system

Abstract

The system according to this embodiment aims to provide customized advice by utilizing individual health data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a presentation unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides customized advice based on the analysis results obtained by the analysis unit. The presentation unit visually presents the user's health data based on the advice provided by the provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently carried out to effectively utilize individual health data to provide customized advice, and there is room for improvement.

[0005] The system according to the embodiment aims to provide customized advice by utilizing individual health data.

Means for Solving the Problems

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

[0007] The system according to this embodiment can provide customized advice by utilizing individual health data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is an innovative platform that comprehensively supports individual health management. This health management system assists users in improving their lifestyles by continuously tracking health data and providing customized advice. Aimed at health-conscious individuals and busy modern people, it proposes health strategies that are easy to incorporate into daily life and aims to spread awareness of preventive medicine. The health management system utilizes the advanced analytical capabilities of generative AI to provide practical advice based on the user's data. This system makes it easier to maintain a healthy lifestyle and helps to accurately understand individual health conditions. For example, the health management system continuously tracks the user's health data. This includes hospital interviews, health checkup results, smartphone pedometer readings, sleep duration, restaurant payment information, and heart rate. Next, the generative AI analyzes this data and evaluates the user's health condition. For example, it provides customized health advice based on the user's dietary preferences and exercise habits. Furthermore, the generative AI proposes specific action plans for preventive medicine based on the user's health data. For example, meal plans to increase the intake of specific nutrients or exercise programs for stress management. This allows users to take concrete actions toward their health goals. Furthermore, the generating AI visually presents the user's health data, making it easier to understand trends and changes. This allows users to make data-driven decisions. For example, they can see how their current health status has changed compared to past data. In this way, the health management system comprehensively supports individual health management and provides specific advice for users to maintain a healthy lifestyle. This enables health-conscious individuals and busy modern people to implement health strategies that are easy to incorporate into their daily lives and to spread awareness of preventive medicine. In short, the health management system comprehensively supports users' health management and provides specific advice for maintaining a healthy lifestyle.

[0029] The health management system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a presentation unit. The collection unit collects health data. The collection unit can collect data such as hospital interviews, health checkup results, smartphone pedometer data, sleep duration, restaurant payment information, and pulse rate. For example, the collection unit can obtain hospital interview data from electronic medical records. The collection unit can also directly obtain health checkup results from medical institutions. Furthermore, the collection unit can obtain smartphone pedometer data from applications. For example, the collection unit automatically collects data from a smartphone pedometer app. The collection unit can also obtain sleep duration data from wearable devices. For example, the collection unit automatically collects sleep duration data from wearable devices. The collection unit can also obtain restaurant payment information from payment apps. For example, the collection unit automatically collects restaurant payment information from payment apps. The collection unit can also obtain pulse rate data from medical devices. For example, the collection unit automatically collects pulse rate data from medical devices. The analysis unit analyzes the data collected by the data collection unit using generative AI. For example, the analysis unit evaluates the user's health status based on the collected data. For example, the analysis unit analyzes the user's dietary preferences using generative AI. The analysis unit can also analyze the user's exercise habits using generative AI. Furthermore, the analysis unit can analyze the user's sleep patterns using generative AI. For example, the analysis unit analyzes the user's sleep patterns using generative AI and evaluates the quality of sleep. The service unit provides customized advice based on the analysis results obtained by the analysis unit. For example, the service unit provides customized health advice based on the user's dietary preferences and exercise habits. For example, the service unit provides a meal plan to increase the intake of specific nutrients based on the user's dietary preferences. The service unit can also provide an exercise program for stress management based on the user's exercise habits. Furthermore, the service unit can propose specific action plans for preventive medicine based on the user's health data. For example, the service unit proposes a meal plan to increase the intake of specific nutrients based on the user's health data.The presentation unit visually presents the user's health data based on the advice provided by the supply unit. For example, the presentation unit visually presents the user's health data using graphs or charts. For example, the presentation unit visually presents the user's health data using graphs to make it easier to grasp trends and changes. The presentation unit can also visually present the user's health data using charts to facilitate data-driven decision-making. As a result, the health management system according to the embodiment can comprehensively support the user's health management and provide specific advice for maintaining a healthy lifestyle.

