system

Abstract

We provide the system. [Solution] A means of receiving and storing health data from users, A generation method that uses the health data to predict future health status, A means of generating an avatar that visualizes a predicted future health state, A means of creating advice about current lifestyle habits based on the generated avatar and health information, Means for transmitting the aforementioned avatar and advice to the user terminal, A system that includes this.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 modern society, the increase in lifestyle diseases and preventable diseases is remarkable. However, many people do not recognize their own health risks and cannot take specific improvement measures. There is a need for a technology to specifically visualize future health conditions based on individual health data and present effective preventive measures.

Means for Solving the Problems

[0005] This invention provides a system that receives and stores health data from users, such as health checkup information, lifestyle data, and treatment history, and uses generated AI to predict future health conditions based on that data. Furthermore, it generates an avatar that visually represents the predicted health condition and provides the user with specific advice on how to improve their current lifestyle. This system allows users to intuitively understand their own health risks and take early preventive measures.

[0006] "Health data" refers to information necessary to understand a user's health status, and includes health checkup information, lifestyle data, and treatment history.

[0007] "Generation method" refers to a means of predicting future health status based on health data received from users, and includes a simulation process using generation AI.

[0008] An "avatar" is a visual representation of a predicted future health state, a digital representation that allows users to intuitively understand their health status.

[0009] "Advice" includes specific action plans for improving current lifestyle habits, derived from the generated avatar and health information.

[0010] A "user terminal" is a device used by users to input their health data and to view the generated avatar and advice. [Brief explanation of the drawing]

[0011] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] 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.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple 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), and the like.

[0018] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] The 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.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention is implemented as a system to provide advanced support for user health management. This system is based on the cooperation of the server, terminal, and user.

[0033] The server first receives health data from the user and their family. This data includes health checkup information, lifestyle data, and treatment history. The server stores this data and, based on it, executes a generation mechanism to predict future health status. The generation mechanism uses AI technology to evaluate the user's future health risks and obtains prediction results. Furthermore, based on the obtained predictions, it generates an avatar that visually represents the future health status.

[0034] The server then generates an avatar and advice based on the predictions, and sends this to the user's terminal. The terminal receives the data from the server and displays it in a way that is easy for the user to understand. For this purpose, the terminal uses a graphical user interface (GUI) to clearly present the predicted health status and advice.

[0035] Users review results generated based on health data entered by themselves or their family members. For example, if the predictive avatar indicates a high risk of diabetes, the system provides advice such as "increase exercise and change to a diet that restricts sugar." Users can use this advice to review and improve their lifestyle. When feedback is provided to the system, the server can use that information to improve the predictive model.

[0036] Thus, the system of the present invention supports effective health management by predicting future risks based on health data and presenting them to the user through both visual and behavioral guidance. Through this process, users can adjust their daily behaviors toward a healthier direction and strive for prevention.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] Users enter their personal and family health data into the terminal. They input health checkup information, lifestyle questionnaire results, past treatment history, etc., and prepare to send it to the system.

[0040] Step 2:

[0041] The terminal formats the user's input data, verifies data integrity as needed, encrypts it, and then sends it to the server.

[0042] Step 3:

[0043] The server receives health data sent from the terminal and stores it in a database. During this process, it performs data classification and consistency checks.

[0044] Step 4:

[0045] The server inputs stored health data into an AI model and performs a process to predict future health conditions. This includes comparing the data with existing big data.

[0046] Step 5:

[0047] The server generates an avatar based on the prediction results. This avatar visualizes the user's future health status.

[0048] Step 6:

[0049] The server generates specific advice to improve the predicted health condition and sends it to the user's terminal along with the avatar.

[0050] Step 7:

[0051] The terminal displays avatars and advice received from the server, enabling users to understand the information visually and intuitively.

[0052] Step 8:

[0053] Users can review and improve their lifestyle habits based on the information presented. They can also evaluate the advice and provide feedback to the server via their device, along with any new information.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In recent years, with the diversification of lifestyles and the rise in health awareness, there has been a growing need to effectively manage and improve individual health conditions. However, conventional health management systems have struggled to utilize detailed individual health data to predict future health conditions and provide concrete, actionable guidelines. Therefore, the challenge is to provide a system that can utilize users' health data to predict future health conditions and offer individually customized advice.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for receiving and recording biometric data from a user, a generation function for processing the biometric data to estimate future health status, and means for generating a digital representation that visualizes the estimated future health status. This enables the user to predict their own health risks and obtain concrete action guidelines for improvement.

[0059] A "user" refers to anyone who uses the system to manage their own or their family's health.

[0060] "Biometric data" refers to information about the health of the user and their family, and includes various data such as health checkup results, lifestyle data, and medical history.

[0061] "Generative function" refers to technical means of performing data processing and analysis using received biometric data to estimate future health conditions.

[0062] "Digital representation" refers to digitized avatars and other forms of representation that visually visualize predicted future health conditions and present them in a format that is easy for users to understand.

[0063] "Advice" refers to actionable guidelines provided to the user based on their predicted health status, including suggestions for improving lifestyle habits.

[0064] "User device" refers to a mobile terminal, computer screen, or other display device used by a user to receive and refer to information from the system.

[0065] This invention is implemented as a system that provides advanced support for user health management. In this system, the server, terminal, and user work together to predict future risks based on health data and provide intuitive feedback to the user.

[0066] The server first receives and records biometric data of the user and their family. This biometric data includes health checkup results, lifestyle data, and medical history. This data is stored in a database system, using software such as MySQL® or MongoDB.

[0067] Next, the server preprocesses the data and uses a generative AI model to estimate future health status. This generative AI model utilizes machine learning frameworks such as TENSORFLOW® and PyTorch, where a neural network calculates future health risks. The processed data is used to predict health risks, for example, assessing the potential for increased risk of diabetes.

[0068] Furthermore, based on the estimation results, the server generates a digital representation, namely an avatar of the user's future health status. Therefore, tools such as Unity are used to render the avatar using 3D graphics and provide it to the user.

[0069] The server generates digital representations and specific advice, and then transfers this information to the user's terminal. The terminal presents the information received from the server to the user through a GUI. The terminal's software utilizes frameworks such as React Native and Flutter®, allowing for an intuitive display of visual information.

[0070] Users can review the information and advice presented and adjust their lifestyle based on that advice. For example, if the predicted avatar shows a high risk of diabetes, the system will suggest advice such as "increase your exercise and choose a diet that limits sugar."

[0071] An example of a prompt message would be, "Predict future health risks based on this user's health data and generate advice." This system provides users with an effective means to maintain a healthy lifestyle.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server receives biometric data from users and their families and stores it in a database. Inputs include health checkup results, lifestyle data, and medical history. The server retrieves this data and stores it in the database in an organized format. For example, users can input data through a smartphone app and send it to the server, thus accumulating information.

[0075] Step 2:

[0076] The server preprocesses the ingested health data. Specifically, it imputes missing values ​​in the input data and detects and corrects outliers. Furthermore, it performs encoding to convert categorical data into numerical data. During this process, data cleansing is performed using data processing libraries such as Pandas. The output is a clean dataset that can be used by AI models.

[0077] Step 3:

[0078] The server uses pre-processed data as input and predicts future health risks using a generative AI model. Specifically, it uses TensorFlow or PyTorch to build a neural network model and assess the risks. The output obtained here is the probability value of each user's predicted health risk.

[0079] Step 4:

[0080] The server generates a digital representation that visualizes future health status based on predicted health risks. Here, Unity and other visual tools are used to create avatars, and the health status is reflected in these avatars. The input is probability values ​​of risk, and the output is in the form of a digital avatar.

[0081] Step 5:

[0082] The server generates advice based on the generated avatar and individual health status. This advice is materialized using natural language generation technology. Using a GPT-based model, it generates advice as text based on prompts. The output is a personalized guideline for improving lifestyle habits.

[0083] Step 6:

[0084] The terminal receives avatars and advice sent from the server and presents them to the user via a GUI. Utilizing React Native and Flutter, it provides a screen layout that allows for visual and intuitive data verification. Input is information from the server, and output is a visually displayed avatar and textual advice.

[0085] Step 7:

[0086] Users re-evaluate their lifestyle habits based on the information displayed on their device and make improvements as needed. Furthermore, users can provide feedback to the system regarding the results of their lifestyle changes. This feedback allows the server to further train the AI ​​model and improve its prediction accuracy.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] In modern society, many people are conscious of health management in their daily lives, but often lack concrete action plans. Furthermore, especially in physical fitness facilities such as gyms, users are expected to visually understand their own health status and use that understanding to improve their training and lifestyle habits. However, existing systems have faced the challenge of providing detailed predictions and guidance based on individual users' health data.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for receiving and storing health data from a user, means for generating data to predict future health status using the health data, and means for generating an avatar that visualizes the predicted future health status. This makes it possible for users in physical stores such as fitness gyms to visually check their own health status and receive specific training guidelines and advice on improving their lifestyle based on that information.

[0092] "Means for receiving and storing health data from users" refers to a function that receives health-related information provided by users, stores it, and makes it available for later processing.

[0093] The "generation method" is a function that uses AI technology to predict the user's future health status based on the received health data.

[0094] "Means of generating avatars" refers to the function of creating a virtual character to visually represent a predicted health state.

[0095] The "means of generating advice" refer to a function that provides specific advice to help users review their current lifestyle habits based on the generated avatar and health information.

[0096] "Means of sending to the user's terminal" refers to a function that delivers the generated avatar and advice to a device that the user can easily view.

[0097] "Means of displaying information within physical stores" refers to a function that allows users to visually check health-related information within fitness gyms and other physical stores.

[0098] The server first receives health data from the user and their family. This data includes health checkup information, lifestyle data, and exercise history. The server stores this data using Amazon Web Services (AWS®) S3. Next, based on this health data, the server executes a generation process using AI technology to predict future health status. Machine learning libraries such as TensorFlow are used for this generation process. The server uses Unity to visualize the predicted health status and generate a 3D avatar. The generated avatar is implemented using React Native for display on the user's terminal's graphical user interface (GUI).

[0099] The terminal presents data received from the server to the user in an easy-to-understand format. To achieve this, the terminal utilizes a graphical user interface (GUI) to display predicted health status and advice. For example, in a fitness gym, the terminal displays training advice along with the user's health avatar. This allows users to receive specific training plans and lifestyle improvement suggestions based on their own health data. Kiosk terminals installed in physical stores also support this, making it easily accessible to users within the store.

[0100] Users regularly input their own or their family's health data and review the results. For example, when a user uses a kiosk terminal at a fitness gym to display a health prediction avatar, if a risk of weight gain is indicated, they can receive specific advice such as "exercise aerobics at least three times a week" and "focus on a high-protein, low-calorie diet."

[0101] An example of a prompt for the generating AI model is as follows: "Based on the user's health data, predict their health status for the next three months and generate a 3D avatar. Based on the generated avatar, create and present training and dietary advice to the user." This process allows users to manage their health more intuitively and efficiently.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] Users enter health data using their smartphones or kiosk terminals in physical stores. This data includes health checkup information, lifestyle data, and exercise history. This data is sent to a server and stored in AWS S3.

[0105] Step 2:

[0106] The server inputs the received health data into an AI model to predict future health status. The generative AI model used in this step is built using TensorFlow and analyzes the data to assess future risks. The output is the user's predicted future health status.

[0107] Step 3:

[0108] The server generates a 3D avatar based on the prediction results. The generated avatar is visualized using Unity. This avatar visually represents the user's future health status, and can, for example, visualize predicted weight gain or loss.

[0109] Step 4:

[0110] The server generates health improvement advice for the user based on the generated avatar and prediction results. The advice is generated by an AI model and specifically shows how to improve the user's lifestyle. For example, it may advise "do 30 minutes of aerobic exercise three times a week" or "try to eat a diet low in sugar."

