System

Abstract

A system is provided.SOLUTION: Generating means for generating individualized health advice and health plans based on the analyzing means, integrating means for incorporating the health plans into gamification elements, providing means for providing the generated health advice and plans to users, and receiving feedback from the users, wherein the generating means generates the individualized health advice and health plans based on the generated health advice and health plans, and the analyzing means analyzes the generated health advice and health plans using a generated AI model. A system comprising: feedback transmission means for transmitting the feedback data to a server; and feedback analysis means for analyzing the feedback and generating updated health advice and plans.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, an increasing number of middle-aged and elderly people find it difficult to regularly visit medical institutions due to their busy lifestyles. This leads to inadequate health management, resulting in an increased risk of lifestyle-related and chronic diseases. Furthermore, it is difficult to maintain motivation for health management, and many people fail to continue receiving health advice. Therefore, there is a need for a method that provides personalized health management that is independent of time and place, while also maintaining user motivation. [Means for solving the problem]

[0005] To address this issue, the present invention provides a system that uses a generative AI model to input and analyze a user's health data. The system generates personalized health advice and a health plan for the user and incorporates gamification elements to maintain motivation. Specifically, the system includes an input means for the user to input health data, a transmission means for transmitting the health data to a server, an analysis means for analyzing the data using a generative AI model, a generation means for generating a personalized health plan, an integration means for integrating the health plan with gamification elements, a provision means for providing the generated information, a feedback transmission means for receiving and transmitting feedback from the user, and a feedback analysis means for analyzing the feedback and generating updated health advice. This allows users to manage their health regardless of time or location, achieving continuous health management while maintaining motivation.

[0006] "User" means an individual who uses the system to input health data and receive health advice and plans.

[0007] "Health Data" refers to personal information entered by users, lifestyle information such as diet, exercise, and sleep, and information related to health status.

[0008] "Input means" refers to the device or interface that users use to input health data, such as a smartphone or PC.

[0009] "Transmission Means" refers to the technical mechanism for transmitting the health data entered through the Input Means to the Server.

[0010] "Server" refers to the central system that analyzes the received health data and runs the generative AI model.

[0011] "Generative AI model" refers to an artificial intelligence model that generates personalized health advice and plans based on the health data it receives.

[0012] "Analysis means" refers to the process of using a generative AI model to analyze a user's health data and evaluate their health status.

[0013] "Generation means" refers to a process for generating personalized health advice or a health plan based on the results of the analysis means.

[0014] "Integration means" refers to the technological mechanisms for incorporating the generated health plan into the gamification elements.

[0015] "Providing means" refers to the process of transmitting the generated health advice and plan to the user's terminal and displaying it.

[0016] "Feedback sending means" refers to a technical mechanism for receiving feedback data from users and sending it back to the server.

[0017] "Feedback analysis means" refers to the process that analyzes received feedback and updates health advice and plans.

[0018] "Gamification elements" refer to features that incorporate game elements to enhance enjoyment and motivation, such as awarding points or providing rewards when users complete health tasks. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention is a personal health advisor system that utilizes generative AI, specifically, a system that allows users to input health data, analyzes the data, and provides personalized health advice and health plans. The system includes the following major elements: input means, transmission means, analysis means, generation means, integration means, provision means, feedback transmission means, and feedback analysis means.

[0041] System Overview

[0042] 1. Enter your health data

[0043] Users use input methods (such as smartphones or PCs) to enter personal information and lifestyle data, such as age, gender, height, weight, diet, exercise habits, and sleep duration.

[0044] 2. Data transmission

[0045] The terminal validates the entered data to ensure it is accurate.

[0046] The device then encrypts the data and sends it securely to the server.

[0047] 3. Analysis by generative AI

[0048] The server decodes the received data and analyzes it using the generative AI model. The analysis method evaluates the user's health status and identifies individual health risks and necessary measures.

[0049] 4. Generating personalized advice

[0050] The server generates optimal health advice and health plans for users based on the analysis results. For example, if a user is deficient in a particular nutrient, a plan recommending foods containing that nutrient will be generated.

[0051] 5. Gamification Integration

[0052] The server then integrates the generated health plan with gamification elements, such as rewards once a certain number of points are accumulated.

[0053] 6. Information provision

[0054] The server transmits the generated advice and health plan, as well as gamification information, to the terminal.

[0055] The device displays this information to the user in an easy-to-understand manner, for example showing the next health action to be taken and the current progress.

[0056] 7. Receiving and Analyzing Feedback

[0057] The user acts on the advice and then enters the results and additional data (e.g., weight fluctuations and number of exercises) back into the device.

[0058] The terminal encrypts the input feedback data and transmits it to the server.

[0059] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data.

[0060] Specific examples

[0061] Case 1: Middle-aged men seeking to maintain their health

[0062] 1. User A (45 years old, male) uses his smartphone to enter his recent meal history (breakfast: toast and coffee, lunch: salad, dinner: pizza), exercise habits (gym once a week), and sleep time (6 hours).

[0063] 2. The device validates this data and sends it to the server.

[0064] 3. The server analyzes the data and determines whether you are not getting enough exercise.

[0065] 4. The server generates a personalized health plan, such as "recommended 30 minutes of walking three times a week."

[0066] 5. Add a gamification element where the server awards 10 points for each walk and when you collect 100 points you can receive virtual goods.

[0067] 6. The server sends this information to the terminal, which displays it to User A.

[0068] 7. User A walks and inputs the results (e.g., walking time and distance) into the terminal.

[0069] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Maintain walking three times a week and add strength training once a week."

[0070] As described above, this invention combines generative AI and gamification elements to support users in efficiently and continuously managing their health. This system makes it possible to provide optimal advice based on an individual's health condition and lifestyle, contributing to the maintenance of health and disease prevention for users.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The user opens their smartphone or PC and enters their personal information (age, gender, height, weight, etc.) and lifestyle data (dietary habits, exercise habits, sleep time, etc.).

[0074] Step 2:

[0075] The terminal validates the entered data, specifically checking the data format and range, and prompting the user to correct the input if necessary.

[0076] Step 3:

[0077] The terminal encrypts the data that has passed validation and sends it securely to the server.

[0078] Step 4:

[0079] The server decrypts the received data and checks whether it has been received correctly, thereby confirming that the data has been delivered to the server correctly.

[0080] Step 5:

[0081] The server normalizes the health data received, unifying the data scale and filling in missing values.

[0082] Step 6:

[0083] The server analyzes the normalized data using a generative AI model, which assesses the user's health status and identifies key health risks.

[0084] Step 7:

[0085] The server generates personalized health advice and health plans based on the analysis results. For example, if a person is deficient in a particular nutrient, it creates a plan that recommends foods containing that nutrient.

[0086] Step 8:

[0087] The server will integrate gamification elements into the generated health plan, such as adding points for completing health tasks and rewards for achieving certain goals.