[0030] The data collection unit collects health data. For example, it can collect data such as hospital interviews, health checkup results, smartphone pedometer readings, sleep duration, restaurant payment information, and pulse rate. Specifically, hospital interview data is obtained from electronic medical records and includes the patient's symptoms, medical history, and current treatment. This allows for a detailed understanding of the medical examination at the healthcare facility. Health checkup results include detailed health information such as blood tests, urine tests, electrocardiograms, and X-rays, and this data is obtained directly from healthcare facilities. Smartphone pedometer data is important for understanding the user's daily exercise level and is automatically collected from applications. For example, a smartphone pedometer app records the number of steps and distance traveled by the user in a day, and this data is periodically acquired by the data collection unit. Sleep duration data is obtained from wearable devices and used to evaluate the user's sleep patterns and sleep quality. Wearable devices are worn on the user's wrist or arm and provide accurate sleep data by monitoring movement and heart rate during sleep. Payment information from dining out is crucial for understanding users' eating habits and is automatically collected from payment apps. This includes information such as menu items, calorie intake, and nutrient content from meals. Pulse rate data is acquired from medical devices and used to assess users' heart rate and cardiac health. This data is automatically collected from medical devices and centrally managed by a data collection unit. The unit collects this diverse data in real time and transmits it to a central database, enabling a comprehensive understanding of the user's health. Furthermore, the unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. This enables the unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes data collected by the data collection department using generative AI. Specifically, it evaluates the user's health status based on the collected data. Generative AI can learn from vast datasets and perform pattern recognition and predictive analysis. For example, to analyze a user's dietary preferences, the generative AI analyzes payment information from dining out and meal records to identify the user's preferred ingredients and cooking tendencies. Generative AI can also analyze a user's exercise habits. It analyzes smartphone pedometer data and exercise app records to evaluate the user's exercise frequency and intensity. Furthermore, the generative AI can analyze a user's sleep patterns. It analyzes sleep data acquired from wearable devices to evaluate the user's sleep quality and sleep cycle. For example, the generative AI analyzes a user's sleep patterns and evaluates the proportion of deep sleep and light sleep, the number of nighttime awakenings, etc., to make a comprehensive judgment on sleep quality. This allows the analysis department to quickly and accurately analyze the collected data and comprehensively evaluate the user's health status. In addition, the analysis department can utilize historical data and statistical information to evaluate long-term health risks and perform trend analysis. For example, based on past health checkup data, the system can predict fluctuations in specific health risks and develop measures for future health management. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor health status in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The service provider offers customized advice based on the analysis results obtained by the analysis provider. Specifically, it provides customized health advice based on the user's dietary preferences and exercise habits. For example, it may provide a meal plan to increase the intake of specific nutrients based on the user's dietary preferences. The generating AI analyzes the user's dietary history and nutritional data to create a balanced meal plan. The service provider can also provide exercise programs for stress management based on the user's exercise habits. The generating AI analyzes the user's exercise data and suggests appropriate exercise intensity and frequency. Furthermore, the service provider can propose specific action plans for preventive medicine based on the user's health data. For example, it may propose a meal plan to increase the intake of specific nutrients based on the user's health data. The generating AI assesses the user's health risks and proposes specific actions for preventive health management. This allows the service provider to provide customized advice tailored to the user's health condition and provide concrete support for maintaining a healthy lifestyle. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can provide feedback on the results of following the provided advice and revise the advice based on that data. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through email, SMS, and voice calls. This allows the service provider to quickly and reliably provide users with customized advice and support them in leading a healthy lifestyle.

[0033] The presentation unit visually presents the user's health data based on the advice provided by the service provider. Specifically, it visually presents the user's health data in graphs and charts to make it easier to grasp trends and changes. For example, it displays data such as the user's steps, exercise level, sleep duration, and diet in graphs, allowing users to see daily changes and trends at a glance. It can also visually present the user's health data in charts to facilitate data-driven decision-making. For example, it displays the user's health checkup results in charts, visually showing the numerical values ​​of each item and comparisons with reference values ​​to make it easier to understand the user's health status. Furthermore, the presentation unit enables users to interactively manipulate their health data, allowing them to gain a deeper understanding of their own health status. For example, it provides an interface that allows users to select data for a specific period to view details or compare different datasets. This allows the presentation unit to support users in visually understanding their health status and making data-driven decisions. In addition, the presentation unit can collect user feedback and continuously improve the displayed content and interface. For example, users can submit comments and questions about the displayed data, and the displayed content can be reviewed based on that feedback. Furthermore, the display unit is compatible with multiple devices and platforms, allowing users to access their health data anytime, anywhere. For example, by enabling users to view the same data on devices such as smartphones, tablets, and personal computers, user convenience is enhanced. This allows the display unit to provide users with visually and interactively presented health data, supporting their health management.

[0034] The data collection unit can collect data such as hospital interview results, health checkup results, smartphone pedometer data, sleep duration, restaurant payment information, and pulse rate. For example, the data collection unit can obtain hospital interview data from electronic medical records. The data collection unit can also obtain health checkup results directly from medical institutions. The data collection unit can also obtain smartphone pedometer data from applications. For example, the data collection unit automatically collects data from smartphone pedometer apps. The data collection unit can also obtain sleep duration data from wearable devices. For example, the data collection unit automatically collects sleep duration data from wearable devices. The data collection unit can also obtain restaurant payment information from payment apps. For example, the data collection unit automatically collects restaurant payment information from payment apps. The data collection unit can also obtain pulse rate data from medical devices. For example, the data collection unit automatically collects pulse rate data from medical devices. By collecting diverse health data in this way, the user's health status can be comprehensively understood. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input hospital medical interview data into a generating AI and have the generating AI perform analysis of the medical interview data.

[0035] The analysis unit can analyze collected data using generative AI and evaluate the user's health status. For example, the analysis unit can evaluate the user's health status based on the collected data. For example, the analysis unit can use generative AI to analyze the user's dietary preferences. The analysis unit can also use generative AI to analyze the user's exercise habits. Furthermore, the analysis unit can use generative AI to analyze the user's sleep patterns. For example, the analysis unit can use generative AI to analyze the user's sleep patterns and evaluate the quality of sleep. This improves the accuracy of health status evaluation by using generative AI. Generative AI is implemented using technologies such as deep learning and natural language processing. Some or all of the above-mentioned processes in the analysis unit are performed using generative AI. For example, the analysis unit can input collected data into the generative AI and have the generative AI perform the evaluation of the user's health status.

