system

Abstract

The system according to this embodiment aims to analyze the user's health information and provide appropriate health management advice. [Solution] The system according to the embodiment comprises a generation unit, a registration unit, an analysis unit, and a transfer unit. The generation unit generates an avatar tailored to the user's preferences. The registration unit registers the user's health information. The analysis unit analyzes the health information registered by the registration unit and provides health management advice. The transfer unit automatically transfers health information when the user is transported to a hospital due to a sudden illness or injury.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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 prior art, it is difficult to provide appropriate advice for user health management, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the health information of a user and provide appropriate health management advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a registration unit, an analysis unit, and a transfer unit. The generation unit generates an avatar tailored to the user's preferences. The registration unit registers the user's health information. The analysis unit analyzes the health information registered by the registration unit and provides health management advice. The transfer unit automatically transfers health information when the user is transported to a hospital due to a sudden illness or injury. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's health information and provide appropriate health management advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". 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 and a smart device 14. An example of the data processing device 12 is a server. <​​​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that uses a generating AI to create a teacher or trainer tailored to the user's preferences and manages their health. In this health management system, the user creates an avatar of their choice, registers health goals and health information, and the generating AI provides health management advice based on this information. Furthermore, if the user is transported to a hospital due to a sudden illness or injury, the registered health information can be automatically transferred to the hospital or relatives, which can be used for early investigation of the cause, treatment, and rehabilitation. For example, when the user creates an avatar of their choice, they can set the appearance, personality, background, etc. For example, they can create an avatar that suits their preferences, such as a realistic style, an anime style, or a character style. Next, the user registers their health goals and health information. For example, they can set a goal to lose weight or a goal to be healthy enough to travel around the world at a certain age. In addition, they can register drinking and smoking habits, questionnaires about physical discomfort and well-being, information on health checkups and treatment history, and pulse and sleep data obtained from a smartwatch. Based on the registered health information, the generating AI provides the user with advice on their daily life. For example, it can suggest exercises and diets to incorporate, identify current and future health risks and countermeasures, and centrally manage past health status and medical history. Furthermore, if a user is transported to a hospital due to a sudden illness or injury, the registered health information can be automatically transferred to the hospital or family members. This allows for early identification of the cause, treatment, and rehabilitation. For instance, the doctor at the hospital can check the user's health information and provide appropriate treatment quickly. In this way, by using generative AI, it is possible to create a doctor or trainer tailored to the user's preferences and provide a system for health management. As a result, users can receive health management that suits them, and it is expected that their healthy lifespan will be extended. In this way, the health management system can efficiently manage the user's health.

[0029] The health management system according to the embodiment comprises a generation unit, a registration unit, an analysis unit, and a transfer unit. The generation unit generates an avatar tailored to the user's preferences. The generation unit generates an avatar based on information such as the user's appearance, personality, and background. The generation unit can generate avatars tailored to the user's preferences, such as realistic, anime, or character-style avatars. The generation unit generates an avatar based on the user's physical characteristics, such as facial features and body type. The generation unit generates an avatar based on the user's personality characteristics, such as introverted or extroverted personality. The generation unit generates an avatar based on information about the user's background, such as work history and educational background. The registration unit registers the user's health information. The registration unit registers the user's health goals and health information, such as a weight loss goal or a goal of achieving a state of health that allows for a round-the-world trip at a specific age. The registration unit can register information such as drinking and smoking habits, questionnaires about physical discomfort and well-being, information on health checkups and treatment history, and pulse and sleep data obtained from smartwatches. The analysis unit analyzes the registered health information and provides health management advice. The analysis unit provides advice such as suggestions for exercise and diet to be incorporated, current and future health risks and countermeasures, and centralized management of past health status and medical history. For example, the analysis unit can suggest exercise to be incorporated based on the user's health information, such as aerobic exercise or strength training. For example, the analysis unit can suggest dietary recommendations based on the user's health information, such as calorie restriction or nutritional balance. For example, the analysis unit can suggest current and future health risks and countermeasures based on the user's health information, such as diabetes risk or heart disease risk. For example, the analysis unit can centrally manage past health status and medical history based on the user's health information, such as using database management or cloud storage. The transfer unit automatically transfers health information when a person is transported to a hospital due to a sudden illness or injury. For example, the transfer unit automatically transfers registered health information to the hospital or to relatives.The transfer unit can automatically transfer health information using, for example, a transfer protocol and security measures. For example, if a person is transported to a hospital due to a sudden illness or injury, the transfer unit automatically transfers the registered health information to the doctor at the hospital. For example, the doctor at the hospital can check the user's health information and provide appropriate treatment quickly. For example, if a person is transported to a hospital due to a sudden illness or injury, the transfer unit automatically transfers the registered health information to a relative. For example, the relative can check the user's health information and use it for early investigation of the cause, treatment, and rehabilitation. As a result, the health management system according to the embodiment can efficiently manage the user's health.

[0030] The generation unit creates avatars tailored to the user's preferences. For example, it generates avatars based on information such as the user's appearance, personality, and background. Specifically, it analyzes the user's provided facial photo and body type data to generate avatars that match the user's preferences, such as realistic, anime, or character-style avatars. For example, it can analyze facial features such as eye size, nose shape, and mouth position in detail and generate an avatar that reflects these features. Similarly, it can generate an avatar that closely matches the user's body type based on data such as height, weight, and body fat percentage. Furthermore, it can also generate avatars based on the user's personality traits. For example, it can generate avatars with calm colors and simple designs for introverted users, and avatars with bright colors and flashy designs for extroverted users. Information about the user's background is also used in avatar generation. For example, it can generate avatars that reflect the user's lifestyle and values ​​based on information such as work history, education, hobbies, and special skills. In this way, the generation unit generates a variety of avatars tailored to the user's individuality and preferences, making health management more approachable and enjoyable for users. The generation unit utilizes AI technology to analyze user input data and generate the optimal avatar. For example, if a user inputs a photo of their face using the generation AI, the AI ​​automatically extracts facial features and generates an avatar. Furthermore, if the user inputs information about their personality and background as prompts, the AI ​​suggests avatar designs based on that information. This allows the generation unit to quickly and accurately generate avatars tailored to the user's preferences.

[0031] The registration unit registers users' health information. For example, it registers users' health goals and health information. Specifically, users can register goals such as weight loss targets or goals to achieve a state of health that allows them to travel around the world at a certain age. Furthermore, it can also register information about the user's daily life. For example, it can register drinking and smoking habits, questionnaires about physical discomfort and well-being, information on health checkups and treatment history, and pulse and sleep data obtained from smartwatches. This allows the registration unit to gain a detailed understanding of the user's health status and provide basic data for creating individual health management plans. The registration unit utilizes a database to efficiently manage the information entered by users. For example, when a user enters health information from a smartphone or computer, that information is stored in a cloud-based database. This allows users to check and update their health information anytime, anywhere. In addition, the registration unit implements security measures to safely manage users' health information. For example, it encrypts data and restricts access to protect user privacy. This allows the registration unit to safely and efficiently manage users' health information and improve the reliability of the entire health management system.