[0088] Step 9:

[0089] The server transmits the generated advice, health plan, and gamification information to the terminal.

[0090] Step 10:

[0091] The device displays the received information to the user, including next health actions to take, current progress, and earned points.

[0092] Step 11:

[0093] The user acts on the advice provided and then enters the results and additional data (e.g., exercise time and weight fluctuations) back into the device.

[0094] Step 12:

[0095] The terminal encrypts the input feedback data and transmits it again to the server.

[0096] Step 13:

[0097] The server analyzes the received feedback data and generates more accurate advice and health plans based on the analysis results, updating the information as needed.

[0098] Step 14:

[0099] The server sends updated advice and health plans to the terminal, which displays them to the user.

[0100] This is the specific process flow of the HealthMate system, which helps users manage their health efficiently and maintain their motivation.

[0101] Example 1

[0102] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0103] In order to provide personalized health management in response to the increasing number of lifestyle-related diseases and health risks in modern society, it is necessary to provide specific and effective advice based on the user's lifestyle and health condition. However, with conventional health management systems, it is difficult to provide advice and plans that meet individual needs, and continuous feedback and improvement are not provided, resulting in insufficient health management for users.

[0104] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0105] In this invention, the server includes an input means for inputting health data from a user, a transmission means for transmitting the health data, a means for encrypting the transmitted health data and transmitting it to the server, an analysis means using a generative AI model to analyze the transmitted health data, a generation means for generating personalized health advice and a health plan based on the analysis means, an integration means for integrating the health plan with gamification elements, a provision means for providing the generated health advice and plan to the user, a feedback transmission means for receiving feedback data from the user and transmitting the feedback data to the server, and a feedback analysis means for analyzing the feedback and generating updated health advice and plans. This enables the provision of personalized health advice and plans to users, and continuous health management and feedback.

[0106] "Health data" refers to information about a user's health status and lifestyle, such as age, gender, height, weight, diet, exercise habits, and sleep duration.

[0107] "Input means" refers to the device or interface through which users input health data, including terminals such as smartphones and PCs.

[0108] "Transmission means" refers to a function for transmitting input health data to a server.

[0109] "Encryption method" refers to the mechanism used to encrypt data to keep it secure.

[0110] "Server" is a central computer system that receives and analyzes the transmitted data and provides generated health advice and plans.

[0111] A "generative AI model" refers to an artificial intelligence algorithm or program that analyzes and generates advice based on input data.

[0112] "Analysis means" refers to the function of analyzing the transmitted health data using a generative AI model.

[0113] "Generation means" refers to the function of generating personalized health advice and health plans based on the analysis results.

[0114] "Integration means" refers to methods and functions for integrating the generated health plan with gamification elements.

[0115] "Gamification elements" refer to incorporating game elements to increase user motivation, including point awarding and reward provision.

[0116] "Provision means" refers to the function for providing the generated health advice and plans to users.

[0117] "Feedback transmission means" refers to a function for transmitting feedback and additional data from users to the server.

[0118] "Feedback analysis means" refers to a function that analyzes received feedback data and generates updated health advice and plans.

[0119] The present invention is a personal health advisor system that uses generative AI to provide personalized health advice. Specifically, the system allows users to input health data, analyzes the data, and provides personalized health advice and health plans. Detailed embodiments of the system are described below.

[0120] The system includes an input means, a transmission means, an encryption means, a server, a generative AI model, an analysis means, a generation means, an integration means, a provision means, a feedback transmission means, and a feedback analysis means.

[0121] Entering health data

[0122] Users enter health data using a smartphone or PC. The hardware used can be a smartphone, tablet, or desktop PC. A dedicated health management application is provided as software. Users launch this application and enter data on items such as "age," "gender," "height," "weight," "diet," "exercise habits," and "sleep time."

[0123] Data Transmission and Encryption

[0124] The terminal validates the entered data. Validation is a process to check whether the entered data is accurate. For example, it checks that the age is over 0 years old, that the weight is within a reasonable range, etc. If invalid data is detected, an error message is displayed to the user, prompting them to correct it.

[0125] After the data is verified to be accurate, the device encrypts it. The latest encryption algorithm (e.g., AES-256) is used for encryption. The encrypted data is then securely transmitted to the server. The HTTPS protocol is used for data transmission, ensuring data security.

[0126] Analysis by generative AI

[0127] The server decodes the received data and generates a prompt for the generative AI model. The generative AI model used is, for example, OpenAI's GPT-3, and this model is used to analyze the data. An example of a prompt might be, "45-year-old male, inactive, recent meals: toast and coffee for breakfast, salad for lunch, pizza for dinner, gym once a week, 6 hours of sleep. Please generate optimal health advice based on this data."

[0128] The server sends prompts to the generative AI model and receives the generated analysis results and health advice. For example, the model might generate advice such as, "We recommend walking 30 minutes three times a week. In addition, we recommend consuming fish and eggs as you may be deficient in vitamin D."

[0129] Generating and synthesizing personalized advice

[0130] The server then creates a specific health plan based on the analysis results. For example, it could generate a personalized plan such as "a plan for walking 30 minutes every morning," "recommended fish recipes," or "a plan for strength training at the gym."

[0131] In addition, gamification elements will be integrated into the health plan created by the server. For example, 10 points will be awarded for each exercise session, and a reward (such as virtual goods) will be provided once a certain number of points have been accumulated. Specifically, a reward system such as "100 points will earn you a virtual good" will be set up.

[0132] Information provision

[0133] The server encrypts the completed health advice and plan, including gamification elements, and sends it to the device. The device then decrypts the received information and displays it to the user through the application's UI. The display includes a dashboard with information such as "Next action: Walk 30 minutes every morning" and "Current score: 40 / 100."

[0134] Receiving and analyzing feedback

[0135] The user again inputs their daily exercise results, dietary habits, weight fluctuations, etc. into the application. For example, they input feedback data such as "Today's walking time: 30 minutes, walking distance: 3 km." The device encrypts this feedback data and sends it to the server.

[0136] The server decodes and analyzes the feedback data. Based on the analysis, it generates new personalized health advice and an improved health plan. For example, new advice might be provided, such as "Maintain your walking frequency and add one strength training session per week."

[0137] Through the above process, personalized health advice and plans are provided, enabling the user to efficiently and continuously manage their health.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Step 1:

[0140] The user enters health data using a smartphone or PC. The user launches a dedicated application and enters health information such as age, gender, height, weight, diet, exercise habits, and sleep duration into an input form. Input is done through a form on the application. The entered data is validated in the next step. Examples of input data include "Age: 45 years old," "Gender: Male," "Height: 175 cm," "Weight: 80 kg," "Diet: Breakfast - toast and coffee, lunch - salad, dinner - pizza," "Exercise habits: Gym once a week," and "Sleep time: 6 hours."