[0036] The service provider can provide customized health advice based on the user's dietary preferences and exercise habits. For example, the service provider can provide a meal plan to increase the intake of specific nutrients based on the user's dietary preferences. The service provider can also provide an exercise program for stress management based on the user's exercise habits. This allows for the provision of health advice tailored to the user's individual needs. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's dietary preferences and exercise habits into a generating AI and have the generating AI generate customized health advice.

[0037] The service provider can propose specific action plans for preventive medicine based on the user's health data. For example, the service provider can propose a meal plan to increase the intake of specific nutrients based on the user's health data. The service provider can also propose an exercise program for stress management based on the user's health data. In this way, by proposing specific action plans for preventive medicine, it supports the user in maintaining their health. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI generate an action plan for preventive medicine.

[0038] The presentation unit can visually present the user's health data, making it easier to grasp trends and changes. For example, the presentation unit can visually present the user's health data using graphs or charts. The presentation unit can visually present the user's health data using graphs, making it easier to grasp trends and changes. The presentation unit can also visually present the user's health data using charts, making it easier to make data-driven decisions. This makes it easier for users to make data-driven decisions by visually presenting their health data. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's health data into a generating AI and have the generating AI perform the generation of the visual presentation.

[0039] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. The data collection unit can also adjust the collection frequency based on the user's past collection history to perform efficient data collection. The data collection unit can also analyze the user's past collection history and customize the collection method. This enables efficient data collection by analyzing past collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data collection history into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the data collection unit can prioritize collecting diet-related data. If the user is interested in exercise, the data collection unit can also prioritize collecting exercise-related data. If the user is interested in stress management, the data collection unit can also prioritize collecting stress-related data. This makes it possible to collect data according to the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting health data. For example, if the user is traveling, the data collection unit can prioritize the collection of health data from their travel destination. If the user is at home, the data collection unit can also prioritize the collection of health data from their home area. If the user is at work, the data collection unit can also prioritize the collection of health data from their workplace area. This enables more accurate health management by collecting highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts about health on social media, the data collection unit can collect data based on the content of that post. The data collection unit can also collect data based on the content of a user's exercise post on social media. The data collection unit can also collect data based on the content of a user's diet post on social media. This allows for health management tailored to the user's interests by collecting relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit will perform a detailed analysis on important health data. The analysis unit can also perform a simplified analysis on less important health data. The analysis unit can also determine the priority of the analysis according to the importance of the health data. This allows for efficient data analysis by adjusting the level of detail of the analysis according to the importance of the health data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input the importance of the health data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a nutrient analysis algorithm to dietary data. It can also apply an exercise volume analysis algorithm to exercise data. It can also apply a sleep quality analysis algorithm to sleep data. By applying an analysis algorithm appropriate to the category of health data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the categories of health data into the generative AI and have the generative AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit can also analyze current data while referring to past data. The analysis unit can also adjust the priority of analysis according to when the health data was collected. This enables efficient data analysis by determining the priority of analysis based on when the health data was collected. Some or all of the above processes in the analysis unit are performed using generative AI. For example, the analysis unit can input the health data collection period into the generative AI and have the generative AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of health data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of health data. Some or all of the above processes in the analysis unit are performed using generative AI. For example, the analysis unit can input the relevance of health data into the generative AI and have the generative AI perform the adjustment of the analysis order.

[0047] The service provider can adjust the level of detail of advice based on the importance of the health data when providing advice. For example, the service provider can provide detailed advice for important health data. For less important health data, the service provider can also provide concise advice. The service provider can also determine the priority of advice according to the importance of the health data. This allows for efficient advice provision by adjusting the level of detail of advice according to the importance of the health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the advice.

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

[0049] The service provider can determine the priority of advice based on the timing of health data collection when providing advice. For example, the service provider can provide advice based on recently collected data. The service provider can also provide advice based on current data while referring to past data. The service provider can also adjust the priority of advice according to the timing of health data collection. This enables efficient advice provision by determining the priority of advice based on the timing of health data collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the timing of health data collection into a generating AI and have the generating AI determine the priority of advice.

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

[0051] The display unit can select the optimal display method by referring to the user's past health data when displaying data. For example, the display unit can suggest the optimal display method based on the display method the user has preferred to use in the past. The display unit can also analyze the user's past data display history and select a display method with high visibility. The display unit can also refer to the user's past health data and provide a display method that highlights trends and changes. This makes it possible to display data optimally by referring to past health data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past health data into a generating AI and have the generating AI select the optimal display method.

[0052] The display unit can apply different display algorithms depending on the category of health data when displaying data. For example, for dietary data, the display unit can apply an algorithm that visually displays the balance of nutrients. For exercise data, the display unit can also apply an algorithm that visually displays the amount of exercise and calories burned. For sleep data, the display unit can also apply an algorithm that visually displays the quality and duration of sleep. By applying a display algorithm according to the category of health data, it becomes possible to display data with higher visibility. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the category of health data into a generating AI and have the generating AI execute the application of different display algorithms.