[0032] The analysis department analyzes registered health information and provides health management advice. For example, it provides advice such as suggestions for exercise and diet, current and future health risks and countermeasures, and centralized management of past health status and medical history. Specifically, it suggests exercise based on the user's health information, such as aerobic exercise or strength training. Furthermore, it provides dietary suggestions based on the user's health information, such as calorie restriction or nutritional balance. The analysis department utilizes AI technology to analyze user health information and provide optimal advice. For example, the AI ​​analyzes the user's health data, evaluates their current health status, and predicts future health risks, such as diabetes risk or heart disease risk. Furthermore, the AI ​​can track changes in health status based on the user's past health data and suggest appropriate countermeasures. This allows the analysis department to efficiently support user health management and minimize health risks. The analysis department utilizes databases and cloud storage to centrally manage user health information. For example, it stores user health information in a database and uses it for analysis and advice as needed. Furthermore, by utilizing cloud storage, users can check and update their health information anytime, anywhere. This allows the analytics department to efficiently manage users' health information and improve the overall performance of the health management system.

[0033] The data transfer unit automatically transfers health information when a person is transported to a hospital due to a sudden illness or injury. For example, the unit automatically transfers registered health information to the hospital or to family members. Specifically, if a user is transported to a hospital due to a sudden illness or injury, the unit automatically transfers the registered health information to the doctor at the hospital. For example, the doctor at the hospital can check the user's health information and provide appropriate treatment quickly. The data transfer unit can also automatically transfer the user's health information to family members. For example, family members can check the user's health information and use it for early diagnosis, treatment, and rehabilitation. The data transfer unit automatically transfers health information using transfer protocols and security measures. For example, it implements data encryption and authentication processes to securely transfer the user's health information. This allows the data transfer unit to quickly and securely transfer the user's health information and support emergency responses. Furthermore, the data transfer unit updates the user's health information in real time, ensuring that the latest information is always available. For example, if a user registers new health information, that information is immediately reflected in the data transfer unit and automatically transferred as needed. This allows the data transfer unit to always support quick and appropriate responses based on the latest health information.

[0034] The generation unit can generate avatars based on information such as the user's appearance, personality, and background. For example, the generation unit can generate avatars based on the user's physical characteristics. For example, the generation unit can generate avatars based on facial features and body type. For example, the generation unit can generate avatars based on the user's personality characteristics. For example, the generation unit can generate avatars based on introverted or extroverted personalities. For example, the generation unit can generate avatars based on information about the user's background. For example, the generation unit can generate avatars based on work history and educational background. In this way, by generating avatars based on information such as the user's appearance, personality, and background, it is possible to provide avatars that match the user's preferences. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input information such as the user's appearance, personality, and background into a generation AI and have the generation AI perform avatar generation.

[0035] The registration unit can register the user's health goals and health information. For example, the registration unit can register the user's health goals. For example, the registration unit can register goals such as losing weight or achieving a state of health that allows one to travel around the world at a specific age. The registration unit can also register the user's health information. For example, the registration unit can register drinking and smoking habits, questionnaires about physical discomfort and well-being, information on health checkups and treatment history, and pulse and sleep data obtained from a smartwatch. By registering the user's health goals and health information, the accuracy of health management is improved. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's health information into the AI ​​and have the AI ​​perform the registration of the health information.

[0036] The analysis unit can provide advice based on registered health information, such as suggestions for exercise and diet, current and future health risks and countermeasures, and centralized management of past health status and medical history. For example, the analysis unit can suggest exercises to be taken based on the user's health information. For example, the analysis unit can suggest aerobic exercise or strength training. For example, the analysis unit can suggest diets based on the user's health information. For example, the analysis unit can suggest calorie restriction or nutritional balance. For example, the analysis unit can suggest current and future health risks and countermeasures based on the user's health information. For example, the analysis unit can suggest diabetes risk or heart disease risk. For example, the analysis unit can centrally manage past health status and medical history based on the user's health information. For example, the analysis unit can perform centralized management using database management or cloud storage. This allows for effective support of the user's health management by providing advice based on health information. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input the user's health information into a generating AI and have the generating AI provide health management advice.

[0037] The transfer unit can automatically transfer registered health information to the hospital or relatives when a person is transported to a hospital due to a sudden illness or injury. For example, the transfer unit can automatically transfer registered health information to a doctor at the hospital. The transfer unit can automatically transfer health information using, for example, a transfer protocol and security measures. For example, when a person is transported to a hospital due to a sudden illness or injury, the transfer unit can automatically transfer registered health information to relatives. For example, relatives can check the user's health information and use it for early investigation of the cause, treatment, and rehabilitation. This helps in rapid treatment and rehabilitation by automatically transferring health information in the event of a sudden illness or injury. Some or all of the above processes in the transfer unit may be performed using AI or not. For example, the transfer unit can input registered health information into AI and have the AI ​​perform the automatic transfer of health information.

[0038] The generation unit can analyze the user's past avatar creation history and select the optimal avatar generation method. For example, the generation unit can analyze the characteristics of avatars the user has created in the past, understand their preferences, and generate a new avatar. For example, the generation unit can suggest the optimal avatar based on the appearance and personality patterns of avatars the user has chosen in the past. For example, the generation unit can generate a more satisfying avatar by referring to the evaluations of avatars the user has created in the past. In this way, by analyzing the past avatar creation history, it is possible to provide avatars that match the user's preferences. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past avatar creation history into a generation AI and have the generation AI select the optimal avatar generation method.

[0039] The generation unit can customize the characteristics of an avatar based on the user's current lifestyle and areas of interest during avatar generation. For example, the generation unit can generate avatars related to work or hobbies according to the user's current lifestyle. For example, the generation unit can generate relevant avatars based on the user's areas of interest (sports, music, art, etc.). For example, the generation unit can generate the optimal avatar to match the user's lifestyle (outdoorsy, indoorsy, etc.). By customizing avatars based on the user's lifestyle and areas of interest, it is possible to provide avatars that are more suitable for the user. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input information about the user's current lifestyle and areas of interest into the generation AI and have the generation AI perform the customization of avatar characteristics.

[0040] The generation unit can prioritize the generation of highly relevant avatars by considering the user's geographical location information during avatar generation. For example, the generation unit can generate avatars that match the culture and customs of the area where the user lives. For example, if the user is traveling, the generation unit can generate avatars related to the area they are visiting. For example, if the user is interested in a particular region, the generation unit can generate avatars related to that region. In this way, by considering the user's geographical location information, it is possible to provide highly relevant avatars. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform avatar generation.

[0041] The generation unit can analyze a user's social media activity and generate relevant avatars when generating avatars. For example, the generation unit can generate relevant avatars based on the content a user frequently posts on social media. For example, the generation unit can analyze the trends of accounts a user follows and generate avatars that suit their preferences. For example, the generation unit can generate avatars that match the themes of online communities a user participates in. In this way, by analyzing a user's social media activity, it is possible to provide highly relevant avatars. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform avatar generation.

[0042] The registration unit can analyze the user's past health information registration history and select the optimal registration method. For example, the registration unit can suggest the optimal registration method based on the health information the user has frequently registered in the past. For example, the registration unit can prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. For example, the registration unit can predict and suggest health information to be registered at a specific time based on the user's past registration history. In this way, by analyzing the past health information registration history, a registration method tailored to the user can be provided. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input the user's past health information registration history into a generation AI and have the generation AI select the optimal registration method.

[0043] The registration unit can filter health information based on the user's current lifestyle and areas of interest when registering it. For example, the registration unit can prioritize registering health information relevant to the user's current lifestyle. For example, the registration unit can register relevant health information based on the user's areas of interest (exercise, diet, sleep, etc.). For example, the registration unit can register optimal health information tailored to the user's lifestyle (work, hobbies, family, etc.). This allows for the registration of highly relevant health information by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input information about the user's current lifestyle and areas of interest into a generation AI and have the generation AI perform the filtering of health information.