[0141] Step 2:

[0142] The terminal validates the health data entered. Validation is the process of making sure the entered data is accurate. For example, it checks that the age is over 0 years old and that the weight is within an appropriate range. It separates normal data from data with errors, and displays an error message to the user if any invalid data is found. An example of an error message might be "Your age is invalid. Please enter it again." Once the data has been validated, it moves on to the next step.

[0143] Step 3:

[0144] The terminal encrypts the data that passes validation and sends it securely to the server. The encryption algorithm used is AES-256, which increases the security of the data. The encrypted data is sent to the server using the HTTPS protocol. The format of the data sent is encrypted binary data.

[0145] Step 4:

[0146] The server decodes the received data and generates a prompt for the generative AI model. An example of a prompt might be, "45-year-old male, inactive, recent meals: toast and coffee for breakfast, salad for lunch, pizza for dinner. Goes to the gym once a week, sleeps 6 hours. Please generate optimal health advice based on this data." OpenAI's GPT-3 is used as an example of a generative AI model. The server sends the prompt to the generative AI model and receives the analysis results.

[0147] Step 5:

[0148] The server analyzes the analysis results received from the AI ​​model and generates optimal health advice and health plans for the user. For example, specific advice may be generated such as "We recommend walking 30 minutes three times a week. In addition, we recommend consuming fish and eggs as you may be deficient in vitamin D." The generated advice is integrated into gamification elements in the next step.

[0149] Step 6:

[0150] The server integrates gamification elements into the generated health plan. The gamification elements include a system that awards points for each exercise session and provides rewards when a certain number of points are accumulated. Specifically, this includes elements such as "receive virtual goods for every 10 points" and "receive a gift card for 100 points." The integrated data then proceeds to the next step.

[0151] Step 7:

[0152] The server encrypts the generated health advice and gamification plan and sends it to the device. The encryption algorithm used is AES-256, and the transmission protocol is HTTPS. The transmitted data is in encrypted binary data format.

[0153] Step 8:

[0154] The device decodes the received data and displays it to the user. Through the UI of the dedicated application, information such as "Next action: Walk 30 minutes every morning" and "Current points: 40 / 100" is displayed on the dashboard.

[0155] Step 9:

[0156] The user re-enters feedback data such as daily exercise results, dietary habits, and weight fluctuations into the dedicated application. For example, they enter data such as "Today's walking time: 30 minutes, walking distance: 3km." The entered feedback data then proceeds to the next step.

[0157] Step 10:

[0158] The terminal validates the feedback data, encrypts it, and sends it to the server. Validation is the process of confirming the accuracy of the input data. The encryption method is AES-256, and the transmission protocol is HTTPS.

[0159] Step 11:

[0160] The server decodes and analyzes the feedback data. A generative AI model is used to generate new health advice based on the feedback data. For example, new advice might be provided, such as "Maintain your walking frequency and add one strength training session per week."

[0161] Through the above process, the user can receive personalized health advice and continuously manage their health.

[0162] (Application example 1)

[0163] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0164] Conventional health management systems have the problem of making it difficult for users to voluntarily input data and provide continuous feedback. Furthermore, while they often provide personalized health plans and advice, they often lack the motivation to encourage users to follow these plans. In particular, there is a demand for a system that enables health management linked to activities in physical stores. Furthermore, when using the system in physical stores, ease of use of the device and secure handling of data are key issues.

[0165] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0166] In this invention, the server includes: an input means for inputting health data from a user; a transmission means for transmitting the health data to the server; an analysis means using a generative AI model to analyze the transmitted health data; a generation means for generating personalized health advice and a health plan based on the analysis means; an integration means for incorporating the health plan into gamification elements; a provision means for providing the generated health advice and plan to the user; a feedback transmission means for receiving feedback from the user and transmitting the feedback data to the server; a feedback analysis means for analyzing the feedback and generating updated health advice and a plan; a physical store integration means for allowing users to input health data using a terminal in a physical store and providing personalized health plans and gamification elements based on the analysis results at the physical store; and a feedback receiving means for inputting the results of implementing the health plan as feedback to the terminal and transmitting the feedback to the server. This enables each user to receive appropriate advice and a health management plan, facilitate their implementation, and provide the results as feedback, thereby enabling more accurate health management.

[0167] "User" refers to an individual who uses the Personal Health Advisor System.

[0168] "Health data" refers to information related to a user's health status and lifestyle, such as their age, gender, height, weight, exercise habits, diet, and sleep duration.

[0169] "Input means" refers to the interface through which users input health data using a smartphone, tablet, PC, etc.

[0170] "Transmission means" refers to the means for encrypting the entered health data and transmitting it securely to the server.

[0171] "Generative AI model" refers to an artificial intelligence model that analyzes health data and generates personalized health advice and health plans.

[0172] "Analysis Means" refers to the means for analyzing health data using a generative AI model.

[0173] "Generation means" refers to means for generating personalized health advice and health plans based on the analysis results by the analysis means.

[0174] "Integration means" refers to means for incorporating the generated health plan into gamification elements.

[0175] "Delivery means" refers to the means for providing the generated health advice and plan to the user.

[0176] "Feedback sending means" refers to means for receiving feedback from a user and sending feedback data to a server.

[0177] "Feedback analysis means" refers to means for analyzing received feedback and generating updated health advice and plans.

[0178] "Physical store integration means" refers to a means by which users can input health data using a terminal in a physical store and receive personalized health plans and gamification elements based on the analysis results in the physical store.

[0179] The "feedback receiving means" refers to a means for inputting the results of the health plan implementation into the terminal as feedback again and transmitting it to the server.

[0180] This invention is a personal health advisor system that utilizes generative AI, specifically designed for use in brick-and-mortar stores. This system inputs users' health data via terminals installed in the brick-and-mortar stores, and based on that data, provides personalized health advice and promotes continuous health management through gamification elements.

[0181] System configuration

[0182] 1. Input Method

[0183] Users enter their health data (age, gender, height, weight, exercise habits, eating habits) using devices such as smartphones, tablets, and PCs installed in physical stores.

[0184] 2. Transmission Method

[0185] The device validates the entered health data to ensure it is accurate, then encrypts it and securely transmits it to the server, using the HTTPS protocol for this process.

[0186] 3. Analysis Methods Using Generative AI Models

[0187] The server decodes the received health data and analyzes it using a generative AI model (e.g., a model using TensorFlow or PyTorch). This analysis evaluates the user's current health status and health risks.

[0188] 4. Means of generating personalized advice and health plans

[0189] The server then generates optimal health advice and plans for each user based on the analysis results. For example, if a user is deficient in a particular nutrient, a plan recommending foods containing that nutrient will be generated.

[0190] 5. Means of integration with gamification elements

[0191] The server integrates the generated health plan with gamification elements, such as a system that awards points for each exercise session and offers products or services from a store when a certain number of points are accumulated.