[0053] The display unit can select the optimal display method when displaying data, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. This enables highly visible data display by selecting the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0054] The display unit can adjust the display order based on the relevance of health data when displaying data. For example, the display unit can display highly relevant data first. The display unit can also postpone displaying less relevant data. The display unit can also adjust the display order according to the relevance of health data. This allows for efficient data display by adjusting the display order based on the relevance of health data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the display order.

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

[0056] A health management system can analyze a user's past health data and predict future health risks. For example, it can predict potential health risks based on past dietary and exercise data. It can also predict the risk of future sleep disorders by analyzing past sleep data. Furthermore, it can predict future stress-related health risks based on past stress data. This allows users to take proactive measures against future health risks.

[0057] A health management system can set personalized health goals based on a user's health data. For example, it can set an appropriate weight target based on the user's current health status. It can also set an appropriate exercise target based on the user's exercise habits. Furthermore, it can set an appropriate nutrition target based on the user's eating habits. This allows users to take concrete actions toward their own health goals.

[0058] A health management system can provide community features based on users' health data. For example, it can match users with similar health goals and encourage each other. It can also provide forums for sharing health-related information. Furthermore, it can host health events and challenges, allowing users to compete with each other. This makes health management more enjoyable for users through the community.

[0059] A health management system can provide educational content about health based on users' health data. For example, it can provide information on nutrition to help users choose healthy foods. It can also provide information on exercise to help users exercise effectively. Furthermore, it can provide information on stress management to help users manage stress effectively. This allows users to deepen their knowledge about health.

[0060] A health management system can provide health-related reminders based on the user's health data. For example, it can set reminders to encourage regular exercise, meal times, and even sleep schedules. This allows users to receive support in maintaining healthy lifestyle habits.

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

[0062] Step 1: The data collection unit collects health data. For example, it obtains medical interview data from electronic medical records at hospitals and health checkup results directly from medical institutions. It also automatically collects pedometer data from smartphones via applications and obtains sleep duration data from wearable devices. Furthermore, it automatically collects payment information for dining out from payment apps and obtains pulse rate data from medical devices. Step 2: The analysis unit analyzes the data collected by the collection unit using generating AI. For example, it evaluates the user's health status and analyzes their dietary preferences, exercise habits, and sleep patterns. This allows for a comprehensive understanding of the user's health status. Step 3: The service provider provides customized advice based on the analysis results obtained by the analysis provider. For example, they may provide a meal plan to increase the intake of specific nutrients or an exercise program for stress management based on the user's dietary preferences and exercise habits. They can also propose specific action plans for preventive medicine. Step 4: The presentation unit visually presents the user's health data based on the advice provided by the delivery unit. For example, the user's health data may be visually presented using graphs and charts to make it easier to understand trends and changes. This makes it easier to make data-driven decisions.

[0063] (Example of form 2) The health management system according to an embodiment of the present invention is an innovative platform that comprehensively supports individual health management. This health management system assists users in improving their lifestyles by continuously tracking health data and providing customized advice. Aimed at health-conscious individuals and busy modern people, it proposes health strategies that are easy to incorporate into daily life and aims to spread awareness of preventive medicine. The health management system utilizes the advanced analytical capabilities of generative AI to provide practical advice based on the user's data. This system makes it easier to maintain a healthy lifestyle and helps to accurately understand individual health conditions. For example, the health management system continuously tracks the user's health data. This includes hospital interviews, health checkup results, smartphone pedometer readings, sleep duration, restaurant payment information, and heart rate. Next, the generative AI analyzes this data and evaluates the user's health condition. For example, it provides customized health advice based on the user's dietary preferences and exercise habits. Furthermore, the generative AI proposes specific action plans for preventive medicine based on the user's health data. For example, meal plans to increase the intake of specific nutrients or exercise programs for stress management. This allows users to take concrete actions toward their health goals. Furthermore, the generating AI visually presents the user's health data, making it easier to understand trends and changes. This allows users to make data-driven decisions. For example, they can see how their current health status has changed compared to past data. In this way, the health management system comprehensively supports individual health management and provides specific advice for users to maintain a healthy lifestyle. This enables health-conscious individuals and busy modern people to implement health strategies that are easy to incorporate into their daily lives and to spread awareness of preventive medicine. In short, the health management system comprehensively supports users' health management and provides specific advice for maintaining a healthy lifestyle.