[0044] The registration unit can prioritize registering highly relevant information when registering health information, taking into account the user's geographical location. For example, the registration unit can prioritize registering information related to health risks in the area where the user lives. For example, if the user is traveling, the registration unit can prioritize registering health information related to the area they are visiting. For example, if the user is interested in a particular area, the registration unit can prioritize registering health information related to that area. This allows the system to provide highly relevant health information by considering the user's geographical location. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input the user's geographical location information into a generation AI and have the generation AI perform the registration of health information.

[0045] The registration unit can analyze a user's social media activity and register relevant information when registering health information. For example, the registration unit can register relevant information based on the health-related content that the user frequently posts on social media. For example, the registration unit can analyze the trends of health-related accounts that the user follows and register relevant information. For example, the registration unit can register health information that matches the themes of online communities that the user participates in. This allows the system to provide highly relevant health information by analyzing the user's social media activity. Some or all of the above processing in the registration unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the registration unit can input data on the user's social media activity into a generative AI and have the generative AI perform the registration of health information.

[0046] The analysis unit can adjust the level of detail of advice based on the user's importance when analyzing health information. For example, the analysis unit can provide detailed advice based on the health goals that the user considers important. For example, the analysis unit can suggest detailed countermeasures based on the health risks that the user is concerned about. For example, the analysis unit can provide detailed analysis results based on the health information that the user prioritizes. By adjusting the level of detail of advice based on the user's importance, more effective advice can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's health information into a generation AI and have the generation AI perform the adjustment of the level of detail of the advice.

[0047] The analysis unit can apply different analysis algorithms depending on the user's category when analyzing health information. For example, if the user is an athlete, the analysis unit can apply an analysis algorithm specialized in improving athletic performance. If the user is elderly, the analysis unit can apply an analysis algorithm specialized in extending healthy life expectancy. If the user has a chronic disease, the analysis unit can apply an analysis algorithm specialized in that disease. By applying an analysis algorithm according to the user's category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input user category information into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0048] The analysis unit can determine the priority of advice based on the timing of user submissions when analyzing health information. For example, the analysis unit can prioritize the analysis of health information recently submitted by the user and provide advice accordingly. For example, the analysis unit can determine priority based on health information that the user submits regularly. For example, the analysis unit can prioritize the analysis of health information submitted by the user before a specific event and provide advice accordingly. This allows for more timely advice to be provided by prioritizing advice based on the timing of user submissions. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input information on the timing of user submissions into a generative AI and have the generative AI determine the priority of advice.

[0049] The analysis unit can adjust the order of advice based on the user's relevance when analyzing health information. For example, the analysis unit can adjust the order of advice based on the health risks that the user considers important. For example, the analysis unit can adjust the order of advice based on the health goals that the user is interested in. For example, the analysis unit can adjust the order of advice based on the health information that the user prioritizes. By adjusting the order of advice based on the user's relevance, more effective advice can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input user relevance information into a generative AI and have the generative AI perform the adjustment of the order of advice.

[0050] The transfer unit can analyze the user's past transfer history to select the optimal transfer method when transferring health information. For example, the transfer unit can propose the optimal transfer method based on the transfer method the user has used in the past. For example, the transfer unit can select a transfer method appropriate to a specific situation from the user's past transfer history. For example, the transfer unit can select the optimal transfer method by referring to a transfer method that the user has evaluated in the past. In this way, by analyzing past transfer history, it is possible to provide a transfer method that suits the user. Some or all of the above processing in the transfer unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the transfer unit can input the user's past transfer history into a generation AI and have the generation AI select the optimal transfer method.

[0051] The transfer unit can customize the means of transfer based on the user's current living situation when transferring health information. For example, the transfer unit can suggest the optimal transfer method according to the user's current living situation. For example, the transfer unit can customize the transfer method to suit the user's lifestyle (work, home, hobbies, etc.). For example, the transfer unit can select an appropriate transfer method based on the user's current health condition. By customizing the transfer method based on the user's living situation, a more appropriate transfer method can be provided. Some or all of the above processing in the transfer unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the transfer unit can input information about the user's current living situation into a generation AI and have the generation AI perform the customization of the transfer method.

[0052] The transfer unit can select the optimal transfer method when transferring health information, taking into account the user's geographical location. For example, the transfer unit may prioritize transferring information related to medical institutions in the user's area of ​​residence. For example, if the user is traveling, the transfer unit may prioritize transferring health information related to the area the user is visiting. For example, if the user is interested in a particular region, the transfer unit may prioritize transferring health information related to that region. This allows for the provision of a highly relevant transfer method by considering the user's geographical location. Some or all of the above processing in the transfer unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the transfer unit may input the user's geographical location information into a generative AI and have the generative AI select the transfer method.

[0053] The transfer unit can analyze the user's social media activity and propose a transfer method when transferring health information. For example, the transfer unit can transfer relevant information based on the health-related content that the user frequently posts on social media. For example, the transfer unit can analyze the trends of health-related accounts that the user follows and transfer relevant information. For example, the transfer unit can transfer health information that matches the theme of the online community the user participates in. In this way, by analyzing the user's social media activity, a highly relevant transfer method can be provided. Some or all of the above processing in the transfer unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the transfer unit can input data on the user's social media activity into a generative AI and have the generative AI propose a transfer method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The health management system can provide health management advice while considering the user's geographical location. For example, it can suggest meals tailored to the climate and food culture of the area where the user lives. If the user is traveling, it can suggest health risks and countermeasures related to the area they are visiting. If the user is interested in a particular region, it can provide health information related to that region. In this way, by considering the user's geographical location, more relevant advice can be provided. The analysis unit inputs the user's geographical location information into the generating AI, and the generating AI can then execute the provision of advice.

[0056] The health management system can analyze a user's social media activity and provide relevant health management advice. For example, it can provide relevant advice based on the health-related content a user frequently posts on social media. It can analyze the trends of health-related accounts a user follows and provide advice tailored to their preferences. It can provide advice aligned with the themes of online communities a user participates in. In this way, by analyzing a user's social media activity, it can provide highly relevant advice. The analysis unit inputs data on the user's social media activity into a generating AI, which then generates the advice.

[0057] The health management system can analyze a user's past health information registration history and select the optimal registration method. For example, it can suggest the optimal registration method based on the health information the user has frequently registered in the past. It can prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. It can predict and suggest health information to be registered at specific times based on the user's past registration history. In this way, by analyzing past health information registration history, it can provide a registration method that suits the user. The registration unit inputs the user's past health information registration history into a generating AI, which then performs the task of selecting the optimal registration method.

[0058] The health management system can register health information while considering the user's geographical location. For example, it can prioritize registering information related to health risks in the area where the user lives. If the user is traveling, it can prioritize registering health information related to the area they are visiting. If the user is interested in a particular region, it can prioritize registering health information related to that region. In this way, by considering the user's geographical location, it can provide highly relevant health information. The registration unit inputs the user's geographical location information into a generation AI, and the generation AI can then perform the registration of health information.

