to the generation AI, which can analyze it and adjust the notification method.

[0106] The providing unit can estimate the user's emotion and adjust the timing of advice based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide advice during a time period when the user can relax. Furthermore, when the user is relaxed, the providing unit can provide advice immediately. Furthermore, when the user is busy, the providing unit can provide advice during a time period when the user is free. For example, when the user is feeling stressed, the providing unit can provide advice during a time period when the user can relax. When the user is relaxed, the advice can be provided immediately. When the user is busy, the advice can be provided during a time period when the user is free. In this way, by adjusting the timing of advice according to the user's emotion, effective advice can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data to the generating AI, which can analyze the data and adjust the timing of advice.

[0107] The tracking unit can estimate the user's emotions and adjust the progress feedback method based on the estimated user's emotions. For example, the tracking unit can provide a simple and encouraging message when the user is stressed. The tracking unit can also provide detailed feedback when the user is relaxed. The tracking unit can also provide visually attractive feedback when the user is excited. For example, the tracking unit can provide a simple and encouraging message when the user is stressed. The tracking unit can also provide detailed feedback when the user is relaxed. The tracking unit can also provide visually attractive feedback when the user is excited. This makes it possible to provide easy-to-understand feedback by adjusting the progress feedback method according to the user's emotions. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the user's emotion data to the generation AI, which can analyze the data and adjust the feedback method.