[0064] The health management system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a presentation unit. The collection unit collects health data. The collection unit can collect data such as hospital interviews, health checkup results, smartphone pedometer data, sleep duration, restaurant payment information, and pulse rate. For example, the collection unit can obtain hospital interview data from electronic medical records. The collection unit can also directly obtain health checkup results from medical institutions. Furthermore, the collection unit can obtain smartphone pedometer data from applications. For example, the collection unit automatically collects data from a smartphone pedometer app. The collection unit can also obtain sleep duration data from wearable devices. For example, the collection unit automatically collects sleep duration data from wearable devices. The collection unit can also obtain restaurant payment information from payment apps. For example, the collection unit automatically collects restaurant payment information from payment apps. The collection unit can also obtain pulse rate data from medical devices. For example, the collection unit automatically collects pulse rate data from medical devices. The analysis unit analyzes the data collected by the data collection unit using generative AI. For example, the analysis unit evaluates the user's health status based on the collected data. For example, the analysis unit analyzes the user's dietary preferences using generative AI. The analysis unit can also analyze the user's exercise habits using generative AI. Furthermore, the analysis unit can analyze the user's sleep patterns using generative AI. For example, the analysis unit analyzes the user's sleep patterns using generative AI and evaluates the quality of sleep. The service unit provides customized advice based on the analysis results obtained by the analysis unit. For example, the service unit provides customized health advice based on the user's dietary preferences and exercise habits. For example, the service unit provides a meal plan to increase the intake of specific nutrients based on the user's dietary preferences. The service unit can also provide an exercise program for stress management based on the user's exercise habits. Furthermore, the service unit can propose specific action plans for preventive medicine based on the user's health data. For example, the service unit proposes a meal plan to increase the intake of specific nutrients based on the user's health data.The presentation unit visually presents the user's health data based on the advice provided by the supply unit. For example, the presentation unit visually presents the user's health data using graphs or charts. For example, the presentation unit visually presents the user's health data using graphs to make it easier to grasp trends and changes. The presentation unit can also visually present the user's health data using charts to facilitate data-driven decision-making. As a result, the health management system according to the embodiment can comprehensively support the user's health management and provide specific advice for maintaining a healthy lifestyle.

[0065] The data collection unit collects health data. For example, it can collect data such as hospital interviews, health checkup results, smartphone pedometer readings, sleep duration, restaurant payment information, and pulse rate. Specifically, hospital interview data is obtained from electronic medical records and includes the patient's symptoms, medical history, and current treatment. This allows for a detailed understanding of the medical examination at the healthcare facility. Health checkup results include detailed health information such as blood tests, urine tests, electrocardiograms, and X-rays, and this data is obtained directly from healthcare facilities. Smartphone pedometer data is important for understanding the user's daily exercise level and is automatically collected from applications. For example, a smartphone pedometer app records the number of steps and distance traveled by the user in a day, and this data is periodically acquired by the data collection unit. Sleep duration data is obtained from wearable devices and used to evaluate the user's sleep patterns and sleep quality. Wearable devices are worn on the user's wrist or arm and provide accurate sleep data by monitoring movement and heart rate during sleep. Payment information from dining out is crucial for understanding users' eating habits and is automatically collected from payment apps. This includes information such as menu items, calorie intake, and nutrient content from meals. Pulse rate data is acquired from medical devices and used to assess users' heart rate and cardiac health. This data is automatically collected from medical devices and centrally managed by a data collection unit. The unit collects this diverse data in real time and transmits it to a central database, enabling a comprehensive understanding of the user's health. Furthermore, the unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. This enables the unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis department analyzes data collected by the data collection department using generative AI. Specifically, it evaluates the user's health status based on the collected data. Generative AI can learn from vast datasets and perform pattern recognition and predictive analysis. For example, to analyze a user's dietary preferences, the generative AI analyzes payment information from dining out and meal records to identify the user's preferred ingredients and cooking tendencies. Generative AI can also analyze a user's exercise habits. It analyzes smartphone pedometer data and exercise app records to evaluate the user's exercise frequency and intensity. Furthermore, the generative AI can analyze a user's sleep patterns. It analyzes sleep data acquired from wearable devices to evaluate the user's sleep quality and sleep cycle. For example, the generative AI analyzes a user's sleep patterns and evaluates the proportion of deep sleep and light sleep, the number of nighttime awakenings, etc., to make a comprehensive judgment on sleep quality. This allows the analysis department to quickly and accurately analyze the collected data and comprehensively evaluate the user's health status. In addition, the analysis department can utilize historical data and statistical information to evaluate long-term health risks and perform trend analysis. For example, based on past health checkup data, the system can predict fluctuations in specific health risks and develop measures for future health management. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor health status in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0067] The service provider offers customized advice based on the analysis results obtained by the analysis provider. Specifically, it provides customized health advice based on the user's dietary preferences and exercise habits. For example, it may provide a meal plan to increase the intake of specific nutrients based on the user's dietary preferences. The generating AI analyzes the user's dietary history and nutritional data to create a balanced meal plan. The service provider can also provide exercise programs for stress management based on the user's exercise habits. The generating AI analyzes the user's exercise data and suggests appropriate exercise intensity and frequency. Furthermore, the service provider can propose specific action plans for preventive medicine based on the user's health data. For example, it may propose a meal plan to increase the intake of specific nutrients based on the user's health data. The generating AI assesses the user's health risks and proposes specific actions for preventive health management. This allows the service provider to provide customized advice tailored to the user's health condition and provide concrete support for maintaining a healthy lifestyle. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can provide feedback on the results of following the provided advice and revise the advice based on that data. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through email, SMS, and voice calls. This allows the service provider to quickly and reliably provide users with customized advice and support them in leading a healthy lifestyle.