[0059] The health management system can prioritize health management advice based on when the user submits it. For example, it can prioritize analyzing health information recently submitted by the user and provide advice accordingly. It can also prioritize health information that the user submits regularly. Furthermore, it can prioritize analyzing health information submitted by the user before a specific event and provide advice accordingly. This allows for more timely advice by prioritizing advice based on when the user submits it. The analysis unit inputs information about the user's submission timing into the generating AI, which then performs the task of determining the priority of the advice.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The generation unit generates an avatar tailored to the user's preferences. The generation unit generates an avatar based on information such as the user's appearance, personality, and background. The generation unit can generate avatars tailored to the user's preferences, such as realistic, anime, or character-style avatars. The generation unit generates an avatar based on the user's physical characteristics, such as facial features and body type. The generation unit generates an avatar based on the user's personality traits, such as introverted or extroverted personality traits. The generation unit generates an avatar based on information about the user's background, such as work history and educational background. Step 2: The registration unit registers the user's health information. The registration unit registers, for example, the user's health goals and health information. For example, the registration unit can register goals such as losing weight or achieving a state of health that allows one to travel around the world at a specific age. For example, the registration unit can register drinking and smoking habits, questionnaires about physical discomfort and well-being, information on health checkups and treatment history, and pulse and sleep data obtained from the smartwatch. Step 3: The analysis unit analyzes the registered health information and provides health management advice. The analysis unit provides advice such as suggestions for exercise and diet to be incorporated, current and future health risks and countermeasures, and centralized management of past health status and medical history. For example, the analysis unit can suggest exercise to be incorporated based on the user's health information. For example, it can suggest aerobic exercise or strength training. For example, the analysis unit can suggest diet based on the user's health information. For example, it can suggest calorie restriction or nutritional balance. For example, the analysis unit can suggest current and future health risks and countermeasures based on the user's health information. For example, it can suggest diabetes risk or heart disease risk. For example, the analysis unit can centrally manage past health status and medical history based on the user's health information. For example, centralized management can be done using database management or cloud storage. Step 4: The transfer unit automatically transfers health information when a person is transported to a hospital due to a sudden illness or injury. The transfer unit automatically transfers registered health information to the hospital or to relatives, for example. The transfer unit can automatically transfer health information using, for example, a transfer protocol and security measures. For example, when a person is transported to a hospital due to a sudden illness or injury, the transfer unit automatically transfers registered health information to the doctor at the hospital. For example, the doctor at the hospital can check the user's health information and provide appropriate treatment quickly. For example, when a person is transported to a hospital due to a sudden illness or injury, the transfer unit automatically transfers registered health information to relatives. For example, relatives can check the user's health information and use it for early investigation of the cause, treatment, and rehabilitation.

[0062] (Example of form 2) The health management system according to an embodiment of the present invention is a system that uses a generating AI to create a teacher or trainer tailored to the user's preferences and manages their health. In this health management system, the user creates an avatar of their choice, registers health goals and health information, and the generating AI provides health management advice based on this information. Furthermore, if the user is transported to a hospital due to a sudden illness or injury, the registered health information can be automatically transferred to the hospital or relatives, which can be used for early investigation of the cause, treatment, and rehabilitation. For example, when the user creates an avatar of their choice, they can set the appearance, personality, background, etc. For example, they can create an avatar that suits their preferences, such as a realistic style, an anime style, or a character style. Next, the user registers their health goals and health information. For example, they can set a goal to lose weight or a goal to be healthy enough to travel around the world at a certain age. In addition, they can register drinking and smoking habits, questionnaires about physical discomfort and well-being, information on health checkups and treatment history, and pulse and sleep data obtained from a smartwatch. Based on the registered health information, the generating AI provides the user with advice on their daily life. For example, it can suggest exercises and diets to incorporate, identify current and future health risks and countermeasures, and centrally manage past health status and medical history. Furthermore, if a user is transported to a hospital due to a sudden illness or injury, the registered health information can be automatically transferred to the hospital or family members. This allows for early identification of the cause, treatment, and rehabilitation. For instance, the doctor at the hospital can check the user's health information and provide appropriate treatment quickly. In this way, by using generative AI, it is possible to create a doctor or trainer tailored to the user's preferences and provide a system for health management. As a result, users can receive health management that suits them, and it is expected that their healthy lifespan will be extended. In this way, the health management system can efficiently manage the user's health.

[0063] The health management system according to the embodiment comprises a generation unit, a registration unit, an analysis unit, and a transfer unit. The generation unit generates an avatar tailored to the user's preferences. The generation unit generates an avatar based on information such as the user's appearance, personality, and background. The generation unit can generate avatars tailored to the user's preferences, such as realistic, anime, or character-style avatars. The generation unit generates an avatar based on the user's physical characteristics, such as facial features and body type. The generation unit generates an avatar based on the user's personality characteristics, such as introverted or extroverted personality. The generation unit generates an avatar based on information about the user's background, such as work history and educational background. The registration unit registers the user's health information. The registration unit registers the user's health goals and health information, such as a weight loss goal or a goal of achieving a state of health that allows for a round-the-world trip at a specific age. The registration unit can register information such as drinking and smoking habits, questionnaires about physical discomfort and well-being, information on health checkups and treatment history, and pulse and sleep data obtained from smartwatches. The analysis unit analyzes the registered health information and provides health management advice. The analysis unit provides advice such as suggestions for exercise and diet to be incorporated, current and future health risks and countermeasures, and centralized management of past health status and medical history. For example, the analysis unit can suggest exercise to be incorporated based on the user's health information, such as aerobic exercise or strength training. For example, the analysis unit can suggest dietary recommendations based on the user's health information, such as calorie restriction or nutritional balance. For example, the analysis unit can suggest current and future health risks and countermeasures based on the user's health information, such as diabetes risk or heart disease risk. For example, the analysis unit can centrally manage past health status and medical history based on the user's health information, such as using database management or cloud storage. The transfer unit automatically transfers health information when a person is transported to a hospital due to a sudden illness or injury. For example, the transfer unit automatically transfers registered health information to the hospital or to relatives.The transfer unit can automatically transfer health information using, for example, a transfer protocol and security measures. For example, if a person is transported to a hospital due to a sudden illness or injury, the transfer unit automatically transfers the registered health information to the doctor at the hospital. For example, the doctor at the hospital can check the user's health information and provide appropriate treatment quickly. For example, if a person is transported to a hospital due to a sudden illness or injury, the transfer unit automatically transfers the registered health information to a relative. For example, the relative can check the user's health information and use it for early investigation of the cause, treatment, and rehabilitation. As a result, the health management system according to the embodiment can efficiently manage the user's health.

[0064] The generation unit creates avatars tailored to the user's preferences. For example, it generates avatars based on information such as the user's appearance, personality, and background. Specifically, it analyzes the user's provided facial photo and body type data to generate avatars that match the user's preferences, such as realistic, anime, or character-style avatars. For example, it can analyze facial features such as eye size, nose shape, and mouth position in detail and generate an avatar that reflects these features. Similarly, it can generate an avatar that closely matches the user's body type based on data such as height, weight, and body fat percentage. Furthermore, it can also generate avatars based on the user's personality traits. For example, it can generate avatars with calm colors and simple designs for introverted users, and avatars with bright colors and flashy designs for extroverted users. Information about the user's background is also used in avatar generation. For example, it can generate avatars that reflect the user's lifestyle and values ​​based on information such as work history, education, hobbies, and special skills. In this way, the generation unit generates a variety of avatars tailored to the user's individuality and preferences, making health management more approachable and enjoyable for users. The generation unit utilizes AI technology to analyze user input data and generate the optimal avatar. For example, if a user inputs a photo of their face using the generation AI, the AI ​​automatically extracts facial features and generates an avatar. Furthermore, if the user inputs information about their personality and background as prompts, the AI ​​suggests avatar designs based on that information. This allows the generation unit to quickly and accurately generate avatars tailored to the user's preferences.