[0192] 6. Means of providing information

[0193] The server sends the generated advice, plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user, such as the next health action to be taken and the user's current progress.

[0194] 7. Feedback Submission Methods

[0195] The user acts according to the advice and then inputs the results and additional data (e.g., weight fluctuations and number of exercise sessions) into the device again. The device then encrypts the input feedback data and sends it to the server.

[0196] 8. Feedback Analysis Methods

[0197] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data.

[0198] System operation example

[0199] A case of a middle-aged man aiming to maintain his health

[0200] 1. User A (male, 45 years old) uses a terminal at a physical store to enter his recent meal details (breakfast: toast and coffee, lunch: salad, dinner: pizza), exercise habits (gym once a week), and sleep time (6 hours).

[0201] 2. The device validates this data and sends it to the server.

[0202] 3. The server analyzes the data and determines whether you are not getting enough exercise.

[0203] 4. The server generates a personalized health plan, such as "recommended 30 minutes of walking three times a week."

[0204] 5. Add a gamification element by awarding 10 points for each walk the server completes, and once they have accumulated 100 points they can receive a reward from the store (e.g. a free training session).

[0205] 6. The server sends this information to the terminal, which displays it to User A.

[0206] 7. User A walks and enters the results (e.g., walking time and distance) into the terminal.

[0207] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Maintain walking three times a week and add strength training once a week."

[0208] Prompt Sentence Examples

[0209] 1. "45 years old, male, height 175 cm, weight 85 kg, exercises once a week."

[0210] 2. "Eating habits: Breakfast (toast and coffee), lunch (salad), dinner (pizza). Added exercise time to feedback."

[0211] As described above, the present invention is a system that supports efficient and continuous health management in physical stores, and specifically includes the provision of personalized health advice using generative AI models and the integration of gamification elements.

[0212] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0213] Step 1:

[0214] Users enter their health data (age, gender, height, weight, diet, exercise habits, and sleep time) using a terminal at a physical store.

[0215] Input: User's health data

[0216] Output: Health data stored on the device

[0217] Step 2:

[0218] The terminal validates the entered data to ensure it is accurate, then encrypts it and securely transmits it to the server.

[0219] Input: Health data stored on the device

[0220] Output: Encrypted health data is sent to the server.

[0221] Step 3:

[0222] The server decodes the received data and analyzes it using a generative AI model, which assesses the user's health status and health risks.

[0223] Input: Encrypted health data

[0224] Output: Analysis results (user's health status and health risks)

[0225] Step 4:

[0226] Based on the analysis results, the server generates personalized health advice and plans that are best suited to the user, such as "walking three times a week is recommended."

[0227] Input: Analysis results

[0228] Output: Personalized health advice and health plans

[0229] Step 5:

[0230] The server then integrates the generated health plan with gamification elements, such as rewarding points for each exercise session and allowing users to receive special rewards at physical stores once they have accumulated a certain number of points.

[0231] Input: personalized health advice and health plans

[0232] Output: A health plan with integrated gamification elements

[0233] Step 6:

[0234] The server sends the generated advice, plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user, such as the next health action to be taken and the current progress.

[0235] Input: Health plans with integrated gamification elements

[0236] Output: Health advice and plan displayed on the device

[0237] Step 7:

[0238] The user follows the advice and then enters the results and additional data (e.g., walking time and distance) back into the device.

[0239] Input: User action results and additional data

[0240] Output: Feedback data stored on the device

[0241] Step 8:

[0242] The terminal encrypts the input feedback data and transmits it to the server.

[0243] Input: Feedback data

[0244] Output: Encrypted feedback data is sent to the server

[0245] Step 9:

[0246] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data. For example, the next advice provided might be, "Maintain walking three times a week and add strength training once a week."

[0247] Input: Feedback data

[0248] Output: The new personalized advice and health plan is sent to the terminal.

[0249] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0250] This invention combines an emotion engine with a personal health advisor system that utilizes generative AI to provide more adaptive and personalized health advice and health plans. This system analyzes the user's health data and emotional state, and generates and provides optimal advice based on that data.

[0251] System Overview

[0252] 1. Entering health and emotional data

[0253] Users use input devices (smartphones or PCs) to input personal information, lifestyle data, and emotional state data. Emotional data is acquired through questionnaires, facial recognition cameras, voice assistants, etc.

[0254] 2. Data transmission

[0255] The device validates the entered health and emotion data, checking the data format and range, and prompting the user to make corrections if necessary.

[0256] The terminal encrypts the data that has passed validation and sends it securely to the server.

[0257] 3. Analysis using generative AI and emotion engine

[0258] The server decodes and normalizes the received data and analyzes it using a generative AI model and an emotion engine. The generative AI model analyzes health data, and the emotion engine analyzes emotion data.

[0259] The server integrates the analysis results of the emotion engine with the generative AI model to comprehensively evaluate the user's health and emotional state.

[0260] 4. Generating personalized advice

[0261] Based on the analysis results, the server generates optimal health advice and health plans for the user. For example, if the user is under high stress, a relaxation plan will be provided.

[0262] The server adjusts the generated health plan based on your emotional state, recommending a more aggressive exercise plan when you're feeling energized and a lighter exercise plan when you're feeling down.

[0263] 5. Gamification Integration

[0264] The server integrates gamification elements into the generated health plan, specifically adding a points system and virtual rewards mechanism.

[0265] 6. Information provision

[0266] The server sends the generated advice, health plan, and gamification information to the device.

[0267] The device displays this information to the user in an easy-to-understand manner, and measures such as changing the message depending on the user's emotional state are taken into consideration.

[0268] 7. Receiving and Analyzing Feedback

[0269] The user acts according to the advice and inputs the results into the terminal again, along with their new emotional state.

[0270] The terminal encrypts the input feedback data and emotion data and transmits them again to the server.

[0271] The server analyzes the feedback data and generates more accurate advice and health plans.

[0272] Specific examples

[0273] Case 1: Middle-aged women seeking health management

[0274] 1. User B (50 years old, female) uses her smartphone to enter her personal information, recent dietary habits, exercise habits, sleep time, and emotional state ("I'm feeling a little stressed today").

[0275] 2. The device validates this data and sends it to the server.

[0276] 3. The server analyzes the data and determines that the stress level is high.

[0277] 4. The server generates a personalized health plan, such as "Light yoga recommended three times a week to reduce stress."

[0278] 5. Add a gamification element where the server awards 10 points for each yoga session, and when you collect 100 points you can receive a virtual prize.

[0279] 6. The server sends this information to the device, which displays it to User B. An encouraging message tailored to User B's emotional state is also displayed.

[0280] 7. User B practices yoga and inputs the results (for example, yoga time and mood changes) into the terminal.

[0281] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Continue doing yoga twice a week and add walking once a week."