[0111] Step 5:

[0112] The server sends the generated 3D avatar and advice to the user's device. The device displays this information using a graphical user interface (GUI). It is designed using React Native to allow for intuitive viewing of the avatar and advice.

[0113] Step 6:

[0114] Based on the presented avatar and advice, users review their daily lives and training plans and provide feedback to the system. This feedback is sent to the server and used to improve future predictive models. This allows the system to provide users with more accurate health management advice.

[0115] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0116] This invention provides more personalized health support by combining an emotion engine with a system designed to improve user health management. This system operates through the collaborative efforts of the server, terminal, and user.

[0117] The server first receives and stores health data collected from the user and their family. This data includes health checkup information, lifestyle data, and treatment history. The server uses this data to predict future health status using an AI model. Furthermore, it generates an avatar based on the prediction results, and this avatar helps visualize the user's future health status.

[0118] The emotion engine recognizes the user's emotional state and provides that information to the server. The server then uses the data from the emotion engine to generate emotionally appropriate advice. For example, if the user is feeling stressed, the server can emphasize specific relaxation techniques.

[0119] The generated emotion-responsive avatar and advice are sent to the user's device. The device displays the information received from the server in a way that is easy for the user to understand. For example, even if the user is in an unstable emotional state, the device will present the information using reassuring designs and messages.

[0120] Users receive health advice tailored to their health status and emotions via their device. This allows users to better improve their lifestyle and maintain or improve their health. User feedback is sent to the server and contributes to system improvement.

[0121] This system enables personalized healthcare that takes emotions into account, allowing for more effective management of users' health risks.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] Users enter personal and family health data into the device. This includes health checkup information, lifestyle data, and treatment history, preparing it for transmission to the system.

[0125] Step 2:

[0126] The terminal formats the entered health data, verifies its integrity, encrypts it, and then sends it to the server.

[0127] Step 3:

[0128] The server receives data sent from the terminal, organizes it in a database, and stores it. The stored data is then input into an AI model for predicting health status.

[0129] Step 4:

[0130] An AI model runs on the server to predict the user's future health status. Based on the prediction results, an avatar is generated that visualizes the user's health.

[0131] Step 5:

[0132] The device acquires data on the user's facial expressions and voice, and analyzes the user's emotions using an emotion engine. This emotional information is then sent to the server.

[0133] Step 6:

[0134] The server receives data from the emotion engine and generates health advice tailored to the user's emotional state. It creates advice that includes improvement suggestions that match the user's emotions.

[0135] Step 7:

[0136] The server sends an avatar and advice that corresponds to the user's emotions to the user's terminal. The avatar is designed with emotional sensitivity in mind.

[0137] Step 8:

[0138] The device displays received information and provides health advice in a user-friendly format. The user interface is designed with emotions in mind.

[0139] Step 9:

[0140] Users review and improve their lifestyle habits based on the advice provided by the device. They also provide feedback on the results of the advice and their impressions via the device, helping to improve the system.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] In modern society, personal health management is becoming increasingly important, but conventional health support systems have struggled to provide personalized advice that takes into account individual emotional states. Furthermore, there was a lack of technology to integrate health and emotional information and present it clearly to users. As a result, users found it difficult to take health-related actions that aligned with their emotions, potentially leading to a deterioration in their health.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes means for receiving and storing biometric data, means for estimating future health conditions, means for generating virtual representations, means for recognizing emotional states, means for creating personalized lifestyle guidance, and means for communicating this guidance to a user interface. This enables the user to comprehensively understand their own health and emotional states and select appropriate health actions based on that understanding.

[0146] "Biometric data" refers to information that indicates a user's health status, and includes data such as test results, lifestyle patterns, and treatment history.

[0147] "Future health status" refers to the predicted state of the user's health, estimated based on current health information.

[0148] "Estimation means" refers to a method or device for predicting future health status from received biometric data using technologies such as AI models.

[0149] A "virtual representation" is a digital avatar or graphic generated to visually represent the predicted results of a user's health status.

[0150] "Emotional state" refers to information that represents the user's psychological state, and is data obtained by analyzing facial expressions, voice, and other factors.

[0151] "Emotional analysis means" refers to a technology or device that analyzes a user's facial expressions, voice, etc., in real time to identify their emotional state.

[0152] "Individualized lifestyle guidance" refers to advice that includes specific actions and precautions tailored to the user's health and emotional state.

[0153] A "user interface" is a device or method for a user to receive information from a system, and includes terminals and screen displays.

[0154] This invention is a system for improving user health management, utilizing biometric data and emotional states to provide personalized health advice. The system functions through the cooperation of the server, terminal, and user.

[0155] The server first receives diverse biometric data from the user and their family and stores it in a database. This biometric data includes examination information, lifestyle pattern data, and treatment history. Based on the received data, the server uses machine learning libraries such as TensorFlow and PyTorch to build an AI model and predict the user's future health status. Based on this prediction, the server uses software such as Unity to generate a virtual representation that visualizes the future health status.

[0156] Regarding emotion analysis, when a user accesses the system through a terminal, the server analyzes facial expressions and voice data using emotion analysis tools. This technology utilizes OpenCV and relevant analysis software. The server considers these analysis results and uses a generative AI model to generate personalized health advice tailored to the user's emotions. For example, it generates advice based on a prompt such as, "Please suggest specific relaxation methods for when the user is feeling stressed."

[0157] The generated virtual representation and personalized advice are sent to the terminal. The terminal displays these visually and in text through an interface that is easy for the user to understand. Based on this, the user can select and implement actions that are appropriate to their health condition and emotions. This feedback is sent back to the server, and the accumulated data is used to continuously improve the accuracy and usefulness of the entire system.

[0158] As a concrete example, when the server inputs the prompt "Please tell me specific vegetables that are recommended for improving my diet" into the AI ​​model, it outputs customized dietary guidance. In this way, users can receive specific advice that they can implement in their daily lives.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The server receives biometric data sent from users and their families and stores it in a database. This data includes examination information, lifestyle pattern data, and treatment history. By receiving and storing biometric data as input, it builds a foundation for estimating future health status.

[0162] Step 2:

[0163] The server uses stored biometric data to build an AI model and predict future health status. Here, the AI ​​model is formed using machine learning libraries such as TensorFlow. Based on the input biometric data, it numerically and statistically analyzes the user's health risks and future health status, and outputs the results.

[0164] Step 3:

[0165] The server uses the predicted health status results to generate a virtual avatar. This uses Unity or similar software to visually represent future health status. Based on the predicted results as input, it generates graphic data and outputs it in a format that the user can easily understand.

[0166] Step 4:

[0167] The server recognizes the user's emotional state in real time using emotion analysis tools. It takes facial expression data and voice data as input, performs analysis using technologies such as OpenCV, and returns the results to the server. This provides the emotional analysis results.

[0168] Step 5:

[0169] The server considers the analyzed emotional information and uses a generative AI model to create personalized health advice. Taking the prompt "What refreshing methods would you recommend when a user is feeling tired?" as input, the generative AI model outputs specific lifestyle guidance plans.

[0170] Step 6:

[0171] The terminal, upon receiving data from the server, displays virtual representations and advice in a user-friendly interface. The design and messaging are carefully crafted to ensure user emotional reassurance. By outputting the received data onto the screen, users can intuitively understand the information.

[0172] Step 7:

[0173] Users provide feedback based on the avatars and advice they receive. This feedback is sent to the server and used to improve the system. Users input their experiences, impressions, and improvement requests, which are then used to improve the overall accuracy and performance of the system as output.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0176] In modern society, comprehensively managing people's health and providing health support tailored to individual emotional states is a crucial challenge. However, conventional health management systems struggle to provide health advice that adequately considers emotional states, resulting in insufficient improvement in users' lifestyles. Therefore, there is a need to realize health support that comprehensively analyzes users' health and emotions and encourages specific actions appropriate to those emotions.

[0177] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0178] In this invention, the server includes means for receiving and storing health data and emotional data from a user, generation means for predicting future health status using said health data and emotional data, and means for generating a virtual display that visualizes the predicted future health status and emotional status. This makes it possible to provide personalized health advice according to the user's emotional status and efficiently promote improvements in lifestyle habits.

[0179] "Health data" refers to information related to a user's physical condition and health, including health checkup information, lifestyle information, and treatment records.

[0180] "Emotional data" refers to information about the user's psychological state, including data such as the type and intensity of emotions obtained by the emotion engine.

[0181] The "generation means" refers to a processing means that uses an AI model to predict the future health state based on health data and emotional data received from the user, and generates a virtual display based on that prediction.

[0182] "Virtual representation" refers to avatars or graphics that visually represent predicted future health and emotional states, displayed in a way that is easy for the user to understand.

[0183] "Advice" includes suggestions and instructions provided to users to encourage improvements in their health and lifestyle, and is personalized according to the user's emotional state.

[0184] "Terminal" refers to equipment used by a user to utilize the system of the present invention, and includes devices such as smart glasses and smartphones.

[0185] In order to implement this invention, the user, server, and terminal must cooperate to build a system. The server receives and stores health data and emotional data from the user. Health data includes health checkup information, lifestyle information, treatment records, etc., and emotional data includes information about the user's emotional state obtained from the emotional engine.

[0186] The server uses this data to predict future health status using a generative AI model. This model employs deep learning libraries such as TensorFlow and PyTorch. Based on the prediction results, the server generates a virtual representation that visualizes future health and emotional states. The generated virtual representation is presented in a user-friendly format, such as an avatar or graphic.

[0187] Furthermore, the server generates personalized health advice based on virtual displays and health information, tailored to the user's emotional state. This advice is calculated by an AI model, taking into account past data and the user's current emotional state, and includes specific action guidelines.

[0188] The terminal is responsible for displaying virtual representations and advice sent from the server to the user. The terminal includes smart glasses and smartphones, allowing the user to receive advice visually. The terminal uses visual devices and processors to display information in real time.

[0189] As a concrete example of implementation, if the user detects stress, the device can offer suggestions for deep breathing and relaxation techniques in the form of a virtual counselor. Furthermore, the AI ​​model can generate appropriate advice using prompt examples such as: "If the user's emotional state indicates stress, what relaxation method would be best?"

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server receives health and emotional data submitted by users and stores them in a database. Inputs are data indicating the user's health checkup information and emotional state, while outputs are the health and emotional data registered in the database. This process involves format conversion to maintain data integrity.

[0193] Step 2:

[0194] The server inputs stored health and emotion data into an AI model to predict future health status. The input is formatted health and emotion data, and the output is numerical or textual information representing the predicted health status. Here, a generative AI model (e.g., TensorFlow) is used to perform the prediction calculation.

[0195] Step 3:

[0196] The server generates a visually easy-to-understand virtual representation based on predicted future health and emotional states. The input is predicted health and emotional data, and the output is a virtual avatar or graphic. Visualized graphic content is generated using design software.

[0197] Step 4:

[0198] The server generates personalized health advice based on the user's emotional state, along with a virtual display. Inputs include the virtual display, health information, and user emotion data, while output is advice containing specific action guidelines. The AI ​​model is queried using prompts (e.g., "If the user's emotional state indicates stress, what relaxation methods are best?") to generate appropriate advice.

[0199] Step 5:

[0200] The device displays virtual information and health advice received from the server to the user. The input is the virtual information and advice sent from the server, and the output is the information the user receives visually. The device includes smart glasses and smartphones, enabling real-time information presentation through applications.

[0201] Step 6:

[0202] Users raise their awareness of lifestyle habits and health through virtual displays and advice shown on their devices. Input is information from the device, and output is the user's review of their behavior and implementation of improvement measures. Users are expected to take concrete actions in their daily lives based on the information they receive.

[0203] 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.

[0204] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0210] 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.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0212] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] 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.

[0214] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The 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.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] This invention is implemented as a system to provide advanced support for user health management. This system is based on the cooperation of the server, terminal, and user.