[0065] The registration unit registers users' health information. For example, it registers users' health goals and health information. Specifically, users can register goals such as weight loss targets or goals to achieve a state of health that allows them to travel around the world at a certain age. Furthermore, it can also register information about the user's daily life. For example, it can register drinking and smoking habits, questionnaires about physical discomfort and well-being, information on health checkups and treatment history, and pulse and sleep data obtained from smartwatches. This allows the registration unit to gain a detailed understanding of the user's health status and provide basic data for creating individual health management plans. The registration unit utilizes a database to efficiently manage the information entered by users. For example, when a user enters health information from a smartphone or computer, that information is stored in a cloud-based database. This allows users to check and update their health information anytime, anywhere. In addition, the registration unit implements security measures to safely manage users' health information. For example, it encrypts data and restricts access to protect user privacy. This allows the registration unit to safely and efficiently manage users' health information and improve the reliability of the entire health management system.

[0066] The analysis department analyzes registered health information and provides health management advice. For example, it provides advice such as suggestions for exercise and diet, current and future health risks and countermeasures, and centralized management of past health status and medical history. Specifically, it suggests exercise based on the user's health information, such as aerobic exercise or strength training. Furthermore, it provides dietary suggestions based on the user's health information, such as calorie restriction or nutritional balance. The analysis department utilizes AI technology to analyze user health information and provide optimal advice. For example, the AI ​​analyzes the user's health data, evaluates their current health status, and predicts future health risks, such as diabetes risk or heart disease risk. Furthermore, the AI ​​can track changes in health status based on the user's past health data and suggest appropriate countermeasures. This allows the analysis department to efficiently support user health management and minimize health risks. The analysis department utilizes databases and cloud storage to centrally manage user health information. For example, it stores user health information in a database and uses it for analysis and advice as needed. Furthermore, by utilizing cloud storage, users can check and update their health information anytime, anywhere. This allows the analytics department to efficiently manage users' health information and improve the overall performance of the health management system.

[0067] The data transfer unit automatically transfers health information when a person is transported to a hospital due to a sudden illness or injury. For example, the unit automatically transfers registered health information to the hospital or to family members. Specifically, if a user is transported to a hospital due to a sudden illness or injury, the unit automatically transfers the registered health information to the doctor at the hospital. For example, the doctor at the hospital can check the user's health information and provide appropriate treatment quickly. The data transfer unit can also automatically transfer the user's health information to family members. For example, family members can check the user's health information and use it for early diagnosis, treatment, and rehabilitation. The data transfer unit automatically transfers health information using transfer protocols and security measures. For example, it implements data encryption and authentication processes to securely transfer the user's health information. This allows the data transfer unit to quickly and securely transfer the user's health information and support emergency responses. Furthermore, the data transfer unit updates the user's health information in real time, ensuring that the latest information is always available. For example, if a user registers new health information, that information is immediately reflected in the data transfer unit and automatically transferred as needed. This allows the data transfer unit to always support quick and appropriate responses based on the latest health information.

[0068] The generation unit can generate avatars based on information such as the user's appearance, personality, and background. For example, the generation unit can generate avatars based on the user's physical characteristics. For example, the generation unit can generate avatars based on facial features and body type. For example, the generation unit can generate avatars based on the user's personality characteristics. For example, the generation unit can generate avatars based on introverted or extroverted personalities. For example, the generation unit can generate avatars based on information about the user's background. For example, the generation unit can generate avatars based on work history and educational background. In this way, by generating avatars based on information such as the user's appearance, personality, and background, it is possible to provide avatars that match the user's preferences. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input information such as the user's appearance, personality, and background into a generation AI and have the generation AI perform avatar generation.

[0069] The registration unit can register the user's health goals and health information. For example, the registration unit can register the user's health goals. For example, the registration unit can register goals such as losing weight or achieving a state of health that allows one to travel around the world at a specific age. The registration unit can also register the user's health information. For example, the registration unit can register drinking and smoking habits, questionnaires about physical discomfort and well-being, information on health checkups and treatment history, and pulse and sleep data obtained from a smartwatch. By registering the user's health goals and health information, the accuracy of health management is improved. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's health information into the AI ​​and have the AI ​​perform the registration of the health information.

[0070] The analysis unit can provide advice based on registered health information, such as suggestions for exercise and diet, current and future health risks and countermeasures, and centralized management of past health status and medical history. For example, the analysis unit can suggest exercises to be taken based on the user's health information. For example, the analysis unit can suggest aerobic exercise or strength training. For example, the analysis unit can suggest diets based on the user's health information. For example, the analysis unit can suggest calorie restriction or nutritional balance. For example, the analysis unit can suggest current and future health risks and countermeasures based on the user's health information. For example, the analysis unit can suggest diabetes risk or heart disease risk. For example, the analysis unit can centrally manage past health status and medical history based on the user's health information. For example, the analysis unit can perform centralized management using database management or cloud storage. This allows for effective support of the user's health management by providing advice based on health information. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input the user's health information into a generating AI and have the generating AI provide health management advice.

[0071] The transfer unit can automatically transfer registered health information to the hospital or relatives when a person is transported to a hospital due to a sudden illness or injury. For example, the transfer unit can automatically transfer registered health information to a doctor at the hospital. The transfer unit can automatically transfer health information using, for example, a transfer protocol and security measures. For example, when a person is transported to a hospital due to a sudden illness or injury, the transfer unit can automatically transfer registered health information to relatives. For example, relatives can check the user's health information and use it for early investigation of the cause, treatment, and rehabilitation. This helps in rapid treatment and rehabilitation by automatically transferring health information in the event of a sudden illness or injury. Some or all of the above processes in the transfer unit may be performed using AI or not. For example, the transfer unit can input registered health information into AI and have the AI ​​perform the automatic transfer of health information.

[0072] The generation unit can estimate the user's emotions and adjust the avatar's appearance and personality based on the estimated emotions. For example, if the user is stressed, the generation unit can generate an avatar with a calm expression and a gentle personality to help them relax. For example, if the user is energetic and lively, the generation unit can generate an avatar with a bright and cheerful expression and an assertive personality. For example, if the user is depressed, the generation unit can generate an avatar that offers encouragement and comfort. By adjusting the avatar based on the user's emotions, it is possible to provide an avatar that is more suited to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI perform the adjustment of the avatar's appearance and personality.

[0073] The generation unit can analyze the user's past avatar creation history and select the optimal avatar generation method. For example, the generation unit can analyze the characteristics of avatars the user has created in the past, understand their preferences, and generate a new avatar. For example, the generation unit can suggest the optimal avatar based on the appearance and personality patterns of avatars the user has chosen in the past. For example, the generation unit can generate a more satisfying avatar by referring to the evaluations of avatars the user has created in the past. In this way, by analyzing the past avatar creation history, it is possible to provide avatars that match the user's preferences. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past avatar creation history into a generation AI and have the generation AI select the optimal avatar generation method.

[0074] The generation unit can customize the characteristics of an avatar based on the user's current lifestyle and areas of interest during avatar generation. For example, the generation unit can generate avatars related to work or hobbies according to the user's current lifestyle. For example, the generation unit can generate relevant avatars based on the user's areas of interest (sports, music, art, etc.). For example, the generation unit can generate the optimal avatar to match the user's lifestyle (outdoorsy, indoorsy, etc.). By customizing avatars based on the user's lifestyle and areas of interest, it is possible to provide avatars that are more suitable for the user. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input information about the user's current lifestyle and areas of interest into the generation AI and have the generation AI perform the customization of avatar characteristics.