[0282] In this way, combining the emotion engine enables optimal health management according to the user's emotional state. This allows users to receive health management tailored to their emotions, making it easier for them to maintain motivation.

[0283] The processing flow will be explained below.

[0284] Step 1:

[0285] Users open their smartphones or PCs and enter personal information (age, gender, height, weight, etc.) and lifestyle data (dietary habits, exercise habits, sleep time, etc.). They also enter data on their emotional state. This emotional data is acquired through questionnaires, facial recognition, voice input, etc.

[0286] Step 2:

[0287] The terminal validates the entered data. Specifically, it checks the data format and range, and prompts the user to correct the entered data if necessary. For example, if the weight entered is out of range, the user is asked to re-enter it.

[0288] Step 3:

[0289] The device encrypts the health data and emotion data that have passed validation and transmits them securely to the server.

[0290] Step 4:

[0291] The server decrypts the received data and verifies that it was received correctly, thereby ensuring that the data has not been corrupted.

[0292] Step 5:

[0293] The health and emotion data received by the server is normalized, the data scale is unified, and missing values ​​are filled in. In this process, the data format is unified, making analysis easier.

[0294] Step 6:

[0295] The server analyzes the normalized health data using a generative AI model, which evaluates the user's health status and identifies health risks and areas for improvement. At the same time, it analyzes the emotional data using an emotion engine to evaluate the user's emotional state.

[0296] Step 7:

[0297] The server combines the results of the generative AI model and the emotion engine to perform a comprehensive evaluation, which generates advice and plans that take into account not only the user's health condition but also their emotional state.

[0298] Step 8:

[0299] The server generates personalized health advice and a health plan based on the analysis results. For example, if a user is gaining weight but experiencing high stress, it will suggest relaxation and light exercise focused on stress reduction.

[0300] Step 9:

[0301] The server integrates gamification elements into the generated health plan, specifically by awarding points for completing each health task and providing rewards for achieving certain goals.

[0302] Step 10:

[0303] The server sends the generated advice, health plan, and gamification information to the device.

[0304] Step 11:

[0305] The device then displays the information it receives to the user, including next health actions, current progress, points earned, and messages based on emotional state.

[0306] Step 12:

[0307] The user acts on the presented advice and inputs the results of the action and the new emotional state into the terminal, including feedback such as "Today's walk felt good."

[0308] Step 13:

[0309] The terminal encrypts the input feedback data and emotion data and transmits them again to the server.

[0310] Step 14:

[0311] The server analyzes the received feedback and emotion data, which is then used to generate more accurate advice and health plans using a generative AI model and emotion engine.

[0312] Step 15:

[0313] The server sends updated advice and health plans to the device, which then displays them to the user, allowing the user to continuously manage their health.

[0314] This is the specific processing flow of the HealthMate system, which combines an emotion engine. This system enables personalized health management that adapts not only to the user's health condition but also to their emotional state.

[0315] Example 2

[0316] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0317] Conventional health management systems only provide general advice based on users' health data and are unable to provide personalized health advice that takes into account the user's emotional state. Furthermore, they lacked gamification elements to maintain motivation, making it difficult to continue long-term health management. This prevented users from receiving effective health management tailored to their emotional state, reducing the efficiency of health improvement.

[0318] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0319] In this invention, the server includes: an input means for inputting health data and emotional data from a user; a transmission means for transmitting the health data and emotional data to the server; a validation means including a terminal for validating and encrypting the transmitted health data and emotional data; an analysis means for decrypting and normalizing the encrypted health data and emotional data and analyzing them using a generative AI model and an emotion engine; a generation means for generating personalized health advice and a health plan based on the analysis means; an adjustment means for adjusting the generated health plan based on the user's emotional state; an integration means for incorporating gamification elements into the health plan; a provision means for providing the generated health advice and plan to the user; a feedback transmission means for receiving feedback from the user and transmitting the feedback data and emotional data to the server; and a feedback analysis means for analyzing the feedback and generating updated health advice and a health plan. This allows for the provision of personalized health advice and a health plan that takes the user's emotional state into consideration. Furthermore, incorporating gamification elements can maintain user motivation and encourage continued health management.

[0320] "Health data" refers to information such as a user's personal information, lifestyle data, diet, exercise habits, and sleep time.

[0321] "Emotional data" refers to data that represents a user's emotional state, and refers to information obtained through questionnaires, facial recognition cameras, voice assistants, etc.

[0322] "Input means" refers to a device or interface that allows a user to input health and emotional data into the system.

[0323] "Transmission means" refers to the functions and processes for transmitting the input health data and emotion data to the server.

[0324] "Validation measures" refer to functions that check the format and scope of health data and emotional data and prompt corrections if necessary.

[0325] "Encryption" refers to the process of using cryptography to protect health and emotional data in order to securely transmit it to a server.

[0326] "Decryption and Normalization" refers to the process by which the server decrypts encrypted data and converts it into a format suitable for the system.

[0327] "Generative AI model" refers to an artificial intelligence model that analyzes health data and generates health advice and health plans appropriate for users.

[0328] "Emotion engine" refers to a computer program that analyzes emotion data and assesses a user's emotional state.

[0329] "Analysis Means" refers to the process of analyzing received data using the generative AI model and emotion engine.

[0330] "Generation means" refers to a function that generates personalized health advice and health plans based on the analysis results.

[0331] "Adjustment means" refers to a function that adjusts the generated health plan based on the user's emotional state.

[0332] "Integration measures" refers to the ability to incorporate gamification elements into health plans.

[0333] "Gamification elements" refers to a system that incorporates game elements such as a point system and virtual rewards to increase user motivation.

[0334] "Delivery means" refers to the process by which the generated health advice and health plans are provided to the user.

[0335] "Feedback transmission means" refers to a function for transmitting feedback data and new emotion data from the user to the server.

[0336] "Feedback analysis means" refers to the process that analyzes received feedback data and generates updated health advice and health plans.

[0337] This invention combines an emotion engine with a personal health advisor system that utilizes generative AI to provide more adaptive and personalized health advice and health plans. This system analyzes the user's health data and emotional state, and generates and provides optimal advice based on that data.

[0338] System Overview

[0339] Entering health and emotional data

[0340] A user launches an application using a device such as a smartphone or PC. The user enters personal information (age, gender, etc.), lifestyle data (dietary habits, exercise habits, sleep duration, etc.), and emotional state (e.g., "I feel a little stressed today"). Emotional data is obtained using questionnaires, facial recognition cameras, voice assistants, etc.

[0341] Sending data

[0342] The terminal validates the entered data, checking the data format and range and prompting the user to make corrections if necessary. After that, the terminal encrypts the data that passes validation using AES encryption technology and sends it securely to the server.

[0343] Analysis using generative AI and emotion engine

[0344] The server decodes and normalizes the received data, analyzes the health data using a generative AI model, and simultaneously analyzes the emotional data using an emotion engine. The server then integrates the results of the generative AI model and the emotion engine to comprehensively evaluate the user's health and emotional state.