[0220] The server first receives health data from the user and their family. This data includes health checkup information, lifestyle data, and treatment history. The server stores this data and, based on it, executes a generation mechanism to predict future health status. The generation mechanism uses AI technology to evaluate the user's future health risks and obtains prediction results. Furthermore, based on the obtained predictions, it generates an avatar that visually represents the future health status.

[0221] The server then generates an avatar and advice based on the predictions, and sends this to the user's terminal. The terminal receives the data from the server and displays it in a way that is easy for the user to understand. For this purpose, the terminal uses a graphical user interface (GUI) to clearly present the predicted health status and advice.

[0222] Users review results generated based on health data entered by themselves or their family members. For example, if the predictive avatar indicates a high risk of diabetes, the system provides advice such as "increase exercise and change to a diet that restricts sugar." Users can use this advice to review and improve their lifestyle. When feedback is provided to the system, the server can use that information to improve the predictive model.

[0223] Thus, the system of the present invention supports effective health management by predicting future risks based on health data and presenting them to the user through both visual and behavioral guidance. Through this process, users can adjust their daily behaviors toward a healthier direction and strive for prevention.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] Users enter their personal and family health data into the device. They input health checkup information, lifestyle questionnaire results, past treatment history, etc., and prepare to send it to the system.

[0227] Step 2:

[0228] The terminal formats the user's input data, verifies data integrity as needed, encrypts it, and then sends it to the server.

[0229] Step 3:

[0230] The server receives health data sent from the terminal and stores it in a database. During this process, it performs data classification and consistency checks.

[0231] Step 4:

[0232] The server inputs stored health data into an AI model and performs a process to predict future health conditions. This includes comparing the data with existing big data.

[0233] Step 5:

[0234] The server generates an avatar based on the prediction results. This avatar visualizes the user's future health status.

[0235] Step 6:

[0236] The server generates specific advice to improve the predicted health condition and sends it to the user's terminal along with the avatar.

[0237] Step 7:

[0238] The terminal displays avatars and advice received from the server, enabling users to understand the information visually and intuitively.

[0239] Step 8:

[0240] Users can review and improve their lifestyle habits based on the information presented. They can also evaluate the advice and provide feedback to the server via their device, along with any new information.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0243] In recent years, with the diversification of lifestyles and the rise in health awareness, there has been a growing need to effectively manage and improve individual health conditions. However, conventional health management systems have struggled to utilize detailed individual health data to predict future health conditions and provide concrete, actionable guidelines. Therefore, the challenge is to provide a system that can utilize users' health data to predict future health conditions and offer individually customized advice.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes means for receiving and recording biometric data from a user, a generation function for processing the biometric data to estimate future health status, and means for generating a digital representation that visualizes the estimated future health status. This enables the user to predict their own health risks and obtain concrete action guidelines for improvement.

[0246] A "user" refers to anyone who uses the system to manage their own or their family's health.

[0247] "Biometric data" refers to information about the health of the user and their family, and includes multiple data points such as health checkup results, lifestyle data, and medical history.

[0248] "Generative function" refers to technical means of performing data processing and analysis using received biometric data to estimate future health conditions.

[0249] "Digital representation" refers to digitized avatars and other forms of representation that visually visualize predicted future health conditions and present them in a format that is easy for users to understand.

[0250] "Advice" refers to actionable guidelines provided to the user based on their predicted health status, including suggestions for improving lifestyle habits.

[0251] "User device" refers to a mobile terminal, computer screen, or other display device used by a user to receive and refer to information from the system.

[0252] This invention is implemented as a system that provides advanced support for user health management. In this system, the server, terminal, and user work together to predict future risks based on health data and provide intuitive feedback to the user.

[0253] The server first receives and records biometric data of the user and their family. This biometric data includes health checkup results, lifestyle data, and medical history. This data is stored in a database system, using software such as MySQL or MongoDB.

[0254] Next, the server preprocesses the data and uses a generative AI model to estimate future health status. This generative AI model utilizes machine learning frameworks such as TensorFlow and PyTorch, where a neural network calculates future health risks. It predicts health risks from the processed data, assessing, for example, the potential for increased risk of diabetes.

[0255] Furthermore, based on the estimation results, the server generates a digital representation, namely an avatar of the user's future health status. Therefore, tools such as Unity are used to render the avatar using 3D graphics and provide it to the user.

[0256] The server generates digital representations and specific advice, and then transfers this information to the user's terminal. The terminal presents the information received from the server to the user through a graphical user interface (GUI). The terminal's software utilizes frameworks such as React Native and Flutter, allowing for an intuitive display of visual information.

[0257] Users can review the information and advice presented and adjust their lifestyle based on that advice. For example, if the predicted avatar shows a high risk of diabetes, the system will suggest advice such as "increase your exercise and choose a diet that limits sugar."

[0258] An example of a prompt message would be, "Predict future health risks based on this user's health data and generate advice." This system provides users with an effective means to maintain a healthy lifestyle.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The server receives biometric data from users and their families and stores it in a database. Inputs include health checkup results, lifestyle data, and medical history. The server retrieves this data and stores it in the database in an organized format. For example, users can input data through a smartphone app and send it to the server, thus accumulating information.

[0262] Step 2:

[0263] The server preprocesses the ingested health data. Specifically, it imputes missing values ​​in the input data and detects and corrects outliers. Furthermore, it performs encoding to convert categorical data into numerical data. During this process, data cleansing is performed using data processing libraries such as Pandas. The output is a clean dataset that can be used by AI models.

[0264] Step 3:

[0265] The server uses pre-processed data as input and predicts future health risks using a generative AI model. Specifically, it uses TensorFlow or PyTorch to build a neural network model and assess the risks. The output obtained here is the probability value of each user's predicted health risk.

[0266] Step 4:

[0267] The server generates a digital representation that visualizes future health status based on predicted health risks. Here, Unity and other visual tools are used to create avatars, and the health status is reflected in these avatars. The input is probability values ​​of risk, and the output is in the form of a digital avatar.

[0268] Step 5:

[0269] The server generates advice based on the generated avatar and individual health status. This advice is materialized using natural language generation technology. Using a GPT-based model, it generates advice as text based on prompts. The output is a personalized guideline for improving lifestyle habits.

[0270] Step 6:

[0271] The terminal receives avatars and advice sent from the server and presents them to the user via a GUI. Utilizing React Native and Flutter, it provides a screen layout that allows for visual and intuitive data verification. Input is information from the server, and output is a visually displayed avatar and textual advice.

[0272] Step 7:

[0273] Users re-evaluate their lifestyle habits based on the information displayed on their device and make improvements as needed. Furthermore, users can provide feedback to the system regarding the results of their lifestyle changes. This feedback allows the server to further train the AI ​​model and improve its prediction accuracy.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In modern society, many people are conscious of health management in their daily lives, but often lack concrete action plans. Furthermore, especially in physical fitness facilities such as gyms, users are expected to visually understand their own health status and use that understanding to improve their training and lifestyle habits. However, existing systems have faced the challenge of providing detailed predictions and guidance based on individual users' health data.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes means for receiving and storing health data from a user, means for generating data to predict future health status using the health data, and means for generating an avatar that visualizes the predicted future health status. This makes it possible for users in physical stores such as fitness gyms to visually check their own health status and receive specific training guidelines and advice on improving their lifestyle based on that information.

[0279] "Means for receiving and storing health data from users" refers to a function that receives health-related information provided by users, stores it, and makes it available for later processing.

[0280] The "generation means" is a function that predicts the future health status of a user using AI technology based on the received health data.

[0281] The "means for generating an avatar" is a function that creates a virtual character for visually representing the predicted health status.

[0282] The "means for creating advice" is a function that provides specific advice for reviewing the user's current lifestyle based on the generated avatar and health information.

[0283] The "means for transmitting to the user terminal" is a function that distributes the generated avatar and advice to a device that allows the user to easily view them.

[0284] The "means for displaying in a physical store" is a function that enables the user to visually confirm health-related information in a fitness gym or other physical store.

[0285] First, the server receives health data from the user and their family. This data includes health diagnosis information, lifestyle data, exercise history, etc. The server stores these data using Amazon Web Services (AWS)'s S3. Next, the server executes the generation means for predicting the future health status using AI technology based on these health data. Machine learning libraries such as TensorFlow are used in the generation means. The server visualizes the predicted health status and utilizes Unity to generate a 3D avatar. The generated avatar is implemented using React Native for display on the graphical user interface (GUI) of the user terminal.

[0286] The terminal presents the data received from the server to the user in an easy-to-understand form. For this purpose, the terminal utilizes the GUI to display the predicted health status and advice. For example, within a fitness gym, the terminal displays training advice along with the user's health avatar. As a result, the user can receive specific training plans and suggestions for improving lifestyle habits based on their own health data. Kiosk terminals installed in physical stores also support this, making it easily accessible to users within the store.

[0287] The user regularly inputs their own or their family's health data and checks the results. For example, when the user uses a kiosk terminal at a fitness gym to display a health prediction avatar and a risk of weight gain is indicated, they can receive specific advice such as "Perform aerobic exercise more than three times a week" and "Be mindful of a high-protein, low-calorie diet".

[0288] Examples of prompt sentences for the generative AI model are as follows. "Based on the user's health data, predict the health status for the next three months and generate a 3D avatar. Based on the generated avatar, create training and diet advice and present it to the user." Through this process, the user can manage their health more intuitively and efficiently.

[0289] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0290] Step 1:

[0291] The user inputs health data using a smartphone or a kiosk terminal within a physical store. The data to be input includes health examination information, lifestyle data, exercise history, etc. These data are sent to the server and stored in AWS's S3.

[0292] Step 2:

[0293] The server inputs the received health data into an AI model to predict future health status. The generative AI model used in this step is built using TensorFlow and analyzes the data to assess future risks. The output is the user's predicted future health status.

[0294] Step 3:

[0295] The server generates a 3D avatar based on the prediction results. The generated avatar is visualized using Unity. This avatar visually represents the user's future health status, and can, for example, visualize predicted weight gain or loss.

[0296] Step 4:

[0297] The server generates health improvement advice for the user based on the generated avatar and prediction results. The advice is generated by an AI model and specifically shows how to improve the user's lifestyle. For example, it may advise "do 30 minutes of aerobic exercise three times a week" or "try to eat a diet low in sugar."

[0298] Step 5:

[0299] The server sends the generated 3D avatar and advice to the user's device. The device displays this information using a graphical user interface (GUI). It is designed using React Native to allow for intuitive viewing of the avatar and advice.

[0300] Step 6:

[0301] Based on the presented avatar and advice, users review their daily lives and training plans and provide feedback to the system. This feedback is sent to the server and used to improve future predictive models. This allows the system to provide users with more accurate health management advice.

[0302] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0303] The present invention provides more personalized health support by combining an emotion engine with a system for improving user health management. This system operates with the cooperation of each entity: the server, the terminal, and the user.

[0304] The server first receives and stores health data collected from the user and their family. This data includes health examination information, lifestyle data, and treatment history. The server uses this data to predict the future health status by means of an AI model. Furthermore, an avatar is generated based on the prediction result, and the avatar helps to visualize the user's future health status.

[0305] The emotion engine recognizes the user's emotional state and provides that information to the server. The server generates advice according to the emotion while taking into account the data from the emotion engine. For example, when the user is feeling stressed, specific advice for relaxation can be emphasized.

[0306] The generated emotion-responsive avatar and advice are sent to the user terminal. The terminal displays the information received from the server in a form that is easy for the user to understand. For example, even when the user is in an unstable emotional state, the terminal presents the information using a design or message that gives a sense of security.

[0307] The user receives health advice tailored to their health status and emotion via the terminal. As a result, the user can more appropriately improve their lifestyle and maintain and improve their health status. The user's feedback is sent to the server, contributing to the improvement of the system.

[0308] This system enables personalized healthcare that takes emotions into account, allowing for more effective management of users' health risks.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] Users enter personal and family health data into the device. This includes health checkup information, lifestyle data, and treatment history, preparing it for transmission to the system.