[0075] The generation unit can estimate the user's emotions and determine the avatar generation order based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate avatars in order of most important elements. If the user is relaxed, the generation unit can generate avatars with detailed customization. If the user is feeling anxious, the generation unit can prioritize elements that provide a sense of security when generating avatars. This allows for the provision of more appropriate avatars by determining the avatar generation order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the avatar generation order.

[0076] The generation unit can prioritize the generation of highly relevant avatars by considering the user's geographical location information during avatar generation. For example, the generation unit can generate avatars that match the culture and customs of the area where the user lives. For example, if the user is traveling, the generation unit can generate avatars related to the area they are visiting. For example, if the user is interested in a particular region, the generation unit can generate avatars related to that region. In this way, by considering the user's geographical location information, it is possible to provide highly relevant avatars. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform avatar generation.

[0077] The generation unit can analyze a user's social media activity and generate relevant avatars when generating avatars. For example, the generation unit can generate relevant avatars based on the content a user frequently posts on social media. For example, the generation unit can analyze the trends of accounts a user follows and generate avatars that suit their preferences. For example, the generation unit can generate avatars that match the themes of online communities a user participates in. In this way, by analyzing a user's social media activity, it is possible to provide highly relevant avatars. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform avatar generation.

[0078] The registration unit can estimate the user's emotions and adjust the timing of health information registration based on the estimated emotions. For example, if the user is relaxed, the registration unit can suggest a time to register detailed health information. For example, if the user is stressed, the registration unit can suggest a time to register only simple health information. For example, if the user is busy, the registration unit can suggest inputting health information that can be registered in a short time. By adjusting the timing of health information registration based on the user's emotions, health information can be registered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input user emotion data into AI and have the AI ​​perform the adjustment of the timing of health information registration.

[0079] The registration unit can analyze the user's past health information registration history and select the optimal registration method. For example, the registration unit can suggest the optimal registration method based on the health information the user has frequently registered in the past. For example, the registration unit can prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. For example, the registration unit can predict and suggest health information to be registered at a specific time based on the user's past registration history. In this way, by analyzing the past health information registration history, a registration method tailored to the user can be provided. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input the user's past health information registration history into a generation AI and have the generation AI select the optimal registration method.

[0080] The registration unit can filter health information based on the user's current lifestyle and areas of interest when registering it. For example, the registration unit can prioritize registering health information relevant to the user's current lifestyle. For example, the registration unit can register relevant health information based on the user's areas of interest (exercise, diet, sleep, etc.). For example, the registration unit can register optimal health information tailored to the user's lifestyle (work, hobbies, family, etc.). This allows for the registration of highly relevant health information by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input information about the user's current lifestyle and areas of interest into a generation AI and have the generation AI perform the filtering of health information.

[0081] The registration unit can estimate the user's emotions and determine the priority of health information to register based on the estimated emotions. For example, if the user is stressed, the registration unit will prioritize registering stress-related health information. For example, if the user is relaxed, the registration unit can register overall health information in a balanced manner. For example, if the user is in a hurry, the registration unit can prioritize registering only important health information. In this way, by prioritizing health information based on the user's emotions, important information can be registered preferentially. 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 registration unit may be performed using AI or not. For example, the registration unit can input user emotion data into AI and have the AI ​​perform the determination of health information priorities.

[0082] The registration unit can prioritize registering highly relevant information when registering health information, taking into account the user's geographical location. For example, the registration unit can prioritize registering information related to health risks in the area where the user lives. For example, if the user is traveling, the registration unit can prioritize registering health information related to the area they are visiting. For example, if the user is interested in a particular area, the registration unit can prioritize registering health information related to that area. This allows the system to provide highly relevant health information by considering the user's geographical location. Some or all of the above processing in the registration unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the registration unit can input the user's geographical location information into a generation AI and have the generation AI perform the registration of health information.

[0083] The registration unit can analyze a user's social media activity and register relevant information when registering health information. For example, the registration unit can register relevant information based on the health-related content that the user frequently posts on social media. For example, the registration unit can analyze the trends of health-related accounts that the user follows and register relevant information. For example, the registration unit can register health information that matches the themes of online communities that the user participates in. This allows the system to provide highly relevant health information by analyzing the user's social media activity. Some or all of the above processing in the registration unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the registration unit can input data on the user's social media activity into a generative AI and have the generative AI perform the registration of health information.

[0084] The analysis unit can estimate the user's emotions and adjust the way health management advice is expressed based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand advice. For example, if the user is relaxed, the analysis unit can provide advice that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide concise advice that gets straight to the point. This allows for the provision of more appropriate advice by adjusting the way advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the way advice is expressed.

[0085] The analysis unit can estimate the user's emotions and adjust the way health management advice is expressed based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand advice. For example, if the user is relaxed, the analysis unit can provide advice that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide concise advice that gets straight to the point. This allows for the provision of more appropriate advice by adjusting the way advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the way advice is expressed.

[0086] The analysis unit can adjust the level of detail of advice based on the user's importance when analyzing health information. For example, the analysis unit can provide detailed advice based on the health goals that the user considers important. For example, the analysis unit can suggest detailed countermeasures based on the health risks that the user is concerned about. For example, the analysis unit can provide detailed analysis results based on the health information that the user prioritizes. By adjusting the level of detail of advice based on the user's importance, more effective advice can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's health information into a generation AI and have the generation AI perform the adjustment of the level of detail of the advice.

[0087] The analysis unit can apply different analysis algorithms depending on the user's category when analyzing health information. For example, if the user is an athlete, the analysis unit can apply an analysis algorithm specialized in improving athletic performance. If the user is elderly, the analysis unit can apply an analysis algorithm specialized in extending healthy life expectancy. If the user has a chronic disease, the analysis unit can apply an analysis algorithm specialized in that disease. By applying an analysis algorithm according to the user's category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input user category information into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide short, concise advice. For example, if the user is relaxed, the analysis unit can provide longer advice that includes detailed explanations. For example, if the user is excited, the analysis unit can provide advice with visually stimulating effects. By adjusting the length of the advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0089] The analysis unit can determine the priority of advice based on the timing of user submissions when analyzing health information. For example, the analysis unit can prioritize the analysis of health information recently submitted by the user and provide advice accordingly. For example, the analysis unit can determine priority based on health information that the user submits regularly. For example, the analysis unit can prioritize the analysis of health information submitted by the user before a specific event and provide advice accordingly. This allows for more timely advice to be provided by prioritizing advice based on the timing of user submissions. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input information on the timing of user submissions into a generative AI and have the generative AI determine the priority of advice.

[0090] The analysis unit can adjust the order of advice based on the user's relevance when analyzing health information. For example, the analysis unit can adjust the order of advice based on the health risks that the user considers important. For example, the analysis unit can adjust the order of advice based on the health goals that the user is interested in. For example, the analysis unit can adjust the order of advice based on the health information that the user prioritizes. By adjusting the order of advice based on the user's relevance, more effective advice can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input user relevance information into a generative AI and have the generative AI perform the adjustment of the order of advice.