[0345] Generating personalized advice

[0346] Based on the analysis results, the server generates optimal health advice and health plans for users. For example, if a user is under high stress, a relaxation plan is provided, and if the user is feeling well, a more active exercise plan is recommended.

[0347] Gamification Integration

[0348] The server integrates gamification elements into the generated health plans, specifically by adding a points system and virtual rewards mechanism to increase motivation.

[0349] Information provision

[0350] The server sends the generated advice, health plan, and gamification information to the device, which then displays this information in an easy-to-understand manner for the user. For example, the device may change the message depending on the user's emotional state.

[0351] Receiving and analyzing feedback

[0352] The user acts on the advice and then inputs the results and new emotional state back into the device. The device then sends this feedback data back to the server, which analyzes the feedback data and generates more accurate advice and health plans. This process continuously optimizes the user's health management.

[0353] Specific examples

[0354] Case 1: Middle-aged women seeking health management

[0355] 1. User B (50 years old, female) uses her smartphone to enter her personal information, recent dietary habits, exercise habits, sleep time, and emotional state ("I'm feeling a little stressed today").

[0356] 2. The device validates this data and sends it to the server.

[0357] 3. The server analyzes the data and determines that the stress level is high.

[0358] 4. The server generates a personalized health plan, such as "Light yoga recommended three times a week to reduce stress."

[0359] 5. Add a gamification element where the server awards 10 points for each yoga session, and when you collect 100 points you can receive a virtual prize.

[0360] 6. The server sends this information to the device, which displays it to the user, along with encouraging messages tailored to the user's emotional state.

[0361] 7. User B practices yoga and again inputs the results (for example, yoga time and mood changes) into the terminal.

[0362] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Continue doing yoga twice a week and add walking once a week."

[0363] By combining this system with an emotion engine, it is possible to provide optimal health management according to the user's emotional state. This allows users to receive health management tailored to their emotions, making it easier for them to maintain motivation.

[0364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0365] Step 1:

[0366] A user starts the application using a smartphone or PC. The user inputs personal information (e.g., age, gender), lifestyle data (e.g., dietary habits, exercise habits, sleep duration), and emotional state (e.g., "I feel a little stressed today"). The input data is saved in the application.

[0367] Input: Personal information, lifestyle data, emotional state

[0368] Output: Saving the entered data within the application

[0369] Specific behavior: The user enters data into the input form and clicks the submit button.

[0370] Step 2:

[0371] The device validates the entered health and emotion data. It checks the data format (for example, age is a number, gender is a string) and range (for example, sleep time is 0-24 hours), and asks the user to make corrections if necessary. Data that passes validation is encrypted in the next step.

[0372] Input: User-entered health and emotion data

[0373] Output: Data that has passed validation, and a request for correction if necessary

[0374] Specific operation: The terminal checks the data format and value range, and if an error message is displayed, prompts you to correct it.

[0375] Step 3:

[0376] The terminal encrypts the data that has passed validation using AES encryption technology, and the encrypted data is sent to the server.

[0377] Input: Data that passes validation

[0378] Output: Encrypted data, sent to server

[0379] Specific operation: The terminal encrypts the data using the AES algorithm and sends the encrypted data to the server over the network.

[0380] Step 4:

[0381] The server decrypts the received encrypted data and normalizes it, for example, standardizing date formats. The health data is then analyzed using a generative AI model. At the same time, the emotion engine analyzes the emotion data. The analysis results are integrated into an overall assessment.

[0382] Input: Encrypted data

[0383] Output: Decoded and normalized data, analysis results of the generative AI model, analysis results of the emotion engine, and overall evaluation

[0384] Specific operation: The server decrypts the encrypted data, unifies the data format, and applies the generative AI model and emotion engine to generate analysis results.

[0385] Step 5:

[0386] The server generates personalized health advice and health plans based on the overall assessment. For example, if the user is under high stress, a yoga plan may be generated. The generated health plan is tailored to the user's emotional state.

[0387] Input: Overall rating

[0388] Output: Personalized health advice and health plans

[0389] Specific operation: The server applies an algorithm based on the overall evaluation results to generate appropriate health advice and plans.

[0390] Step 6:

[0391] The server integrates gamification elements into the generated health plans, specifically adding a points system and virtual rewards mechanism.

[0392] Input: personalized health advice and health plans

[0393] Output: A health plan with integrated gamification elements

[0394] Specific operation: The server incorporates a points system and achievement rewards into the health plan.

[0395] Step 7:

[0396] The server sends the generated health advice, health plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user. It also displays messages based on the user's emotional state.

[0397] Input: Health plans with integrated gamification elements

[0398] Output: Displayed health advice and health plan

[0399] Specific operation: The server sends data to the device, and the device conveys the information to the user via push notification or in-app display.

[0400] Step 8:

[0401] The user acts according to the advice and then inputs the results and emotional state back into the device, which then re-encrypts this feedback data and sends it to the server.

[0402] Input: Feedback data and emotion data from users

[0403] Output: Encrypted feedback data, sent to server

[0404] Specific operation: The user fills out the feedback form and clicks the submit button. The device encrypts the data and sends it to the server.

[0405] Step 9:

[0406] The server analyzes the feedback data and generates new health advice and health plans, and further refines the health plans based on the analysis results.

[0407] Input: Encrypted feedback data

[0408] Output: Updated health advice and health plan

[0409] What happens: The server decodes the feedback data and applies analysis algorithms to generate new advice and plans.

[0410] (Application example 2)

[0411] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0412] In modern society, people often find it difficult to balance health management with their emotions. As lifestyles become more diverse and individuals' health and emotional states fluctuate daily, standardized health advice and plans are insufficient. Other issues include a lack of motivation and health advice tailored to individual emotional states. Therefore, there is a need for a system that provides adaptive and personalized health plans and advice based on each user's health and emotional data.

[0413] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for inputting health data and emotional data from the user, a transmission means for transmitting the health data and emotional data to the server, an analysis means using a generative AI model and an emotion engine to analyze the transmitted health data and emotional data, a generation means for generating personalized health advice and a health plan based on the analysis means, an integration means for incorporating the health plan into gamification elements, a provision means for providing the generated health advice and plan to the user, a feedback transmission means for receiving feedback from the user and transmitting the feedback data to the server, and a feedback analysis means for analyzing the feedback and generating updated health advice and a plan. This makes it possible to adaptively provide optimal health plans and advice based on the user's health and emotional state. Furthermore, the gamification elements can improve the user's motivation and support continuous health management.

[0414] "User" refers to an individual who uses the system.

[0415] "Health data" refers to information about an individual's health status, including, for example, dietary habits, exercise habits, and sleep duration.

[0416] "Emotional data" is information about an individual's emotional state, collected through surveys, facial recognition cameras, and voice assistants.