[0312] Step 2:

[0313] The terminal formats the entered health data, verifies its integrity, encrypts it, and then sends it to the server.

[0314] Step 3:

[0315] The server receives data sent from the terminal, organizes it in a database, and stores it. The stored data is then input into an AI model for predicting health status.

[0316] Step 4:

[0317] An AI model runs on the server to predict the user's future health status. Based on the prediction results, an avatar is generated that visualizes the user's health.

[0318] Step 5:

[0319] The device acquires data on the user's facial expressions and voice, and analyzes the user's emotions using an emotion engine. This emotional information is then sent to the server.

[0320] Step 6:

[0321] The server receives data from the emotion engine and generates health advice tailored to the user's emotional state. It creates advice that includes improvement suggestions that match the user's emotions.

[0322] Step 7:

[0323] The server sends an avatar and advice that corresponds to the user's emotions to the user's terminal. The avatar is designed with emotional sensitivity in mind.

[0324] Step 8:

[0325] The device displays received information and provides health advice in a user-friendly format. The user interface is designed with emotions in mind.

[0326] Step 9:

[0327] Users review and improve their lifestyle habits based on the advice provided by the device. They also provide feedback on the results of the advice and their impressions via the device, helping to improve the system.

[0328] (Example 2)

[0329] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0330] In modern society, personal health management is becoming increasingly important, but conventional health support systems have struggled to provide personalized advice that takes into account individual emotional states. Furthermore, there was a lack of technology to integrate health and emotional information and present it clearly to users. As a result, users found it difficult to take health-related actions that aligned with their emotions, potentially leading to a deterioration in their health.

[0331] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0332] In this invention, the server includes means for receiving and storing biometric data, means for estimating future health conditions, means for generating virtual representations, means for recognizing emotional states, means for creating personalized lifestyle guidance, and means for communicating this guidance to a user interface. This enables the user to comprehensively understand their own health and emotional states and select appropriate health actions based on that understanding.

[0333] "Biometric data" refers to information that indicates a user's health status, and includes data such as test results, lifestyle patterns, and treatment history.

[0334] "Future health status" refers to the predicted state of the user's health, estimated based on current health information.

[0335] "Estimation means" refers to a method or device for predicting future health status from received biometric data using technologies such as AI models.

[0336] A "virtual representation" is a digital avatar or graphic generated to visually represent the predicted results of a user's health status.

[0337] "Emotional state" refers to information that represents the user's psychological state, and is data obtained by analyzing facial expressions, voice, and other factors.

[0338] "Emotional analysis means" refers to a technology or device that analyzes a user's facial expressions, voice, etc., in real time to identify their emotional state.

[0339] "Individualized lifestyle guidance" refers to advice that includes specific actions and precautions tailored to the user's health and emotional state.

[0340] A "user interface" is a device or method for a user to receive information from a system, and includes terminals and screen displays.

[0341] This invention is a system for improving user health management, utilizing biometric data and emotional states to provide personalized health advice. The system functions through the cooperation of the server, terminal, and user.

[0342] The server first receives diverse biometric data from the user and their family and stores it in a database. This biometric data includes examination information, lifestyle pattern data, and treatment history. Based on the received data, the server uses machine learning libraries such as TensorFlow and PyTorch to build an AI model and predict the user's future health status. Based on this prediction, the server uses software such as Unity to generate a virtual representation that visualizes the future health status.

[0343] Regarding emotion analysis, when a user accesses the system through a terminal, the server analyzes facial expressions and voice data using emotion analysis tools. This technology utilizes OpenCV and relevant analysis software. The server considers these analysis results and uses a generative AI model to generate personalized health advice tailored to the user's emotions. For example, it generates advice based on a prompt such as, "Please suggest specific relaxation methods for when the user is feeling stressed."

[0344] The generated virtual representation and personalized advice are sent to the terminal. The terminal displays these visually and in text through an interface that is easy for the user to understand. Based on this, the user can select and implement actions that are appropriate to their health condition and emotions. This feedback is sent back to the server, and the accumulated data is used to continuously improve the accuracy and usefulness of the entire system.

[0345] As a concrete example, when the server inputs the prompt "Please tell me specific vegetables that are recommended for improving my diet" into the AI ​​model, it outputs customized dietary guidance. In this way, users can receive specific advice that they can implement in their daily lives.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The server receives biometric data sent from users and their families and stores it in a database. This data includes examination information, lifestyle pattern data, and treatment history. By receiving and storing biometric data as input, it builds a foundation for estimating future health status.

[0349] Step 2:

[0350] The server uses stored biometric data to build an AI model and predict future health status. Here, the AI ​​model is formed using machine learning libraries such as TensorFlow. Based on the input biometric data, it numerically and statistically analyzes the user's health risks and future health status, and outputs the results.

[0351] Step 3:

[0352] The server uses the predicted health status results to generate a virtual avatar. This uses Unity or similar software to visually represent future health status. Based on the predicted results as input, it generates graphic data and outputs it in a format that the user can easily understand.

[0353] Step 4:

[0354] The server recognizes the user's emotional state in real time using emotion analysis tools. It takes facial expression data and voice data as input, performs analysis using technologies such as OpenCV, and returns the results to the server. This provides the emotional analysis results.

[0355] Step 5:

[0356] The server considers the analyzed emotional information and uses a generative AI model to create personalized health advice. Taking the prompt "What refreshing methods would you recommend when a user is feeling tired?" as input, the generative AI model outputs specific lifestyle guidance plans.

[0357] Step 6:

[0358] The terminal, upon receiving data from the server, displays virtual representations and advice in a user-friendly interface. The design and messaging are carefully crafted to ensure user emotional reassurance. By outputting the received data onto the screen, users can intuitively understand the information.

[0359] Step 7:

[0360] Users provide feedback based on the avatars and advice they receive. This feedback is sent to the server and used to improve the system. Users input their experiences, impressions, and improvement requests, which are then used to improve the overall accuracy and performance of the system as output.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0363] In modern society, comprehensively managing people's health and providing health support tailored to individual emotional states is a crucial challenge. However, conventional health management systems struggle to provide health advice that adequately considers emotional states, resulting in insufficient improvement in users' lifestyles. Therefore, there is a need to realize health support that comprehensively analyzes users' health and emotions and encourages specific actions appropriate to those emotions.

[0364] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0365] In this invention, the server includes means for receiving and storing health data and emotional data from a user, generation means for predicting future health status using said health data and emotional data, and means for generating a virtual display that visualizes the predicted future health status and emotional status. This makes it possible to provide personalized health advice according to the user's emotional status and efficiently promote improvements in lifestyle habits.

[0366] "Health data" refers to information related to a user's physical condition and health, including health checkup information, lifestyle information, and treatment records.

[0367] "Emotional data" refers to information about the user's psychological state, including data such as the type and intensity of emotions obtained by the emotion engine.

[0368] The "generation means" refers to a processing means that uses an AI model to predict the future health state based on health data and emotional data received from the user, and generates a virtual display based on that prediction.

[0369] "Virtual representation" refers to avatars or graphics that visually represent predicted future health and emotional states, displayed in a way that is easy for the user to understand.

[0370] "Advice" includes suggestions and instructions provided to users to encourage improvements in their health and lifestyle, and is personalized according to the user's emotional state.

[0371] "Terminal" refers to equipment used by a user to utilize the system of the present invention, and includes devices such as smart glasses and smartphones.

[0372] In order to implement this invention, the user, server, and terminal must cooperate to build a system. The server receives and stores health data and emotional data from the user. Health data includes health checkup information, lifestyle information, treatment records, etc., and emotional data includes information about the user's emotional state obtained from the emotional engine.

[0373] The server uses this data to predict future health status using a generative AI model. This model employs deep learning libraries such as TensorFlow and PyTorch. Based on the prediction results, the server generates a virtual representation that visualizes future health and emotional states. The generated virtual representation is presented in a user-friendly format, such as an avatar or graphic.

[0374] Furthermore, the server generates personalized health advice based on virtual displays and health information, tailored to the user's emotional state. This advice is calculated by an AI model, taking into account past data and the user's current emotional state, and includes specific action guidelines.

[0375] The terminal is responsible for displaying virtual representations and advice sent from the server to the user. The terminal includes smart glasses and smartphones, allowing the user to receive advice visually. The terminal uses visual devices and processors to display information in real time.

[0376] As a concrete example of implementation, if the user detects stress, the device can offer suggestions for deep breathing and relaxation techniques in the form of a virtual counselor. Furthermore, the AI ​​model can generate appropriate advice using prompt examples such as: "If the user's emotional state indicates stress, what relaxation method would be best?"

[0377] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0378] Step 1:

[0379] The server receives health and emotional data submitted by users and stores them in a database. Inputs are data indicating the user's health checkup information and emotional state, while outputs are the health and emotional data registered in the database. This process involves format conversion to maintain data integrity.

[0380] Step 2:

[0381] The server inputs stored health and emotion data into an AI model to predict future health status. The input is formatted health and emotion data, and the output is numerical or textual information representing the predicted health status. Here, a generative AI model (e.g., TensorFlow) is used to perform the prediction calculation.

[0382] Step 3:

[0383] The server generates a visually easy-to-understand virtual representation based on predicted future health and emotional states. The input is predicted health and emotional data, and the output is a virtual avatar or graphic. Visualized graphic content is generated using design software.

[0384] Step 4:

[0385] The server generates personalized health advice based on the user's emotional state, along with a virtual display. Inputs include the virtual display, health information, and user emotion data, while output is advice containing specific action guidelines. The AI ​​model is queried using prompts (e.g., "If the user's emotional state indicates stress, what relaxation methods are best?") to generate appropriate advice.

[0386] Step 5:

[0387] The device displays virtual information and health advice received from the server to the user. The input is the virtual information and advice sent from the server, and the output is the information the user receives visually. The device includes smart glasses and smartphones, enabling real-time information presentation through applications.

[0388] Step 6:

[0389] Users raise their awareness of lifestyle habits and health through virtual displays and advice shown on their devices. Input is information from the device, and output is the user's review of their behavior and implementation of improvement measures. Users are expected to take concrete actions in their daily lives based on the information they receive.

[0390] 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.

[0391] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0392] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0393] [Third Embodiment]

[0394] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0395] 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.

[0396] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0397] 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.

[0398] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0399] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0400] 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.

[0401] 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.

[0402] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0403] The 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.

[0404] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0405] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0406] This invention is implemented as a system to provide advanced support for user health management. This system is based on the cooperation of the server, terminal, and user.

[0407] The server first receives health data from the user and their family. This data includes health checkup information, lifestyle data, and treatment history. The server stores this data and, based on it, executes a generation mechanism to predict future health status. The generation mechanism uses AI technology to evaluate the user's future health risks and obtains prediction results. Furthermore, based on the obtained predictions, it generates an avatar that visually represents the future health status.

[0408] The server then generates an avatar and advice based on the predictions, and sends this to the user's terminal. The terminal receives the data from the server and displays it in a way that is easy for the user to understand. For this purpose, the terminal uses a graphical user interface (GUI) to clearly present the predicted health status and advice.

[0409] Users review results generated based on health data entered by themselves or their family members. For example, if the predictive avatar indicates a high risk of diabetes, the system provides advice such as "increase exercise and change to a diet that restricts sugar." Users can use this advice to review and improve their lifestyle. When feedback is provided to the system, the server can use that information to improve the predictive model.

[0410] Thus, the system of the present invention supports effective health management by predicting future risks based on health data and presenting them to the user through both visual and behavioral guidance. Through this process, users can adjust their daily behaviors toward a healthier direction and strive for prevention.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] Users enter their personal and family health data into the device. They input health checkup information, lifestyle questionnaire results, past treatment history, etc., and prepare to send it to the system.

[0414] Step 2:

[0415] The terminal formats the user's input data, verifies data integrity as needed, encrypts it, and then sends it to the server.