[0091] The transfer unit can estimate the user's emotions and adjust the method of transferring health information based on the estimated user emotions. For example, if the user is tense, the transfer unit can provide a simple and highly visible transfer method. For example, if the user is relaxed, the transfer unit can provide a transfer method that includes detailed information. For example, if the user is in a hurry, the transfer unit can provide a concise transfer method that gets straight to the point. This allows for the provision of a more appropriate transfer method by adjusting the method of transferring health information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 transfer unit may be performed using AI or not. For example, the transfer unit can input user emotion data into AI and have the AI ​​adjust the method of transferring health information.

[0092] The transfer unit can analyze the user's past transfer history to select the optimal transfer method when transferring health information. For example, the transfer unit can propose the optimal transfer method based on the transfer method the user has used in the past. For example, the transfer unit can select a transfer method appropriate to a specific situation from the user's past transfer history. For example, the transfer unit can select the optimal transfer method by referring to a transfer method that the user has evaluated in the past. In this way, by analyzing past transfer history, it is possible to provide a transfer method that suits the user. Some or all of the above processing in the transfer unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the transfer unit can input the user's past transfer history into a generation AI and have the generation AI select the optimal transfer method.

[0093] The transfer unit can customize the means of transfer based on the user's current living situation when transferring health information. For example, the transfer unit can suggest the optimal transfer method according to the user's current living situation. For example, the transfer unit can customize the transfer method to suit the user's lifestyle (work, home, hobbies, etc.). For example, the transfer unit can select an appropriate transfer method based on the user's current health condition. By customizing the transfer method based on the user's living situation, a more appropriate transfer method can be provided. Some or all of the above processing in the transfer unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the transfer unit can input information about the user's current living situation into a generation AI and have the generation AI perform the customization of the transfer method.

[0094] The transfer unit can estimate the user's emotions and determine the priority of transferring health information based on the estimated emotions. For example, if the user is stressed, the transfer unit will prioritize transferring important health information. For example, if the user is relaxed, the transfer unit can transfer overall health information in a balanced manner. For example, if the user is in a hurry, the transfer unit can prioritize transferring only important health information. This ensures that important information is transferred preferentially by determining the priority of transferring health information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 transfer unit may be performed using AI or not. For example, the transfer unit can input user emotion data into an AI and have the AI ​​determine the priority of transferring health information.

[0095] The transfer unit can select the optimal transfer method when transferring health information, taking into account the user's geographical location. For example, the transfer unit may prioritize transferring information related to medical institutions in the user's area of ​​residence. For example, if the user is traveling, the transfer unit may prioritize transferring health information related to the area the user is visiting. For example, if the user is interested in a particular region, the transfer unit may prioritize transferring health information related to that region. This allows for the provision of a highly relevant transfer method by considering the user's geographical location. Some or all of the above processing in the transfer unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the transfer unit may input the user's geographical location information into a generative AI and have the generative AI select the transfer method.

[0096] The transfer unit can analyze the user's social media activity and propose a transfer method when transferring health information. For example, the transfer unit can transfer relevant information based on the health-related content that the user frequently posts on social media. For example, the transfer unit can analyze the trends of health-related accounts that the user follows and transfer relevant information. For example, the transfer unit can transfer health information that matches the theme of the online community the user participates in. In this way, by analyzing the user's social media activity, a highly relevant transfer method can be provided. Some or all of the above processing in the transfer unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the transfer unit can input data on the user's social media activity into a generative AI and have the generative AI propose a transfer method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] A health management system can estimate a user's emotions and provide health management advice based on those emotions. For example, if a user is stressed, it can suggest relaxing exercises or dietary recommendations. If a user is energetic and active, it can suggest energetic activities or challenging goals. If a user is depressed, it can offer encouraging and comforting advice. By providing appropriate advice based on the user's emotions, it can support more effective health management. Emotion estimation is achieved using an emotion engine or generative AI. The analysis unit inputs the user's emotion data into the generative AI, which then provides the advice.

[0099] The health management system can provide health management advice while considering the user's geographical location. For example, it can suggest meals tailored to the climate and food culture of the area where the user lives. If the user is traveling, it can suggest health risks and countermeasures related to the area they are visiting. If the user is interested in a particular region, it can provide health information related to that region. In this way, by considering the user's geographical location, more relevant advice can be provided. The analysis unit inputs the user's geographical location information into the generating AI, and the generating AI can then execute the provision of advice.

[0100] The health management system can analyze a user's social media activity and provide relevant health management advice. For example, it can provide relevant advice based on the health-related content a user frequently posts on social media. It can analyze the trends of health-related accounts a user follows and provide advice tailored to their preferences. It can provide advice aligned with the themes of online communities a user participates in. In this way, by analyzing a user's social media activity, it can provide highly relevant advice. The analysis unit inputs data on the user's social media activity into a generating AI, which then generates the advice.

[0101] The health management system can estimate the user's emotions and adjust the timing of health information registration based on those emotions. For example, if the user is relaxed, it can suggest a time to register detailed health information. If the user is stressed, it can suggest a time to register only basic health information. If the user is busy, it can suggest entering health information that can be registered in a short amount of time. By adjusting the timing of health information registration based on the user's emotions, health information can be registered at a more appropriate time. The registration unit inputs the user's emotion data into a generating AI, which then performs the adjustment of the health information registration timing.

[0102] The health management system can analyze a user's past health information registration history and select the optimal registration method. For example, it can suggest the optimal registration method based on the health information the user has frequently registered in the past. It can prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. It can predict and suggest health information to be registered at specific times based on the user's past registration history. In this way, by analyzing past health information registration history, it can provide a registration method that suits the user. The registration unit inputs the user's past health information registration history into a generating AI, which then performs the task of selecting the optimal registration method.

[0103] The health management system can estimate the user's emotions and prioritize health information based on those emotions. For example, if the user is stressed, stress-related health information can be registered first. If the user is relaxed, overall health information can be registered in a balanced manner. If the user is in a hurry, only important health information can be registered first. In this way, by prioritizing health information based on the user's emotions, important information can be registered first. The registration unit inputs the user's emotion data into a generating AI, which then performs the task of determining the priority of health information.

[0104] The health management system can register health information while considering the user's geographical location. For example, it can prioritize registering information related to health risks in the area where the user lives. If the user is traveling, it can prioritize registering health information related to the area they are visiting. If the user is interested in a particular region, it can prioritize registering health information related to that region. In this way, by considering the user's geographical location, it can provide highly relevant health information. The registration unit inputs the user's geographical location information into a generation AI, and the generation AI can then perform the registration of health information.

[0105] The health management system can estimate the user's emotions and adjust the way health management advice is presented based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand advice. If the user is relaxed, it can provide advice that includes detailed information. If the user is in a hurry, it can provide concise advice that gets straight to the point. By adjusting the way advice is presented based on the user's emotions, it can provide more appropriate advice. The analysis unit inputs the user's emotion data into the generating AI, which then adjusts the way the advice is presented.

[0106] The health management system can prioritize health management advice based on when the user submits it. For example, it can prioritize analyzing health information recently submitted by the user and provide advice accordingly. It can also prioritize health information that the user submits regularly. Furthermore, it can prioritize analyzing health information submitted by the user before a specific event and provide advice accordingly. This allows for more timely advice by prioritizing advice based on when the user submits it. The analysis unit inputs information about the user's submission timing into the generating AI, which then performs the task of determining the priority of the advice.