[0417] A "generative AI model" is an artificial intelligence model that analyzes a user's health data and generates personalized health plans and advice.

[0418] The "emotion engine" is an engine for analyzing emotion data and evaluating the user's emotional state.

[0419] "Gamification elements" are systems that incorporate game elements to increase user engagement, and examples include point systems and virtual rewards.

[0420] "Input means" refers to the means by which users input health data and emotional data into the system, and includes smartphones and PCs.

[0421] The "transmission means" is a means for transmitting the input health data and emotion data to the server.

[0422] "Analysis Means" means means for analyzing health data and emotion data using a generative AI model and emotion engine.

[0423] "Generation means" refers to means for generating personalized health advice and health plans based on the analysis results.

[0424] "Integration means" refers to means for integrating the generated health plan with gamification elements.

[0425] The "provision means" is a means for providing the generated health advice and plan to the user.

[0426] The "feedback sending means" is a means for receiving feedback from a user and sending it to the server.

[0427] "Feedback analysis means" means for analyzing received feedback and generating updated health advice and plans.

[0428] System Overview

[0429] The system according to the present invention is a system for providing personalized health advice and health plans using health and emotional data of a user. Specifically, the system includes the following elements:

[0430] 1. Input means for inputting health data and emotion data from the user

[0431] Users use their smartphones or PCs to enter personal information, lifestyle data, recent dietary habits, exercise habits, sleep time, emotional state, etc. Emotional data is collected through questionnaires, facial recognition cameras, and voice assistants.

[0432] 2. Means for transmitting health data and emotion data to the server

[0433] The device validates the entered health and emotion data, encrypts it, and securely transmits it to the server.

[0434] 3. Analysis methods using generative AI models and emotion engines to analyze data

[0435] The server decodes and normalizes the received data and analyzes the health data using a generative AI model. At the same time, it analyzes the emotion data using an emotion engine. The generative AI model evaluates the user's health status, and the emotion engine evaluates their emotional state.

[0436] 4. Means for generating personalized health advice and health plans

[0437] Based on the analysis results, the server generates optimal health advice and health plans for the user. For example, if the user is experiencing high stress, a relaxation plan will be provided.

[0438] 5. Integrating health plans into gamification elements

[0439] The server integrates gamification elements into the generated health plan, specifically adding a points system and virtual rewards mechanism.

[0440] 6. Means for providing generated health advice and plans to users

[0441] The server transmits the generated advice, health plan, and gamification information to the terminal, which then displays this information to the user.

[0442] 7. Feedback sending means for receiving feedback and sending feedback data to the server

[0443] The user then inputs the results of their actions based on the advice into the terminal, including their new emotional state. The terminal then transmits this feedback data and emotional data to the server.

[0444] 8. Feedback analysis means for analyzing the feedback and generating updated health advice and plans.

[0445] The server analyzes the feedback data and generates new health advice and plans, which allows for more accurate advice and health plans to be provided to the user.

[0446] Hardware and software used

[0447] Hardware:

[0448] Smartphone, PC: Used to enter and display user data and receive feedback.

[0449] software:

[0450] Cloud server: Used to analyze data and generate advice.

[0451] Generative AI models: Analyze health data and generate personalized advice.

[0452] Emotion engine: Analyzes emotional data and generates appropriate messages based on emotional state.

[0453] Specific examples

[0454] User A opens his smartphone and inputs his personal information, recent dietary habits, exercise habits, and sleep time, as well as his emotional state, such as "I feel a little stressed today." This data is sent to the server and analyzed by a generative AI model and emotion engine. The server generates a personalized plan for stress reduction, such as "Light yoga recommended three times a week," and adds a message based on the user's emotional state, such as "Relax today." This information is then displayed on the smartphone. When the user practices yoga, points are awarded, and when a certain number of points are reached, virtual rewards are earned.

[0455] Prompt Sentence Examples

[0456] Age: 30

[0457] Gender: Male

[0458] Recent meal: Chicken, salad, pasta

[0459] Exercise habits: Running twice a month

[0460] Sleep time: 7 hours

[0461] Emotional state: Very tired

[0462] In this way, it is possible to provide users with appropriate health plans and emotional support tailored to their specific circumstances.

[0463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0464] Step 1:

[0465] Users use a smartphone or PC to input personal information, recent dietary habits, exercise habits, sleep duration, and emotional state (e.g., "I feel a little stressed today") using a dedicated application. This input method generates input data.

[0466] Step 2:

[0467] The device validates the entered health and emotion data. For example, it checks the data format and range, and prompts the user to make corrections if necessary. Once validated, the data is encrypted and sent to the cloud server. Encryption is performed using technologies such as SSL / TLS.

[0468] Step 3:

[0469] The server decrypts the received encrypted data and normalizes it. This data includes personal information, health data, and emotional data. The server then analyzes the health data using a generative AI model to generate a personalized health plan. At the same time, it analyzes the emotional data using an emotional engine to generate appropriate emotional messages.

[0470] Step 4:

[0471] The server combines the analysis results of the generative AI model and the emotion engine to generate optimal health advice and plans for users. For example, if a user is in a high stress state, the server generates a plan such as "recommended light yoga three times a week to reduce stress."

[0472] Step 5:

[0473] The server then integrates the generated health plan with gamification elements, such as a points system and virtual rewards system. For example, points are awarded for each yoga session, and virtual goods can be awarded once a certain number of points are accumulated.

[0474] Step 6:

[0475] The server sends the generated health plan, advice, and gamification information to the end user's device, which displays the received information in an easy-to-understand format, including encouraging messages based on the user's emotional state.

[0476] Step 7:

[0477] The user acts according to the advice and health plan provided and inputs the results as feedback into the device, including the time spent doing yoga and changes in mood during that time.

[0478] Step 8:

[0479] The terminal again transmits the feedback data and new emotion data to the server, which is then encrypted before transmission.

[0480] Step 9:

[0481] The server analyzes the feedback data and generates new health advice and health plans based on the most recent data, thereby providing more accurate advice to the user.

[0482] Through these steps, the system can adaptively provide optimal health plans and advice according to the user's health and emotional state. Furthermore, the gamification element can increase user motivation and support continuous health management.

[0483] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0484] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0485] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0486] [Second embodiment]

[0487] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0488] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0489] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0490] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0491] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0492] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0493] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0494] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0495] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0496] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0497] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0498] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0499] The present invention is a personal health advisor system that utilizes generative AI, specifically, a system that allows users to input health data, analyzes the data, and provides personalized health advice and health plans. The system includes the following major elements: input means, transmission means, analysis means, generation means, integration means, provision means, feedback transmission means, and feedback analysis means.

[0500] System Overview

[0501] 1. Enter your health data

[0502] Users use input methods (such as smartphones or PCs) to enter personal information and lifestyle data, such as age, gender, height, weight, diet, exercise habits, and sleep duration.