[0416] Step 3:

[0417] The server receives health data sent from the terminal and stores it in a database. During this process, it performs data classification and consistency checks.

[0418] Step 4:

[0419] The server inputs stored health data into an AI model and performs a process to predict future health conditions. This includes comparing the data with existing big data.

[0420] Step 5:

[0421] The server generates an avatar based on the prediction results. This avatar visualizes the user's future health status.

[0422] Step 6:

[0423] The server generates specific advice to improve the predicted health condition and sends it to the user's terminal along with the avatar.

[0424] Step 7:

[0425] The terminal displays avatars and advice received from the server, enabling users to understand the information visually and intuitively.

[0426] Step 8:

[0427] Users can review and improve their lifestyle habits based on the information presented. They can also evaluate the advice and provide feedback to the server via their device, along with any new information.

[0428] (Example 1)

[0429] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0430] In recent years, with the diversification of lifestyles and the rise in health awareness, there has been a growing need to effectively manage and improve individual health conditions. However, conventional health management systems have struggled to utilize detailed individual health data to predict future health conditions and provide concrete, actionable guidelines. Therefore, the challenge is to provide a system that can utilize users' health data to predict future health conditions and offer individually customized advice.

[0431] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0432] In this invention, the server includes means for receiving and recording biometric data from a user, a generation function for processing the biometric data to estimate future health status, and means for generating a digital representation that visualizes the estimated future health status. This enables the user to predict their own health risks and obtain concrete action guidelines for improvement.

[0433] A "user" refers to anyone who uses the system to manage their own or their family's health.

[0434] "Biometric data" refers to information about the health of the user and their family, and includes multiple data points such as health checkup results, lifestyle data, and medical history.

[0435] "Generative function" refers to technical means of performing data processing and analysis using received biometric data to estimate future health conditions.

[0436] "Digital representation" refers to digitized avatars and other forms of representation that visually visualize predicted future health conditions and present them in a format that is easy for users to understand.

[0437] "Advice" refers to actionable guidelines provided to the user based on their predicted health status, including suggestions for improving lifestyle habits.

[0438] "User device" refers to a mobile terminal, computer screen, or other display device used by a user to receive and refer to information from the system.

[0439] This invention is implemented as a system that provides advanced support for user health management. In this system, the server, terminal, and user work together to predict future risks based on health data and provide intuitive feedback to the user.

[0440] The server first receives and records biometric data of the user and their family. This biometric data includes health checkup results, lifestyle data, and medical history. This data is stored in a database system, using software such as MySQL or MongoDB.

[0441] Next, the server preprocesses the data and uses a generative AI model to estimate future health status. This generative AI model utilizes machine learning frameworks such as TensorFlow and PyTorch, where a neural network calculates future health risks. It predicts health risks from the processed data, assessing, for example, the potential for increased risk of diabetes.

[0442] Furthermore, based on the estimation results, the server generates a digital representation, namely an avatar of the user's future health status. Therefore, tools such as Unity are used to render the avatar using 3D graphics and provide it to the user.

[0443] The server generates digital representations and specific advice, and then transfers this information to the user's terminal. The terminal presents the information received from the server to the user through a graphical user interface (GUI). The terminal's software utilizes frameworks such as React Native and Flutter, allowing for an intuitive display of visual information.

[0444] Users can review the information and advice presented and adjust their lifestyle based on that advice. For example, if the predicted avatar shows a high risk of diabetes, the system will suggest advice such as "increase your exercise and choose a diet that limits sugar."

[0445] An example of a prompt message would be, "Predict future health risks based on this user's health data and generate advice." This system provides users with an effective means to maintain a healthy lifestyle.

[0446] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0447] Step 1:

[0448] The server receives biometric data from users and their families and stores it in a database. Inputs include health checkup results, lifestyle data, and medical history. The server retrieves this data and stores it in the database in an organized format. For example, users can input data through a smartphone app and send it to the server, thus accumulating information.

[0449] Step 2:

[0450] The server preprocesses the ingested health data. Specifically, it imputes missing values ​​in the input data and detects and corrects outliers. Furthermore, it performs encoding to convert categorical data into numerical data. During this process, data cleansing is performed using data processing libraries such as Pandas. The output is a clean dataset that can be used by AI models.

[0451] Step 3:

[0452] The server uses pre-processed data as input and predicts future health risks using a generative AI model. Specifically, it uses TensorFlow or PyTorch to build a neural network model and assess the risks. The output obtained here is the probability value of each user's predicted health risk.

[0453] Step 4:

[0454] The server generates a digital representation that visualizes future health status based on predicted health risks. Here, Unity and other visual tools are used to create avatars, and the health status is reflected in these avatars. The input is probability values ​​of risk, and the output is in the form of a digital avatar.

[0455] Step 5:

[0456] The server generates advice based on the generated avatar and individual health status. This advice is materialized using natural language generation technology. Using a GPT-based model, it generates advice as text based on prompts. The output is a personalized guideline for improving lifestyle habits.

[0457] Step 6:

[0458] The terminal receives avatars and advice sent from the server and presents them to the user via a GUI. Utilizing React Native and Flutter, it provides a screen layout that allows for visual and intuitive data verification. Input is information from the server, and output is a visually displayed avatar and textual advice.

[0459] Step 7:

[0460] Users re-evaluate their lifestyle habits based on the information displayed on their device and make improvements as needed. Furthermore, users can provide feedback to the system regarding the results of their lifestyle changes. This feedback allows the server to further train the AI ​​model and improve its prediction accuracy.

[0461] (Application Example 1)

[0462] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0463] In modern society, many people are conscious of health management in their daily lives, but often lack concrete action plans. Furthermore, especially in physical fitness facilities such as gyms, users are expected to visually understand their own health status and use that understanding to improve their training and lifestyle habits. However, existing systems have faced the challenge of providing detailed predictions and guidance based on individual users' health data.

[0464] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0465] In this invention, the server includes means for receiving and storing health data from a user, means for generating data to predict future health status using the health data, and means for generating an avatar that visualizes the predicted future health status. This makes it possible for users in physical stores such as fitness gyms to visually check their own health status and receive specific training guidelines and advice on improving their lifestyle based on that information.

[0466] "Means for receiving and storing health data from users" refers to a function that receives health-related information provided by users, stores it, and makes it available for later processing.

[0467] The "generation method" is a function that uses AI technology to predict the user's future health status based on the received health data.

[0468] "Means of generating avatars" refers to the function of creating a virtual character to visually represent a predicted health state.

[0469] The "means of generating advice" refer to a function that provides specific advice to help users review their current lifestyle habits based on the generated avatar and health information.

[0470] "Means of sending to the user's terminal" refers to a function that delivers the generated avatar and advice to a device that the user can easily view.

[0471] "Means of displaying information within physical stores" refers to a function that allows users to visually check health-related information within fitness gyms and other physical stores.

[0472] The server first receives health data from the user and their family. This data includes health checkup information, lifestyle data, and exercise history. The server stores this data using Amazon Web Services (AWS) S3. Next, based on this health data, the server executes a generation process using AI technology to predict future health status. Machine learning libraries such as TensorFlow are used for this generation process. The server uses Unity to visualize the predicted health status and generate a 3D avatar. The generated avatar is implemented using React Native to be displayed on the user's terminal's graphical user interface (GUI).

[0473] The terminal presents data received from the server to the user in an easy-to-understand format. To achieve this, the terminal utilizes a graphical user interface (GUI) to display predicted health status and advice. For example, in a fitness gym, the terminal displays training advice along with the user's health avatar. This allows users to receive specific training plans and lifestyle improvement suggestions based on their own health data. Kiosk terminals installed in physical stores also support this, making it easily accessible to users within the store.

[0474] Users regularly input their own or their family's health data and review the results. For example, when a user uses a kiosk terminal at a fitness gym to display a health prediction avatar, if a risk of weight gain is indicated, they can receive specific advice such as "exercise aerobics at least three times a week" and "focus on a high-protein, low-calorie diet."

[0475] An example of a prompt for the generating AI model is as follows: "Based on the user's health data, predict their health status for the next three months and generate a 3D avatar. Based on the generated avatar, create and present training and dietary advice to the user." This process allows users to manage their health more intuitively and efficiently.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] Users enter health data using their smartphones or kiosk terminals in physical stores. This data includes health checkup information, lifestyle data, and exercise history. This data is sent to a server and stored in AWS S3.

[0479] Step 2:

[0480] The server inputs the received health data into an AI model to predict future health status. The generative AI model used in this step is built using TensorFlow and analyzes the data to assess future risks. The output is the user's predicted future health status.

[0481] Step 3:

[0482] The server generates a 3D avatar based on the prediction results. The generated avatar is visualized using Unity. This avatar visually represents the user's future health status, and can, for example, visualize predicted weight gain or loss.

[0483] Step 4:

[0484] The server generates health improvement advice for the user based on the generated avatar and prediction results. The advice is generated by an AI model and specifically shows how to improve the user's lifestyle. For example, it may advise "do 30 minutes of aerobic exercise three times a week" or "try to eat a diet low in sugar."

[0485] Step 5:

[0486] The server sends the generated 3D avatar and advice to the user's device. The device displays this information using a graphical user interface (GUI). It is designed using React Native to allow for intuitive viewing of the avatar and advice.

[0487] Step 6:

[0488] Based on the presented avatar and advice, users review their daily lives and training plans and provide feedback to the system. This feedback is sent to the server and used to improve future predictive models. This allows the system to provide users with more accurate health management advice.

[0489] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0490] This invention provides more personalized health support by combining an emotion engine with a system designed to improve user health management. This system operates through the collaborative efforts of the server, terminal, and user.

[0491] The server first receives and stores health data collected from the user and their family. This data includes health checkup information, lifestyle data, and treatment history. The server uses this data to predict future health status using an AI model. Furthermore, it generates an avatar based on the prediction results, and this avatar helps visualize the user's future health status.

[0492] The emotion engine recognizes the user's emotional state and provides that information to the server. The server then uses the data from the emotion engine to generate emotionally appropriate advice. For example, if the user is feeling stressed, the server can emphasize specific relaxation techniques.

[0493] The generated emotion-responsive avatar and advice are sent to the user's device. The device displays the information received from the server in a way that is easy for the user to understand. For example, even if the user is in an unstable emotional state, the device will present the information using reassuring designs and messages.

[0494] Users receive health advice tailored to their health status and emotions via their device. This allows users to better improve their lifestyle and maintain or improve their health. User feedback is sent to the server and contributes to system improvement.

[0495] This system enables personalized healthcare that takes emotions into account, allowing for more effective management of users' health risks.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] Users enter personal and family health data into the device. This includes health checkup information, lifestyle data, and treatment history, which are then prepared for transmission to the system.

[0499] Step 2:

[0500] The terminal formats the entered health data, verifies its integrity, encrypts it, and then sends it to the server.

[0501] Step 3:

[0502] The server receives data sent from the terminal, organizes it in a database, and stores it. The stored data is then input into an AI model for predicting health status.

[0503] Step 4:

[0504] An AI model runs on the server to predict the user's future health status. Based on the prediction results, an avatar is generated that visualizes the user's health.

[0505] Step 5:

[0506] The device acquires data on the user's facial expressions and voice, and analyzes the user's emotions using an emotion engine. This emotional information is then sent to the server.

[0507] Step 6:

[0508] The server receives data from the emotion engine and generates health advice tailored to the user's emotional state. It creates advice that includes improvement suggestions that match the user's emotions.

[0509] Step 7:

[0510] The server sends an avatar and advice that corresponds to the user's emotions to the user's terminal. The avatar is designed with emotional sensitivity in mind.

[0511] Step 8:

[0512] The device displays received information and provides health advice in a user-friendly format. The user interface is designed with emotions in mind.

[0513] Step 9:

[0514] Users review and improve their lifestyle habits based on the advice provided by the device. They also provide feedback on the results of the advice and their impressions via the device, helping to improve the system.