[0107] The health management system can estimate the user's emotions and adjust the method of transferring health information based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand transfer method. If the user is relaxed, it can provide a transfer method that includes detailed information. If the user is in a hurry, it can provide a concise transfer method that gets straight to the point. By adjusting the method of transferring health information based on the user's emotions, a more appropriate method can be provided. The transfer unit inputs the user's emotion data into a generating AI, which then performs the adjustment of the health information transfer method.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The generation unit generates an avatar tailored to the user's preferences. The generation unit generates an avatar based on information such as the user's appearance, personality, and background. The generation unit can generate avatars tailored to the user's preferences, such as realistic, anime, or character-style avatars. The generation unit generates an avatar based on the user's physical characteristics, such as facial features and body type. The generation unit generates an avatar based on the user's personality traits, such as introverted or extroverted personality traits. The generation unit generates an avatar based on information about the user's background, such as work history and educational background. Step 2: The registration unit registers the user's health information. The registration unit registers, for example, the user's health goals and health information. For example, the registration unit can register goals such as losing weight or achieving a state of health that allows one to travel around the world at a specific age. For example, the registration unit can register drinking and smoking habits, questionnaires about physical discomfort and well-being, information on health checkups and treatment history, and pulse and sleep data obtained from the smartwatch. Step 3: The analysis unit analyzes the registered health information and provides health management advice. The analysis unit provides advice such as suggestions for exercise and diet to be incorporated, current and future health risks and countermeasures, and centralized management of past health status and medical history. For example, the analysis unit can suggest exercise to be incorporated based on the user's health information. For example, it can suggest aerobic exercise or strength training. For example, the analysis unit can suggest diet based on the user's health information. For example, it can suggest calorie restriction or nutritional balance. For example, the analysis unit can suggest current and future health risks and countermeasures based on the user's health information. For example, it can suggest diabetes risk or heart disease risk. For example, the analysis unit can centrally manage past health status and medical history based on the user's health information. For example, centralized management can be done using database management or cloud storage. Step 4: The transfer unit automatically transfers health information when a person is transported to a hospital due to a sudden illness or injury. The transfer unit automatically transfers registered health information to the hospital or to relatives, for example. The transfer unit can automatically transfer health information using, for example, a transfer protocol and security measures. For example, when a person is transported to a hospital due to a sudden illness or injury, the transfer unit automatically transfers registered health information to the doctor at the hospital. For example, the doctor at the hospital can check the user's health information and provide appropriate treatment quickly. For example, when a person is transported to a hospital due to a sudden illness or injury, the transfer unit automatically transfers registered health information to relatives. For example, relatives can check the user's health information and use it for early investigation of the cause, treatment, and rehabilitation.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the generation unit, registration unit, analysis unit, and transfer unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and generates an avatar tailored to the user's preferences. The registration unit is implemented by the control unit 46A of the smart device 14 and registers the user's health information. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the registered health information and provides health management advice. The transfer unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically transfers health information when the user is transported to a hospital due to a sudden illness or injury. 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.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the generation unit, registration unit, analysis unit, and transfer unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and generates an avatar tailored to the user's preferences. The registration unit is implemented by the control unit 46A of the smart glasses 214 and registers the user's health information. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the registered health information and provides health management advice. The transfer unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically transfers health information when the user is transported to a hospital due to a sudden illness or injury. 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.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the generation unit, registration unit, analysis unit, and transfer unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and generates an avatar tailored to the user's preferences. The registration unit is implemented by the control unit 46A of the headset terminal 314 and registers the user's health information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the registered health information and provides health management advice. The transfer unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically transfers health information when the user is transported to a hospital due to a sudden illness or injury. 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.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the generation unit, registration unit, analysis unit, and transfer unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and generates an avatar tailored to the user's preferences. The registration unit is implemented by the control unit 46A of the robot 414 and registers the user's health information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the registered health information and provides health management advice. The transfer unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically transfers health information when the user is transported to a hospital due to a sudden illness or injury. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A generation unit that generates avatars tailored to the user's preferences, A registration section for registering the user's health information, An analysis unit analyzes the health information registered by the aforementioned registration unit and provides health management advice, It includes a transfer unit that automatically transfers health information when a person is transported to a hospital due to a sudden illness or injury. A system characterized by the following features. (Note 2) The generating unit is Avatars are generated based on information such as the user's appearance, personality, and background. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned registration unit is Register your health goals and health information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Based on registered health information, the service provides advice such as suggestions for exercise and diet, current and future health risks and countermeasures, and centralized management of past health conditions and medical history. The system described in Appendix 1, characterized by the features described herein. (Note 5) The transfer unit is, If you are transported to a hospital due to a sudden illness or injury, your registered health information will be automatically forwarded to the hospital or your relatives. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is It estimates the user's emotions and adjusts the avatar's appearance and personality based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is The system analyzes the user's past avatar creation history and selects the optimal avatar generation method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is When creating an avatar, its features are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is The system estimates the user's emotions and determines the order in which avatars are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating avatars, the system prioritizes generating highly relevant avatars by taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating avatars, the system analyzes the user's social media activity and generates relevant avatars. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of health information registration based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned registration unit is Analyze the user's past health information registration history and select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned registration unit is When registering health information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned registration unit is The system estimates the user's emotions and prioritizes the health information to register based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned registration unit is When registering health information, the system prioritizes registering highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned registration unit is When registering health information, the system analyzes the user's social media activity and registers relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the way health management advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the way health management advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, When analyzing health information, the level of detail in advice is adjusted based on the user's perceived importance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, When analyzing health information, different analysis algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, When analyzing health information, the system prioritizes advice based on when the user submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, When analyzing health information, the order of advice is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The transfer unit is, It estimates the user's emotions and adjusts how health information is transmitted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The transfer unit is, When transferring health information, the system analyzes the user's past transfer history to select the optimal transfer method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The transfer unit is, When transferring health information, the transfer method is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The transfer unit is, The system estimates the user's emotions and determines the priority of transferring health information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The transfer unit is, When transferring health information, the optimal transfer method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The transfer unit is, When transferring health information, the system analyzes the user's social media activity and suggests methods for transferring the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 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 generation unit that generates avatars tailored to the user's preferences, A registration section for registering the user's health information, An analysis unit analyzes the health information registered by the aforementioned registration unit and provides health management advice, It includes a transfer unit that automatically transfers health information when a person is transported to a hospital due to a sudden illness or injury. A system characterized by the following features.

2. The generating unit is Avatars are generated based on information such as the user's appearance, personality, and background. The system according to feature 1.

3. The aforementioned registration unit is Register your health goals and health information. The system according to feature 1.

4. The aforementioned analysis unit, Based on registered health information, the service provides advice such as suggestions for exercise and diet, current and future health risks and countermeasures, and centralized management of past health conditions and medical history. The system according to feature 1.

5. The transfer unit is, If you are transported to a hospital due to a sudden illness or injury, your registered health information will be automatically forwarded to the hospital or your relatives. The system according to feature 1.

6. The generating unit is It estimates the user's emotions and adjusts the avatar's appearance and personality based on those estimated emotions. The system according to feature 1.

7. The generating unit is The system analyzes the user's past avatar creation history and selects the optimal avatar generation method. The system according to feature 1.

8. The generating unit is When creating an avatar, its features are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The generating unit is The system estimates the user's emotions and determines the order in which avatars are generated based on those estimated emotions. The system according to feature 1.

10. The generating unit is When generating avatars, the system prioritizes generating highly relevant avatars by taking into account the user's geographical location information. The system according to feature 1.

Images