[0068] The presentation unit visually presents the user's health data based on the advice provided by the service provider. Specifically, it visually presents the user's health data in graphs and charts to make it easier to grasp trends and changes. For example, it displays data such as the user's steps, exercise level, sleep duration, and diet in graphs, allowing users to see daily changes and trends at a glance. It can also visually present the user's health data in charts to facilitate data-driven decision-making. For example, it displays the user's health checkup results in charts, visually showing the numerical values ​​of each item and comparisons with reference values ​​to make it easier to understand the user's health status. Furthermore, the presentation unit enables users to interactively manipulate their health data, allowing them to gain a deeper understanding of their own health status. For example, it provides an interface that allows users to select data for a specific period to view details or compare different datasets. This allows the presentation unit to support users in visually understanding their health status and making data-driven decisions. In addition, the presentation unit can collect user feedback and continuously improve the displayed content and interface. For example, users can submit comments and questions about the displayed data, and the displayed content can be reviewed based on that feedback. Furthermore, the display unit is compatible with multiple devices and platforms, allowing users to access their health data anytime, anywhere. For example, by enabling users to view the same data on devices such as smartphones, tablets, and personal computers, user convenience is enhanced. This allows the display unit to provide users with visually and interactively presented health data, supporting their health management.

[0069] The data collection unit can collect data such as hospital interview results, health checkup results, smartphone pedometer data, sleep duration, restaurant payment information, and pulse rate. For example, the data collection unit can obtain hospital interview data from electronic medical records. The data collection unit can also obtain health checkup results directly from medical institutions. The data collection unit can also obtain smartphone pedometer data from applications. For example, the data collection unit automatically collects data from smartphone pedometer apps. The data collection unit can also obtain sleep duration data from wearable devices. For example, the data collection unit automatically collects sleep duration data from wearable devices. The data collection unit can also obtain restaurant payment information from payment apps. For example, the data collection unit automatically collects restaurant payment information from payment apps. The data collection unit can also obtain pulse rate data from medical devices. For example, the data collection unit automatically collects pulse rate data from medical devices. By collecting diverse health data in this way, the user's health status can be comprehensively understood. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input hospital medical interview data into a generating AI and have the generating AI perform analysis of the medical interview data.

[0070] The analysis unit can analyze collected data using generative AI and evaluate the user's health status. For example, the analysis unit can evaluate the user's health status based on the collected data. For example, the analysis unit can use generative AI to analyze the user's dietary preferences. The analysis unit can also use generative AI to analyze the user's exercise habits. Furthermore, the analysis unit can use generative AI to analyze the user's sleep patterns. For example, the analysis unit can use generative AI to analyze the user's sleep patterns and evaluate the quality of sleep. This improves the accuracy of health status evaluation by using generative AI. Generative AI is implemented using technologies such as deep learning and natural language processing. Some or all of the above-mentioned processes in the analysis unit are performed using generative AI. For example, the analysis unit can input collected data into the generative AI and have the generative AI perform the evaluation of the user's health status.

[0071] The service provider can provide customized health advice based on the user's dietary preferences and exercise habits. For example, the service provider can provide a meal plan to increase the intake of specific nutrients based on the user's dietary preferences. The service provider can also provide an exercise program for stress management based on the user's exercise habits. This allows for the provision of health advice tailored to the user's individual needs. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's dietary preferences and exercise habits into a generating AI and have the generating AI generate customized health advice.

[0072] The service provider can propose specific action plans for preventive medicine based on the user's health data. For example, the service provider can propose a meal plan to increase the intake of specific nutrients based on the user's health data. The service provider can also propose an exercise program for stress management based on the user's health data. In this way, by proposing specific action plans for preventive medicine, it supports the user in maintaining their health. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI generate an action plan for preventive medicine.

[0073] The presentation unit can visually present the user's health data, making it easier to grasp trends and changes. For example, the presentation unit can visually present the user's health data using graphs or charts. The presentation unit can visually present the user's health data using graphs, making it easier to grasp trends and changes. The presentation unit can also visually present the user's health data using charts, making it easier to make data-driven decisions. This makes it easier for users to make data-driven decisions by visually presenting their health data. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's health data into a generating AI and have the generating AI perform the generation of the visual presentation.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection timing and collect data when the user is relaxed. If the user is relaxed, the data collection unit can also increase the collection timing and collect more detailed data. If the user is in a hurry, the data collection unit can shorten the collection timing and collect only the minimum necessary data. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0075] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. The data collection unit can also adjust the collection frequency based on the user's past collection history to perform efficient data collection. The data collection unit can also analyze the user's past collection history and customize the collection method. This enables efficient data collection by analyzing past collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data collection history into a generating AI and have the generating AI select the optimal collection method.

[0076] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the data collection unit can prioritize collecting diet-related data. If the user is interested in exercise, the data collection unit can also prioritize collecting exercise-related data. If the user is interested in stress management, the data collection unit can also prioritize collecting stress-related data. This makes it possible to collect data according to the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate the user's emotions and prioritize the health data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. If the user is relaxed, the data collection unit can also collect overall health data in a balanced manner. If the user is in a hurry, the data collection unit can also prioritize collecting only the most important data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI determine the priority of the health data to collect.