[0108] The tracking unit can estimate the user's emotions and adjust the progress reporting frequency based on the estimated user's emotions. For example, when the user is stressed, the tracking unit can reduce the reporting frequency to reduce the burden. In addition, when the user is relaxed, the tracking unit can increase the reporting frequency to provide detailed progress. In addition, when the user is excited, the tracking unit can provide frequent visually appealing reports. For example, when the user is stressed, the tracking unit can reduce the reporting frequency to reduce the burden. When the user is relaxed, the tracking unit can increase the reporting frequency to provide detailed progress. When the user is excited, the tracking unit can provide frequent visually appealing reports. In this way, the progress reporting frequency can be adjusted according to the user's emotions, so that the progress can be reported at an appropriate frequency. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's emotion data to the generation AI, which can analyze the data and adjust the reporting frequency.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception unit receives the results of a medical checkup input by the user. The results of the medical checkup input by the user include, but are not limited to, blood pressure, blood glucose level, cholesterol level, and the like. The reception unit can directly receive data input manually by the user or submitted in digital form, for example. Step 2: The analysis unit uses the generation AI to analyze the health checkup results received by the reception unit. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the health checkup results and evaluate the user's health condition. The analysis unit can also use the generation AI to evaluate the user's health condition and identify areas that need improvement in areas such as nutrition, exercise, and sleep. For example, the generation AI provides advice to limit salt intake if blood pressure is high. Step 3: The providing unit provides individually customized advice based on the analysis results obtained by the analyzing unit. For example, the providing unit may suggest that the user review their diet and eat a balanced diet. The providing unit may also provide an appropriate exercise plan in the case of lack of exercise. Furthermore, the providing unit may provide advice for improving the quality of sleep. Step 4: The tracking unit tracks the user's progress based on the advice provided by the providing unit. For example, the tracking unit records the user's daily activities and dietary content, which are analyzed by the generating AI. The tracking unit can also track the user's goal achievement and provide feedback to maintain motivation. For example, the tracking unit maintains the user's motivation by setting exercise goals and tracking the achievement of those goals.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and tracking unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user inputs the results of the health check. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the results of the health check using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides individually customized advice. The tracking unit is realized, for example, by the control unit 46A of the smart device 14, and tracks the progress of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and tracking unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user inputs the results of the health check. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the results of the health check using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides individually customized advice. The tracking unit is realized, for example, by the control unit 46A of the smart glasses 214, and tracks the progress of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and tracking unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user inputs the results of the health check. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the results of the health check using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides individually customized advice. The tracking unit is realized, for example, by the control unit 46A of the headset type terminal 314, and tracks the progress of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and tracking unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user inputs the results of the health check. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the results of the health check using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides individually customized advice. The tracking unit is realized, for example, by the control unit 46A of the robot 414, and tracks the progress of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A reception unit that receives input of health examination results; an analysis unit that analyzes the medical examination results received by the reception unit; a providing unit that provides individually customized advice based on the analysis result obtained by the analysis unit; a tracking unit that tracks a user's progress based on the advice provided by the providing unit. A system characterized by: (Appendix 2) The providing unit is Providing nutritional ingredient and recipe suggestions 2. The system of claim 1, (Appendix 3) The providing unit is Providing exercise plans according to the user's physical fitness level 2. The system of claim 1, (Appendix 4) The providing unit is Providing advice to improve sleep quality 2. The system of claim 1, (Appendix 5) The tracking unit is The user's daily activities and diet are recorded and analyzed by AI. 2. The system of claim 1, (Appendix 6) The tracking unit is Track user progress and provide feedback to keep them motivated 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust the timing of inputting a medical examination result based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past health check results and select the input method 2. The system of claim 1, (Appendix 9) The reception unit is When entering health checkup results, filtering is performed based on the user's current health condition and lifestyle habits. 2. The system of claim 1, (Appendix 10) The reception unit is To estimate a user's emotion and determine a priority of a medical examination result to be input based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is When entering medical examination results, the system prioritizes data that is more relevant based on the user's geographic location. 2. The system of claim 1, (Appendix 12) The reception unit is Analyze users' social media activity and enter relevant data when entering health check results 2. The system of claim 1, (Appendix 13) The analysis unit includes: Infer user sentiment and adjust analytics presentation based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit includes: During analysis, adjust the level of detail of the analysis based on the importance of the health check results 2. The system of claim 1, (Appendix 15) The analysis unit includes: During analysis, different analysis algorithms are applied depending on the category of the health examination result. 2. The system of claim 1, (Appendix 16) The analysis unit includes: Inferring user sentiment and adjusting the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit includes: During analysis, prioritize analysis based on when medical examination results were submitted 2. The system of claim 1, (Appendix 18) The analysis unit includes: During analysis, adjust the order of analysis based on the relevance of the health test results 2. The system of claim 1, (Appendix 19) The providing unit is Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The providing unit is When providing advice, adjust the level of detail of the advice based on the importance of the health check results. 2. The system of claim 1, (Appendix 21) The providing unit is When providing advice, different advice algorithms are applied depending on the category of the health check result. 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The providing unit is When providing advice, prioritize advice based on when medical examination results were submitted 2. The system of claim 1, (Appendix 24) The providing unit is When providing advice, adjust the order of advice based on the relevance of the health check results 2. The system of claim 1, (Appendix 25) The tracking unit is Inferring a user's emotions and adjusting how progress is tracked based on the inferred user emotions 2. The system of claim 1, (Appendix 26) The tracking unit is When tracking progress, choose the best tracking method by looking at the user's past activity history 2. The system of claim 1, (Appendix 27) The tracking unit is When tracking progress, customize the tracking method based on the user's current life situation 2. The system of claim 1, (Appendix 28) The tracking unit is Estimate the user's emotions and prioritize progress based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The tracking unit is When tracking progress, consider the user's geographic location to find the best way to track progress 2. The system of claim 1, (Appendix 30) The tracking unit is When tracking progress, analyze users' social media activity to suggest ways to track progress 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. A reception unit that receives input of health examination results; an analysis unit that analyzes the medical examination results received by the reception unit; a providing unit that provides individually customized advice based on the analysis result obtained by the analysis unit; a tracking unit that tracks a user's progress based on the advice provided by the providing unit. A system characterized by:

2. The providing unit is Providing nutritional ingredient and recipe suggestions 2. The system of claim 1 .

3. The providing unit is Providing exercise plans according to the user's physical fitness level 2. The system of claim 1 .

4. The providing unit is Providing advice to improve sleep quality 2. The system of claim 1 .

5. The tracking unit is The user's daily activities and dietary habits are recorded and analyzed by AI.

2. The system of claim 1 .

6. The tracking unit is Track user progress and provide feedback to keep them motivated 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and adjust the timing of inputting a medical examination result based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past health check results and select the input method 2. The system of claim 1 .

9. The reception unit is When entering health checkup results, filtering is performed based on the user's current health condition and lifestyle habits.

2. The system of claim 1 .

Images