[0515] (Example 2)

[0516] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0517] In modern society, personal health management is becoming increasingly important, but conventional health support systems have struggled to provide personalized advice that takes into account individual emotional states. Furthermore, there was a lack of technology to integrate health and emotional information and present it clearly to users. As a result, users found it difficult to take health-related actions that aligned with their emotions, potentially leading to a deterioration in their health.

[0518] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0519] In this invention, the server includes means for receiving and storing biometric data, means for estimating future health conditions, means for generating virtual representations, means for recognizing emotional states, means for creating personalized lifestyle guidance, and means for communicating this guidance to a user interface. This enables the user to comprehensively understand their own health and emotional states and select appropriate health actions based on that understanding.

[0520] "Biometric data" refers to information that indicates a user's health status, and includes data such as test results, lifestyle patterns, and treatment history.

[0521] "Future health status" refers to the predicted state of the user's health, estimated based on current health information.

[0522] "Estimation means" refers to a method or device for predicting future health status from received biometric data using technologies such as AI models.

[0523] A "virtual representation" is a digital avatar or graphic generated to visually represent the predicted results of a user's health status.

[0524] "Emotional state" refers to information that represents the user's psychological state, and is data obtained by analyzing facial expressions, voice, and other factors.

[0525] "Emotional analysis means" refers to a technology or device that analyzes a user's facial expressions, voice, etc., in real time to identify their emotional state.

[0526] "Individualized lifestyle guidance" refers to advice that includes specific actions and precautions tailored to the user's health and emotional state.

[0527] A "user interface" is a device or method for a user to receive information from a system, and includes terminals and screen displays.

[0528] This invention is a system for improving user health management, utilizing biometric data and emotional states to provide personalized health advice. The system functions through the cooperation of the server, terminal, and user.

[0529] The server first receives diverse biometric data from the user and their family and stores it in a database. This biometric data includes examination information, lifestyle pattern data, and treatment history. Based on the received data, the server uses machine learning libraries such as TensorFlow and PyTorch to build an AI model and predict the user's future health status. Based on this prediction, the server uses software such as Unity to generate a virtual representation that visualizes the future health status.

[0530] Regarding emotion analysis, when a user accesses the system through a terminal, the server analyzes facial expressions and voice data using emotion analysis tools. This technology utilizes OpenCV and relevant analysis software. The server considers these analysis results and uses a generative AI model to generate personalized health advice tailored to the user's emotions. For example, it generates advice based on a prompt such as, "Please suggest specific relaxation methods for when the user is feeling stressed."

[0531] The generated virtual representation and personalized advice are sent to the terminal. The terminal displays these visually and in text through an interface that is easy for the user to understand. Based on this, the user can select and implement actions that are appropriate to their health condition and emotions. This feedback is sent back to the server, and the accumulated data is used to continuously improve the accuracy and usefulness of the entire system.

[0532] As a concrete example, when the server inputs the prompt "Please tell me specific vegetables that are recommended for improving my diet" into the AI ​​model, it outputs customized dietary guidance. In this way, users can receive specific advice that they can implement in their daily lives.

[0533] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0534] Step 1:

[0535] The server receives biometric data sent from users and their families and stores it in a database. This data includes examination information, lifestyle pattern data, and treatment history. By receiving and storing biometric data as input, it builds a foundation for estimating future health status.

[0536] Step 2:

[0537] The server uses stored biometric data to build an AI model and predict future health status. Here, the AI ​​model is formed using machine learning libraries such as TensorFlow. Based on the input biometric data, it numerically and statistically analyzes the user's health risks and future health status, and outputs the results.

[0538] Step 3:

[0539] The server uses the predicted health status results to generate a virtual avatar. This uses Unity or similar software to visually represent future health status. Based on the predicted results as input, it generates graphic data and outputs it in a format that the user can easily understand.

[0540] Step 4:

[0541] The server recognizes the user's emotional state in real time using emotion analysis tools. It takes facial expression data and voice data as input, performs analysis using technologies such as OpenCV, and returns the results to the server. This provides the emotional analysis results.

[0542] Step 5:

[0543] The server considers the analyzed emotional information and uses a generative AI model to create personalized health advice. Taking the prompt "What refreshing methods would you recommend when a user is feeling tired?" as input, the generative AI model outputs specific lifestyle guidance plans.

[0544] Step 6:

[0545] The terminal, upon receiving data from the server, displays virtual representations and advice in a user-friendly interface. The design and messaging are carefully crafted to ensure user emotional reassurance. By outputting the received data onto the screen, users can intuitively understand the information.

[0546] Step 7:

[0547] Users provide feedback based on the avatars and advice they receive. This feedback is sent to the server and used to improve the system. Users input their experiences, impressions, and improvement requests, which are then used to improve the overall accuracy and performance of the system.

[0548] (Application Example 2)

[0549] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0550] In modern society, comprehensively managing people's health and providing health support tailored to individual emotional states is a crucial challenge. However, conventional health management systems struggle to provide health advice that adequately considers emotional states, resulting in insufficient improvement in users' lifestyles. Therefore, there is a need to realize health support that comprehensively analyzes users' health and emotions and encourages specific actions appropriate to those emotions.

[0551] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0552] In this invention, the server includes means for receiving and storing health data and emotional data from a user, generation means for predicting future health status using said health data and emotional data, and means for generating a virtual display that visualizes the predicted future health status and emotional status. This makes it possible to provide personalized health advice according to the user's emotional status and efficiently promote improvements in lifestyle habits.

[0553] "Health data" refers to information related to a user's physical condition and health, including health checkup information, lifestyle information, and treatment records.

[0554] "Emotional data" refers to information about the user's psychological state, including data such as the type and intensity of emotions obtained by the emotion engine.

[0555] The "generation means" refers to a processing means that uses an AI model to predict the future health state based on health data and emotional data received from the user, and generates a virtual display based on that prediction.

[0556] "Virtual representation" refers to avatars or graphics that visually represent predicted future health and emotional states, displayed in a way that is easy for the user to understand.

[0557] "Advice" includes suggestions and instructions provided to users to encourage improvements in their health and lifestyle, and is personalized according to the user's emotional state.

[0558] "Terminal" refers to equipment used by a user to utilize the system of the present invention, and includes devices such as smart glasses and smartphones.

[0559] In order to implement this invention, the user, server, and terminal must cooperate to build a system. The server receives and stores health data and emotional data from the user. Health data includes health checkup information, lifestyle information, treatment records, etc., and emotional data includes information about the user's emotional state obtained from the emotional engine.

[0560] The server uses this data to predict future health status using a generative AI model. This model employs deep learning libraries such as TensorFlow and PyTorch. Based on the prediction results, the server generates a virtual representation that visualizes future health and emotional states. The generated virtual representation is presented in a user-friendly format, such as an avatar or graphic.

[0561] Furthermore, the server generates personalized health advice based on virtual displays and health information, tailored to the user's emotional state. This advice is calculated by an AI model, taking into account past data and the user's current emotional state, and includes specific action guidelines.

[0562] The terminal is responsible for displaying virtual representations and advice sent from the server to the user. The terminal includes smart glasses and smartphones, allowing the user to receive advice visually. The terminal uses visual devices and processors to display information in real time.

[0563] As a concrete example of implementation, if the user detects stress, the device can offer suggestions for deep breathing and relaxation techniques in the form of a virtual counselor. Furthermore, the AI ​​model can generate appropriate advice using prompt examples such as: "If the user's emotional state indicates stress, what relaxation method would be best?"

[0564] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0565] Step 1:

[0566] The server receives health and emotional data submitted by users and stores them in a database. Inputs are data indicating the user's health checkup information and emotional state, while outputs are the health and emotional data registered in the database. This process involves format conversion to maintain data integrity.

[0567] Step 2:

[0568] The server inputs stored health and emotion data into an AI model to predict future health status. The input is formatted health and emotion data, and the output is numerical or textual information representing the predicted health status. Here, a generative AI model (e.g., TensorFlow) is used to perform the prediction calculation.

[0569] Step 3:

[0570] The server generates a visually easy-to-understand virtual representation based on predicted future health and emotional states. The input is predicted health and emotional data, and the output is a virtual avatar or graphic. Visualized graphic content is generated using design software.

[0571] Step 4:

[0572] The server generates personalized health advice based on the user's emotional state, along with a virtual display. Inputs include the virtual display, health information, and user emotion data, while output is advice containing specific action guidelines. The AI ​​model is queried using prompts (e.g., "If the user's emotional state indicates stress, what relaxation methods are best?") to generate appropriate advice.

[0573] Step 5:

[0574] The device displays virtual information and health advice received from the server to the user. The input is the virtual information and advice sent from the server, and the output is the information the user receives visually. The device includes smart glasses and smartphones, enabling real-time information presentation through applications.

[0575] Step 6:

[0576] Users raise their awareness of lifestyle habits and health through virtual displays and advice shown on their devices. Input is information from the device, and output is the user's review of their behavior and implementation of improvement measures. Users are expected to take concrete actions in their daily lives based on the information they receive.

[0577] 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.

[0578] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0579] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0580] [Fourth Embodiment]

[0581] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0582] 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.

[0583] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0584] 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.

[0585] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0586] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0587] 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.

[0588] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0589] 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.

[0590] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0591] The 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.

[0592] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0593] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0594] This invention is implemented as a system to provide advanced support for user health management. This system is based on the cooperation of the server, terminal, and user.

[0595] The server first receives health data from the user and their family. This data includes health checkup information, lifestyle data, and treatment history. The server stores this data and, based on it, executes a generation mechanism to predict future health status. The generation mechanism uses AI technology to evaluate the user's future health risks and obtains prediction results. Furthermore, based on the obtained predictions, it generates an avatar that visually represents the future health status.

[0596] The server then generates an avatar and advice based on the predictions, and sends this to the user's terminal. The terminal receives the data from the server and displays it in a way that is easy for the user to understand. For this purpose, the terminal uses a graphical user interface (GUI) to clearly present the predicted health status and advice.

[0597] Users review results generated based on health data entered by themselves or their family members. For example, if the predictive avatar indicates a high risk of diabetes, the system provides advice such as "increase exercise and change to a diet that restricts sugar." Users can use this advice to review and improve their lifestyle. When feedback is provided to the system, the server can use that information to improve the predictive model.

[0598] Thus, the system of the present invention supports effective health management by predicting future risks based on health data and presenting them to the user through both visual and behavioral guidance. Through this process, users can adjust their daily behaviors toward a healthier direction and strive for prevention.

[0599] The following describes the processing flow.

[0600] Step 1:

[0601] Users enter their personal and family health data into the device. They input health checkup information, lifestyle questionnaire results, past treatment history, etc., and prepare to send it to the system.

[0602] Step 2:

[0603] The terminal formats the user's input data, verifies data integrity as needed, encrypts it, and then sends it to the server.

[0604] Step 3:

[0605] The server receives health data sent from the terminal and stores it in a database. During this process, it performs data classification and consistency checks.

[0606] Step 4:

[0607] The server inputs stored health data into an AI model and performs a process to predict future health conditions. This includes comparing the data with existing big data.

[0608] Step 5:

[0609] The server generates an avatar based on the prediction results. This avatar visualizes the user's future health status.

[0610] Step 6:

[0611] The server generates specific advice to improve the predicted health condition and sends it to the user's terminal along with the avatar.

[0612] Step 7:

[0613] The terminal displays avatars and advice received from the server, enabling users to understand the information visually and intuitively.

[0614] Step 8:

[0615] Users can review and improve their lifestyle habits based on the information presented. They can also evaluate the advice and provide feedback to the server via their device, along with any new information.

[0616] (Example 1)

[0617] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0618] In recent years, with the diversification of lifestyles and the rise in health awareness, there has been a growing need to effectively manage and improve individual health conditions. However, conventional health management systems have struggled to utilize detailed individual health data to predict future health conditions and provide concrete, actionable guidelines. Therefore, the challenge is to provide a system that can utilize users' health data to predict future health conditions and offer individually customized advice.