[0078] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting health data. For example, if the user is traveling, the data collection unit can prioritize the collection of health data from their travel destination. If the user is at home, the data collection unit can also prioritize the collection of health data from their home area. If the user is at work, the data collection unit can also prioritize the collection of health data from their workplace area. This enables more accurate health management by collecting highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts about health on social media, the data collection unit can collect data based on the content of that post. The data collection unit can also collect data based on the content of a user's exercise post on social media. The data collection unit can also collect data based on the content of a user's diet post on social media. This allows for health management tailored to the user's interests by collecting relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit will perform a detailed analysis on important health data. The analysis unit can also perform a simplified analysis on less important health data. The analysis unit can also determine the priority of the analysis according to the importance of the health data. This allows for efficient data analysis by adjusting the level of detail of the analysis according to the importance of the health data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input the importance of the health data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a nutrient analysis algorithm to dietary data. It can also apply an exercise volume analysis algorithm to exercise data. It can also apply a sleep quality analysis algorithm to sleep data. By applying an analysis algorithm appropriate to the category of health data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the categories of health data into the generative AI and have the generative AI execute the application of different analysis algorithms.

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

[0084] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit can also analyze current data while referring to past data. The analysis unit can also adjust the priority of analysis according to when the health data was collected. This enables efficient data analysis by determining the priority of analysis based on when the health data was collected. Some or all of the above processes in the analysis unit are performed using generative AI. For example, the analysis unit can input the health data collection period into the generative AI and have the generative AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of health data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of health data. Some or all of the above processes in the analysis unit are performed using generative AI. For example, the analysis unit can input the relevance of health data into the generative AI and have the generative AI perform the adjustment of the analysis order.

[0086] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed advice. If the user is stressed, the service provider can also provide concise advice. If the user is in a hurry, the service provider can also provide concise advice. By adjusting the way advice is expressed according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way advice is expressed.

[0087] The service provider can adjust the level of detail of advice based on the importance of the health data when providing advice. For example, the service provider can provide detailed advice for important health data. For less important health data, the service provider can also provide concise advice. The service provider can also determine the priority of advice according to the importance of the health data. This allows for efficient advice provision by adjusting the level of detail of advice according to the importance of the health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the advice.

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

[0089] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed advice. If the user is stressed, the service provider can also provide concise advice. If the user is in a hurry, the service provider can also provide short, to-the-point advice. By adjusting the length of the advice according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0090] The service provider can determine the priority of advice based on the timing of health data collection when providing advice. For example, the service provider can provide advice based on recently collected data. The service provider can also provide advice based on current data while referring to past data. The service provider can also adjust the priority of advice according to the timing of health data collection. This enables efficient advice provision by determining the priority of advice based on the timing of health data collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the timing of health data collection into a generating AI and have the generating AI determine the priority of advice.

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

[0092] The presentation unit can estimate the user's emotions and adjust how the data is displayed based on the estimated emotions. For example, if the user is relaxed, the presentation unit can display detailed data. If the user is stressed, the presentation unit can also display concise data. If the user is in a hurry, the presentation unit can also display concise data. This allows for more appropriate data display by adjusting how the data is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust how the data is displayed.

[0093] The display unit can select the optimal display method by referring to the user's past health data when displaying data. For example, the display unit can suggest the optimal display method based on the display method the user has preferred to use in the past. The display unit can also analyze the user's past data display history and select a display method with high visibility. The display unit can also refer to the user's past health data and provide a display method that highlights trends and changes. This makes it possible to display data optimally by referring to past health data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past health data into a generating AI and have the generating AI select the optimal display method.

[0094] The display unit can apply different display algorithms depending on the category of health data when displaying data. For example, for dietary data, the display unit can apply an algorithm that visually displays the balance of nutrients. For exercise data, the display unit can also apply an algorithm that visually displays the amount of exercise and calories burned. For sleep data, the display unit can also apply an algorithm that visually displays the quality and duration of sleep. By applying a display algorithm according to the category of health data, it becomes possible to display data with higher visibility. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the category of health data into a generating AI and have the generating AI execute the application of different display algorithms.

[0095] The presentation unit can estimate the user's emotions and adjust the display order of data based on the estimated emotions. For example, if the user is relaxed, the presentation unit can display detailed data first. If the user is stressed, the presentation unit can also display concise data first. If the user is in a hurry, the presentation unit can also display important data first. This allows for more appropriate data display by adjusting the display order of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, or not using AI. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the data.

[0096] The display unit can select the optimal display method when displaying data, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. This enables highly visible data display by selecting the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0097] The display unit can adjust the display order based on the relevance of health data when displaying data. For example, the display unit can display highly relevant data first. The display unit can also postpone displaying less relevant data. The display unit can also adjust the display order according to the relevance of health data. This allows for efficient data display by adjusting the display order based on the relevance of health data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the display order.

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

[0099] A health management system can estimate a user's emotions and adjust the content of health advice based on those emotions. For example, if a user is feeling stressed, it can prioritize providing advice on relaxation methods and stress reduction. If a user is feeling positive, it can provide advice on maintaining motivation. Furthermore, if a user is feeling tired, it can provide advice on rest and recovery. This makes it possible to provide appropriate health advice tailored to the user's emotions.

[0100] A health management system can analyze a user's past health data and predict future health risks. For example, it can predict potential health risks based on past dietary and exercise data. It can also predict the risk of future sleep disorders by analyzing past sleep data. Furthermore, it can predict future stress-related health risks based on past stress data. This allows users to take proactive measures against future health risks.