[0619] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0620] In this invention, the server includes means for receiving and recording biometric data from a user, a generation function for processing the biometric data to estimate future health status, and means for generating a digital representation that visualizes the estimated future health status. This enables the user to predict their own health risks and obtain concrete action guidelines for improvement.

[0621] A "user" refers to anyone who uses the system to manage their own or their family's health.

[0622] "Biometric data" refers to information about the health of the user and their family, and includes multiple data points such as health checkup results, lifestyle data, and medical history.

[0623] "Generative function" refers to technical means of performing data processing and analysis using received biometric data to estimate future health conditions.

[0624] "Digital representation" refers to digitized avatars and other forms of representation that visually visualize predicted future health conditions and present them in a format that is easy for users to understand.

[0625] "Advice" refers to actionable guidelines provided to the user based on their predicted health status, including suggestions for improving lifestyle habits.

[0626] "User device" refers to a mobile terminal, computer screen, or other display device used by a user to receive and refer to information from the system.

[0627] This invention is implemented as a system that provides advanced support for user health management. In this system, the server, terminal, and user work together to predict future risks based on health data and provide intuitive feedback to the user.

[0628] The server first receives and records biometric data of the user and their family. This biometric data includes health checkup results, lifestyle data, and medical history. This data is stored in a database system, using software such as MySQL or MongoDB.

[0629] Next, the server preprocesses the data and uses a generative AI model to estimate future health status. This generative AI model utilizes machine learning frameworks such as TensorFlow and PyTorch, where a neural network calculates future health risks. It predicts health risks from the processed data, assessing, for example, the potential for increased risk of diabetes.

[0630] Furthermore, based on the estimation results, the server generates a digital representation, namely an avatar of the user's future health status. Therefore, tools such as Unity are used to render the avatar using 3D graphics and provide it to the user.

[0631] The server generates digital representations and specific advice, and then transfers this information to the user's terminal. The terminal presents the information received from the server to the user through a graphical user interface (GUI). The terminal's software utilizes frameworks such as React Native and Flutter, allowing for an intuitive display of visual information.

[0632] Users can review the information and advice presented and adjust their lifestyle based on that advice. For example, if the predicted avatar shows a high risk of diabetes, the system will suggest advice such as "increase your exercise and choose a diet that limits sugar."

[0633] An example of a prompt message would be, "Predict future health risks based on this user's health data and generate advice." This system provides users with an effective means to maintain a healthy lifestyle.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The server receives biometric data from users and their families and stores it in a database. Inputs include health checkup results, lifestyle data, and medical history. The server retrieves this data and stores it in the database in an organized format. For example, users can input data through a smartphone app and send it to the server, thus accumulating information.

[0637] Step 2:

[0638] The server preprocesses the ingested health data. Specifically, it imputes missing values ​​in the input data and detects and corrects outliers. Furthermore, it performs encoding to convert categorical data into numerical data. During this process, data cleansing is performed using data processing libraries such as Pandas. The output is a clean dataset that can be used by AI models.

[0639] Step 3:

[0640] The server uses pre-processed data as input and predicts future health risks using a generative AI model. Specifically, it uses TensorFlow or PyTorch to build a neural network model and assess the risks. The output obtained here is the probability value of each user's predicted health risk.

[0641] Step 4:

[0642] The server generates a digital representation that visualizes future health status based on predicted health risks. Here, Unity and other visual tools are used to create avatars, and the health status is reflected in these avatars. The input is probability values ​​of risk, and the output is in the form of a digital avatar.

[0643] Step 5:

[0644] The server generates advice based on the generated avatar and individual health status. This advice is materialized using natural language generation technology. Using a GPT-based model, it generates advice as text based on prompts. The output is a personalized guideline for improving lifestyle habits.

[0645] Step 6:

[0646] The terminal receives avatars and advice sent from the server and presents them to the user via a GUI. Utilizing React Native and Flutter, it provides a screen layout that allows for visual and intuitive data verification. Input is information from the server, and output is a visually displayed avatar and textual advice.

[0647] Step 7:

[0648] Users re-evaluate their lifestyle habits based on the information displayed on their device and make improvements as needed. Furthermore, users can provide feedback to the system regarding the results of their lifestyle changes. This feedback allows the server to further train the AI ​​model and improve its prediction accuracy.

[0649] (Application Example 1)

[0650] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0651] In modern society, many people are conscious of health management in their daily lives, but often lack concrete action plans. Furthermore, especially in physical fitness facilities such as gyms, users are expected to visually understand their own health status and use that understanding to improve their training and lifestyle habits. However, existing systems have faced the challenge of providing detailed predictions and guidance based on individual users' health data.

[0652] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0653] In this invention, the server includes means for receiving and storing health data from a user, means for generating data to predict future health status using the health data, and means for generating an avatar that visualizes the predicted future health status. This makes it possible for users in physical stores such as fitness gyms to visually check their own health status and receive specific training guidelines and advice on improving their lifestyle based on that information.

[0654] "Means for receiving and storing health data from users" refers to a function that receives health-related information provided by users, stores it, and makes it available for later processing.

[0655] The "generation method" is a function that uses AI technology to predict the user's future health status based on the received health data.

[0656] "Means of generating avatars" refers to the function of creating a virtual character to visually represent a predicted health state.

[0657] The "means of generating advice" refer to a function that provides specific advice to help users review their current lifestyle habits based on the generated avatar and health information.

[0658] "Means of sending to the user's terminal" refers to a function that delivers the generated avatar and advice to a device that the user can easily view.

[0659] "Means of displaying information within physical stores" refers to a function that allows users to visually check health-related information within fitness gyms and other physical stores.

[0660] The server first receives health data from the user and their family. This data includes health checkup information, lifestyle data, and exercise history. The server stores this data using Amazon Web Services (AWS) S3. Next, based on this health data, the server executes a generation process using AI technology to predict future health status. Machine learning libraries such as TensorFlow are used for this generation process. The server uses Unity to visualize the predicted health status and generate a 3D avatar. The generated avatar is implemented using React Native to be displayed on the user's terminal's graphical user interface (GUI).

[0661] The terminal presents data received from the server to the user in an easy-to-understand format. To achieve this, the terminal utilizes a graphical user interface (GUI) to display predicted health status and advice. For example, in a fitness gym, the terminal displays training advice along with the user's health avatar. This allows users to receive specific training plans and lifestyle improvement suggestions based on their own health data. Kiosk terminals installed in physical stores also support this, making it easily accessible to users within the store.

[0662] Users regularly input their own or their family's health data and review the results. For example, when a user uses a kiosk terminal at a fitness gym to display a health prediction avatar, if a risk of weight gain is indicated, they can receive specific advice such as "exercise aerobics at least three times a week" and "focus on a high-protein, low-calorie diet."

[0663] An example of a prompt for the generating AI model is as follows: "Based on the user's health data, predict their health status for the next three months and generate a 3D avatar. Based on the generated avatar, create and present training and dietary advice to the user." This process allows users to manage their health more intuitively and efficiently.

[0664] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0665] Step 1:

[0666] Users enter health data using their smartphones or kiosk terminals in physical stores. This data includes health checkup information, lifestyle data, and exercise history. This data is sent to a server and stored in AWS S3.

[0667] Step 2:

[0668] The server inputs the received health data into an AI model to predict future health status. The generative AI model used in this step is built using TensorFlow and analyzes the data to assess future risks. The output is the user's predicted future health status.

[0669] Step 3:

[0670] The server generates a 3D avatar based on the prediction results. The generated avatar is visualized using Unity. This avatar visually represents the user's future health status, and can, for example, visualize predicted weight gain or loss.

[0671] Step 4:

[0672] The server generates health improvement advice for the user based on the generated avatar and prediction results. The advice is generated by an AI model and specifically shows how to improve the user's lifestyle. For example, it may advise "do 30 minutes of aerobic exercise three times a week" or "try to eat a diet low in sugar."

[0673] Step 5:

[0674] The server sends the generated 3D avatar and advice to the user's device. The device displays this information using a graphical user interface (GUI). It is designed using React Native to allow for intuitive viewing of the avatar and advice.

[0675] Step 6:

[0676] Based on the presented avatar and advice, users review their daily lives and training plans and provide feedback to the system. This feedback is sent to the server and used to improve future predictive models. This allows the system to provide users with more accurate health management advice.

[0677] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0678] This invention provides more personalized health support by combining an emotion engine with a system designed to improve user health management. This system operates through the collaborative efforts of the server, terminal, and user.

[0679] The server first receives and stores health data collected from the user and their family. This data includes health checkup information, lifestyle data, and treatment history. The server uses this data to predict future health status using an AI model. Furthermore, it generates an avatar based on the prediction results, and this avatar helps visualize the user's future health status.

[0680] The emotion engine recognizes the user's emotional state and provides that information to the server. The server then uses the data from the emotion engine to generate emotionally appropriate advice. For example, if the user is feeling stressed, the server can emphasize specific relaxation techniques.

[0681] The generated emotion-responsive avatar and advice are sent to the user's device. The device displays the information received from the server in a way that is easy for the user to understand. For example, even if the user is in an unstable emotional state, the device will present the information using reassuring designs and messages.

[0682] Users receive health advice tailored to their health status and emotions via their device. This allows users to better improve their lifestyle and maintain or improve their health. User feedback is sent to the server and contributes to system improvement.

[0683] This system enables personalized healthcare that takes emotions into account, allowing for more effective management of users' health risks.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] Users enter personal and family health data into the device. This includes health checkup information, lifestyle data, and treatment history, which are then prepared for transmission to the system.

[0687] Step 2:

[0688] The terminal formats the entered health data, verifies its integrity, encrypts it, and then sends it to the server.

[0689] Step 3:

[0690] The server receives data sent from the terminal, organizes it in a database, and stores it. The stored data is then input into an AI model for predicting health status.

[0691] Step 4:

[0692] An AI model runs on the server to predict the user's future health status. Based on the prediction results, an avatar is generated that visualizes the user's health.

[0693] Step 5:

[0694] The device acquires data on the user's facial expressions and voice, and analyzes the user's emotions using an emotion engine. This emotional information is then sent to the server.

[0695] Step 6:

[0696] The server receives data from the emotion engine and generates health advice tailored to the user's emotional state. It creates advice that includes improvement suggestions that match the user's emotions.

[0697] Step 7:

[0698] The server sends an avatar and advice that corresponds to the user's emotions to the user's terminal. The avatar is designed with emotional sensitivity in mind.

[0699] Step 8:

[0700] The device displays received information and provides health advice in a user-friendly format. The user interface is designed with emotions in mind.

[0701] Step 9:

[0702] Users review and improve their lifestyle habits based on the advice provided by the device. They also provide feedback on the results of the advice and their impressions via the device, helping to improve the system.

[0703] (Example 2)

[0704] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0705] In modern society, personal health management is becoming increasingly important, but conventional health support systems have struggled to provide personalized advice that takes into account individual emotional states. Furthermore, there was a lack of technology to integrate health and emotional information and present it clearly to users. As a result, users found it difficult to take health-related actions that aligned with their emotions, potentially leading to a deterioration in their health.

[0706] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0707] In this invention, the server includes means for receiving and storing biometric data, means for estimating future health conditions, means for generating virtual representations, means for recognizing emotional states, means for creating personalized lifestyle guidance, and means for communicating this guidance to a user interface. This enables the user to comprehensively understand their own health and emotional states and select appropriate health actions based on that understanding.

[0708] "Biometric data" refers to information that indicates a user's health status, and includes data such as test results, lifestyle patterns, and treatment history.

[0709] "Future health status" refers to the predicted state of the user's health, estimated based on current health information.

[0710] "Estimation means" refers to a method or device for predicting future health status from received biometric data using technologies such as AI models.

[0711] A "virtual representation" is a digital avatar or graphic generated to visually represent the predicted results of a user's health status.