[0101] A health management system can estimate a user's emotions and adjust how health data is collected based on those estimates. For example, if a user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if a user is relaxed, more detailed data can be collected. Furthermore, if a user is in a hurry, only the minimum necessary data can be collected. This enables appropriate data collection tailored to the user's emotions.

[0102] A health management system can set personalized health goals based on a user's health data. For example, it can set an appropriate weight target based on the user's current health status. It can also set an appropriate exercise target based on the user's exercise habits. Furthermore, it can set an appropriate nutrition target based on the user's eating habits. This allows users to take concrete actions toward their own health goals.

[0103] A health management system can estimate a user's emotions and adjust the visual presentation of health data based on those emotions. For example, if a user is stressed, it can provide a concise and easy-to-understand data display. If the user is relaxed, it can provide a detailed data display. Furthermore, if the user is in a hurry, it can provide a concise data display. This enables appropriate data display tailored to the user's emotions.

[0104] A health management system can provide community features based on users' health data. For example, it can match users with similar health goals and encourage each other. It can also provide forums for sharing health-related information. Furthermore, it can host health events and challenges, allowing users to compete with each other. This makes health management more enjoyable for users through the community.

[0105] A health management system can estimate a user's emotions and adjust the timing of health advice based on those emotions. For example, if a user is stressed, advice can be provided at a time when they are relaxed. Conversely, if a user is relaxed, positive advice can be provided. Furthermore, if a user is in a hurry, concise advice can be provided. This makes it possible to provide advice at the appropriate time according to the user's emotions.

[0106] A health management system can provide educational content about health based on users' health data. For example, it can provide information on nutrition to help users choose healthy foods. It can also provide information on exercise to help users exercise effectively. Furthermore, it can provide information on stress management to help users manage stress effectively. This allows users to deepen their knowledge about health.

[0107] A health management system can estimate a user's emotions and adjust the frequency of health data collection based on those estimates. For example, if a user is stressed, the collection frequency can be reduced to lessen the user's burden. Conversely, if a user is relaxed, the collection frequency can be increased to collect more detailed data. Furthermore, if a user is in a hurry, only the minimum necessary data can be collected. This enables appropriate data collection tailored to the user's emotions.

[0108] A health management system can provide health-related reminders based on the user's health data. For example, it can set reminders to encourage regular exercise, meal times, and even sleep schedules. This allows users to receive support in maintaining healthy lifestyle habits.

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

[0110] Step 1: The data collection unit collects health data. For example, it obtains medical interview data from electronic medical records at hospitals and health checkup results directly from medical institutions. It also automatically collects pedometer data from smartphones via applications and obtains sleep duration data from wearable devices. Furthermore, it automatically collects payment information for dining out from payment apps and obtains pulse rate data from medical devices. Step 2: The analysis unit analyzes the data collected by the collection unit using generating AI. For example, it evaluates the user's health status and analyzes their dietary preferences, exercise habits, and sleep patterns. This allows for a comprehensive understanding of the user's health status. Step 3: The service provider provides customized advice based on the analysis results obtained by the analysis provider. For example, they may provide a meal plan to increase the intake of specific nutrients or an exercise program for stress management based on the user's dietary preferences and exercise habits. They can also propose specific action plans for preventive medicine. Step 4: The presentation unit visually presents the user's health data based on the advice provided by the delivery unit. For example, the user's health data may be visually presented using graphs and charts to make it easier to understand trends and changes. This makes it easier to make data-driven decisions.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects health data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates customized advice based on the analysis results. The presentation unit is implemented in the display 40A of the smart device 14 and visually presents the user's health data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and presentation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects health data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The provision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and generates customized advice based on the analysis results. The presentation unit is implemented, for example, in the display of the smart glasses 214 and visually presents the user's health data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects health data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates customized advice based on the analysis results. The presentation unit is implemented in the display 343 of the headset terminal 314 and visually presents the user's health data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and presentation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects health data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates customized advice based on the analysis results. The presentation unit is implemented, for example, by the display of the robot 414 and visually presents the user's health data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data collection unit that collects health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit that provides customized advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a display unit that visually presents the user's health data based on the advice provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as hospital interviews, health checkup results, smartphone pedometer readings, sleep duration, restaurant payment information, and pulse rate. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed by a generating AI to evaluate the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It provides customized health advice based on the user's dietary preferences and exercise habits. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Based on user health data, we propose specific action plans for preventive medicine. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, Visually present user health data to make it easier to understand trends and changes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, It estimates the user's emotions and adjusts how data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When displaying data, the system selects the optimal display method by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When displaying data, different display algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, It estimates the user's emotions and adjusts the display order of data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, When displaying data, the system selects the optimal display method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, When displaying data, adjust the display order based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A data collection unit that collects health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit that provides customized advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a display unit that visually presents the user's health data based on the advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as hospital interviews, health checkup results, smartphone pedometer readings, sleep duration, restaurant payment information, and pulse rate. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed using AI to evaluate the user's health status. The system according to feature 1.

4. The aforementioned supply unit is, It provides customized health advice based on the user's dietary preferences and exercise habits. The system according to feature 1.

5. The aforementioned supply unit is, Based on user health data, we propose specific action plans for preventive medicine. The system according to feature 1.

6. The aforementioned display unit is, Visually present user health data to make it easier to understand trends and changes. The system according to feature 1.

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

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

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