[0712] "Emotional state" refers to information that represents the user's psychological state, and is data obtained by analyzing facial expressions, voice, and other factors.

[0713] "Emotional analysis means" refers to a technology or device that analyzes a user's facial expressions, voice, etc., in real time to identify their emotional state.

[0714] "Individualized lifestyle guidance" refers to advice that includes specific actions and precautions tailored to the user's health and emotional state.

[0715] A "user interface" is a device or method for a user to receive information from a system, and includes terminals and screen displays.

[0716] This invention is a system for improving user health management, utilizing biometric data and emotional states to provide personalized health advice. The system functions through the cooperation of the server, terminal, and user.

[0717] The server first receives diverse biometric data from the user and their family and stores it in a database. This biometric data includes examination information, lifestyle pattern data, and treatment history. Based on the received data, the server uses machine learning libraries such as TensorFlow and PyTorch to build an AI model and predict the user's future health status. Based on this prediction, the server uses software such as Unity to generate a virtual representation that visualizes the future health status.

[0718] Regarding emotion analysis, when a user accesses the system through a terminal, the server analyzes facial expressions and voice data using emotion analysis tools. This technology utilizes OpenCV and relevant analysis software. The server considers these analysis results and uses a generative AI model to generate personalized health advice tailored to the user's emotions. For example, it generates advice based on a prompt such as, "Please suggest specific relaxation methods for when the user is feeling stressed."

[0719] The generated virtual representation and personalized advice are sent to the terminal. The terminal displays these visually and in text through an interface that is easy for the user to understand. Based on this, the user can select and implement actions that are appropriate to their health condition and emotions. This feedback is sent back to the server, and the accumulated data is used to continuously improve the accuracy and usefulness of the entire system.

[0720] As a concrete example, when the server inputs the prompt "Please tell me specific vegetables that are recommended for improving my diet" into the AI ​​model, it outputs customized dietary guidance. In this way, users can receive specific advice that they can implement in their daily lives.

[0721] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0722] Step 1:

[0723] The server receives biometric data sent from users and their families and stores it in a database. This data includes examination information, lifestyle pattern data, and treatment history. By receiving and storing biometric data as input, it builds a foundation for estimating future health status.

[0724] Step 2:

[0725] The server uses stored biometric data to build an AI model and predict future health status. Here, the AI ​​model is formed using machine learning libraries such as TensorFlow. Based on the input biometric data, it numerically and statistically analyzes the user's health risks and future health status, and outputs the results.

[0726] Step 3:

[0727] The server uses the predicted health status results to generate a virtual avatar. This uses Unity or similar software to visually represent future health status. Based on the predicted results as input, it generates graphic data and outputs it in a format that the user can easily understand.

[0728] Step 4:

[0729] The server recognizes the user's emotional state in real time using emotion analysis tools. It takes facial expression data and voice data as input, performs analysis using technologies such as OpenCV, and returns the results to the server. This provides the emotional analysis results.

[0730] Step 5:

[0731] The server considers the analyzed emotional information and uses a generative AI model to create personalized health advice. Taking the prompt "What refreshing methods would you recommend when a user is feeling tired?" as input, the generative AI model outputs specific lifestyle guidance plans.

[0732] Step 6:

[0733] The terminal, upon receiving data from the server, displays virtual representations and advice in a user-friendly interface. The design and messaging are carefully crafted to ensure user emotional reassurance. By outputting the received data onto the screen, users can intuitively understand the information.

[0734] Step 7:

[0735] Users provide feedback based on the avatars and advice they receive. This feedback is sent to the server and used to improve the system. Users input their experiences, impressions, and improvement requests, which are then used to improve the overall accuracy and performance of the system.

[0736] (Application Example 2)

[0737] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0738] In modern society, comprehensively managing people's health and providing health support tailored to individual emotional states is a crucial challenge. However, conventional health management systems struggle to provide health advice that adequately considers emotional states, resulting in insufficient improvement in users' lifestyles. Therefore, there is a need to realize health support that comprehensively analyzes users' health and emotions and encourages specific actions appropriate to those emotions.

[0739] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0740] In this invention, the server includes means for receiving and storing health data and emotional data from a user, generation means for predicting future health status using said health data and emotional data, and means for generating a virtual display that visualizes the predicted future health status and emotional status. This makes it possible to provide personalized health advice according to the user's emotional status and efficiently promote improvements in lifestyle habits.

[0741] "Health data" refers to information related to a user's physical condition and health, including health checkup information, lifestyle information, and treatment records.

[0742] "Emotional data" refers to information about the user's psychological state, including data such as the type and intensity of emotions obtained by the emotion engine.

[0743] The "generation means" refers to a processing means that uses an AI model to predict the future health state based on health data and emotional data received from the user, and generates a virtual display based on that prediction.

[0744] "Virtual representation" refers to avatars or graphics that visually represent predicted future health and emotional states, displayed in a way that is easy for the user to understand.

[0745] "Advice" includes suggestions and instructions provided to users to encourage improvements in their health and lifestyle, and is personalized according to the user's emotional state.

[0746] "Terminal" refers to equipment used by a user to utilize the system of the present invention, and includes devices such as smart glasses and smartphones.

[0747] In order to implement this invention, the user, server, and terminal must cooperate to build a system. The server receives and stores health data and emotional data from the user. Health data includes health checkup information, lifestyle information, treatment records, etc., and emotional data includes information about the user's emotional state obtained from the emotional engine.

[0748] The server uses this data to predict future health status using a generative AI model. This model employs deep learning libraries such as TensorFlow and PyTorch. Based on the prediction results, the server generates a virtual representation that visualizes future health and emotional states. The generated virtual representation is presented in a user-friendly format, such as an avatar or graphic.

[0749] Furthermore, the server generates personalized health advice based on virtual displays and health information, tailored to the user's emotional state. This advice is calculated by an AI model, taking into account past data and the user's current emotional state, and includes specific action guidelines.

[0750] The terminal is responsible for displaying virtual representations and advice sent from the server to the user. The terminal includes smart glasses and smartphones, allowing the user to receive advice visually. The terminal uses visual devices and processors to display information in real time.

[0751] As a concrete example of implementation, if the user detects stress, the device can offer suggestions for deep breathing and relaxation techniques in the form of a virtual counselor. Furthermore, the AI ​​model can generate appropriate advice using prompt examples such as: "If the user's emotional state indicates stress, what relaxation method would be best?"

[0752] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0753] Step 1:

[0754] The server receives health and emotional data submitted by users and stores them in a database. Inputs are data indicating the user's health checkup information and emotional state, while outputs are the health and emotional data registered in the database. This process involves format conversion to maintain data integrity.

[0755] Step 2:

[0756] The server inputs stored health and emotion data into an AI model to predict future health status. The input is formatted health and emotion data, and the output is numerical or textual information representing the predicted health status. Here, a generative AI model (e.g., TensorFlow) is used to perform the prediction calculation.

[0757] Step 3:

[0758] The server generates a visually easy-to-understand virtual representation based on predicted future health and emotional states. The input is predicted health and emotional data, and the output is a virtual avatar or graphic. Visualized graphic content is generated using design software.

[0759] Step 4:

[0760] The server generates personalized health advice based on the user's emotional state, along with a virtual display. Inputs include the virtual display, health information, and user emotion data, while output is advice containing specific action guidelines. The AI ​​model is queried using prompts (e.g., "If the user's emotional state indicates stress, what relaxation methods are best?") to generate appropriate advice.

[0761] Step 5:

[0762] The device displays virtual information and health advice received from the server to the user. The input is the virtual information and advice sent from the server, and the output is the information the user receives visually. The device includes smart glasses and smartphones, enabling real-time information presentation through applications.

[0763] Step 6:

[0764] Users raise their awareness of lifestyle habits and health through virtual displays and advice shown on their devices. Input is information from the device, and output is the user's review of their behavior and implementation of improvement measures. Users are expected to take concrete actions in their daily lives based on the information they receive.

[0765] 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.

[0766] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0767] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0768] 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.

[0769] Figure 9 shows an 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.

[0770] 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.

[0771] 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.

[0772] 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, motorcycles, etc., 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, for example, based 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.

[0773] 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."

[0774] 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.

[0775] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0776] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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 this memory.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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 the like 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.

[0785] 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 as being incorporated by reference.

[0786] The following is further disclosed regarding the embodiments described above.

[0787] (Claim 1)

[0788] A means of receiving and storing health data from users,

[0789] A generation method that uses the health data to predict future health status,

[0790] A means of generating an avatar that visualizes a predicted future health state,

[0791] A means of creating advice about current lifestyle habits based on the generated avatar and health information,

[0792] Means for transmitting the aforementioned avatar and advice to the user terminal,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, wherein the health data includes at least health checkup information, lifestyle data, and treatment history.

[0796] (Claim 3)

[0797] The system according to claim 1, wherein the advice includes specific action guidelines for improving the user's health.

[0798] "Example 1"

[0799] (Claim 1)

[0800] A means of receiving and recording biometric data from a user,

[0801] A generation function that processes the biometric data to estimate future health status,

[0802] A means of generating a digital representation that visualizes an estimated future health condition,

[0803] A means of creating advice on current lifestyle habits based on generated digital representations and health information,

[0804] Means for transferring the aforementioned digital representation and advice to the user device,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein the biometric data includes at least health checkup information, lifestyle data, and medical history.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the advice includes specific action guidelines for improving the user's health.

[0810] "Application Example 1"

[0811] (Claim 1)

[0812] A means of receiving and storing health data from users,

[0813] A generation method that uses the health data to predict future health status,

[0814] A means of generating an avatar that visualizes a predicted future health state,

[0815] A means of creating advice about current lifestyle habits based on the generated avatar and health information,

[0816] Means for transmitting the aforementioned avatar and advice to the user terminal,

[0817] A means of displaying training and lifestyle improvement indicators based on health data within the physical store where the user is located,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, wherein the health data includes at least health checkup information, lifestyle data, and exercise history.

[0821] (Claim 3)

[0822] The system according to claim 1, wherein the advice includes specific action guidelines for improving the user's health, and further includes means for enabling use in a physical store.

[0823] "Example 2 of combining an emotion engine"

[0824] (Claim 1)

[0825] A means of receiving and storing biometric data from a user,

[0826] An estimation means for predicting future health status using the said biometric data,

[0827] A means for generating a virtual representation that visualizes an estimated future health condition,

[0828] A means for recognizing a user's emotional state using emotion analysis tools,

[0829] A means of creating individualized life guidance based on the user's emotional state,

[0830] Means for transmitting the aforementioned virtual representation and life guidance to the user interface,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, wherein the biometric data includes at least examination information, lifestyle pattern data, and treatment history.

[0834] (Claim 3)

[0835] The system according to claim 1, wherein the lifestyle guidance includes specific behavioral suggestions for improving the user's health.

[0836] "Application example 2 when combining with an emotional engine"

[0837] (Claim 1)

[0838] A means of receiving and storing health data and emotional data from users,

[0839] A generation means for predicting future health status using the health data and emotional data,

[0840] A means for generating a virtual representation that visualizes predicted future health and emotional states,

[0841] A means for generating advice on current lifestyle habits based on generated virtual displays and health information, and for adjusting the advice according to emotional state,

[0842] Means for transmitting the aforementioned virtual display and advice to the terminal,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the health data includes at least health checkup information, lifestyle information, and treatment records.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the advice includes specific action guidelines for improving the user's health, and further includes adjustments to recommendations based on emotional state. [Explanation of Symbols]

[0848] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving and storing health data from users, A generation method that uses the health data to predict future health status, A means of generating an avatar that visualizes a predicted future health state, A means of creating advice about current lifestyle habits based on the generated avatar and health information, Means for transmitting the aforementioned avatar and advice to the user terminal, A system that includes this.

2. The system according to claim 1, wherein the health data includes at least health checkup information, lifestyle data, and treatment history.

3. The system according to claim 1, wherein the advice includes specific action guidelines for improving the user's health.

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