[0503] 2. Data transmission

[0504] The terminal validates the entered data to ensure it is accurate.

[0505] The device then encrypts the data and sends it securely to the server.

[0506] 3. Analysis by generative AI

[0507] The server decodes the received data and analyzes it using the generative AI model. The analysis method evaluates the user's health status and identifies individual health risks and necessary measures.

[0508] 4. Generating personalized advice

[0509] The server generates optimal health advice and health plans for users based on the analysis results. For example, if a user is deficient in a particular nutrient, a plan recommending foods containing that nutrient will be generated.

[0510] 5. Gamification Integration

[0511] The server then integrates the generated health plan with gamification elements, such as rewards once a certain number of points are accumulated.

[0512] 6. Information provision

[0513] The server transmits the generated advice and health plan, as well as gamification information, to the terminal.

[0514] The device displays this information to the user in an easy-to-understand manner, for example showing the next health action to be taken and the current progress.

[0515] 7. Receiving and Analyzing Feedback

[0516] The user acts on the advice and then enters the results and additional data (e.g., weight fluctuations and number of exercises) back into the device.

[0517] The terminal encrypts the input feedback data and transmits it to the server.

[0518] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data.

[0519] Specific examples

[0520] Case 1: Middle-aged men seeking to maintain their health

[0521] 1. User A (45 years old, male) uses his smartphone to enter his recent meal history (breakfast: toast and coffee, lunch: salad, dinner: pizza), exercise habits (gym once a week), and sleep time (6 hours).

[0522] 2. The device validates this data and sends it to the server.

[0523] 3. The server analyzes the data and determines whether you are not getting enough exercise.

[0524] 4. The server generates a personalized health plan, such as "recommended 30 minutes of walking three times a week."

[0525] 5. Add a gamification element where the server awards 10 points for each walk and when you collect 100 points you can receive virtual goods.

[0526] 6. The server sends this information to the terminal, which displays it to User A.

[0527] 7. User A walks and inputs the results (e.g., walking time and distance) into the terminal.

[0528] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Maintain walking three times a week and add strength training once a week."

[0529] As described above, this invention combines generative AI and gamification elements to support users in efficiently and continuously managing their health. This system makes it possible to provide optimal advice based on an individual's health condition and lifestyle, contributing to the maintenance of health and disease prevention for users.

[0530] The processing flow will be explained below.

[0531] Step 1:

[0532] The user opens their smartphone or PC and enters their personal information (age, gender, height, weight, etc.) and lifestyle data (dietary habits, exercise habits, sleep time, etc.).

[0533] Step 2:

[0534] The terminal validates the entered data, specifically checking the data format and range, and prompting the user to correct the input if necessary.

[0535] Step 3:

[0536] The terminal encrypts the data that has passed validation and sends it securely to the server.

[0537] Step 4:

[0538] The server decrypts the received data and checks whether it has been received correctly, thereby confirming that the data has been delivered to the server correctly.

[0539] Step 5:

[0540] The server normalizes the health data received, unifying the data scale and filling in missing values.

[0541] Step 6:

[0542] The server analyzes the normalized data using a generative AI model, which assesses the user's health status and identifies key health risks.

[0543] Step 7:

[0544] The server generates personalized health advice and health plans based on the analysis results. For example, if a person is deficient in a particular nutrient, it creates a plan that recommends foods containing that nutrient.

[0545] Step 8:

[0546] The server will integrate gamification elements into the generated health plan, such as adding points for completing health tasks and rewards for achieving certain goals.

[0547] Step 9:

[0548] The server transmits the generated advice, health plan, and gamification information to the terminal.

[0549] Step 10:

[0550] The device displays the received information to the user, including next health actions to take, current progress, and earned points.

[0551] Step 11:

[0552] The user acts on the advice provided and then enters the results and additional data (e.g., exercise time and weight fluctuations) back into the device.

[0553] Step 12:

[0554] The terminal encrypts the input feedback data and transmits it again to the server.

[0555] Step 13:

[0556] The server analyzes the received feedback data and generates more accurate advice and health plans based on the analysis results, updating the information as needed.

[0557] Step 14:

[0558] The server sends updated advice and health plans to the terminal, which displays them to the user.

[0559] This is the specific process flow of the HealthMate system, which helps users manage their health efficiently and maintain their motivation.

[0560] Example 1

[0561] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0562] In order to provide personalized health management in response to the increasing number of lifestyle-related diseases and health risks in modern society, it is necessary to provide specific and effective advice based on the user's lifestyle and health condition. However, with conventional health management systems, it is difficult to provide advice and plans that meet individual needs, and continuous feedback and improvement are not provided, resulting in insufficient health management for users.

[0563] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0564] In this invention, the server includes an input means for inputting health data from a user, a transmission means for transmitting the health data, a means for encrypting the transmitted health data and transmitting it to the server, an analysis means using a generative AI model to analyze the transmitted health data, a generation means for generating personalized health advice and a health plan based on the analysis means, an integration means for integrating the health plan with gamification elements, a provision means for providing the generated health advice and plan to the user, a feedback transmission means for receiving feedback data from the user and transmitting the feedback data to the server, and a feedback analysis means for analyzing the feedback and generating updated health advice and plans. This enables the provision of personalized health advice and plans to users, and continuous health management and feedback.

[0565] "Health data" refers to information about a user's health status and lifestyle, such as age, gender, height, weight, diet, exercise habits, and sleep duration.

[0566] "Input means" refers to the device or interface through which users input health data, including terminals such as smartphones and PCs.

[0567] "Transmission means" refers to a function for transmitting input health data to a server.

[0568] "Encryption method" refers to the mechanism used to encrypt data to keep it secure.

[0569] "Server" is a central computer system that receives and analyzes the transmitted data and provides generated health advice and plans.

[0570] A "generative AI model" refers to an artificial intelligence algorithm or program that analyzes and generates advice based on input data.

[0571] "Analysis means" refers to the function of analyzing the transmitted health data using a generative AI model.

[0572] "Generation means" refers to the function of generating personalized health advice and health plans based on the analysis results.

[0573] "Integration means" refers to methods and functions for integrating the generated health plan with gamification elements.

[0574] "Gamification elements" refer to incorporating game elements to increase user motivation, including point awarding and reward provision.

[0575] "Provision means" refers to the function for providing the generated health advice and plans to users.

[0576] "Feedback transmission means" refers to a function for transmitting feedback and additional data from users to the server.

[0577] "Feedback analysis means" refers to a function that analyzes received feedback data and generates updated health advice and plans.

[0578] The present invention is a personal health advisor system that uses generative AI to provide personalized health advice. Specifically, the system allows users to input health data, analyzes the data, and provides personalized health advice and health plans. Detailed embodiments of the system are described below.

[0579] The system includes an input means, a transmission means, an encryption means, a server, a generative AI model, an analysis means, a generation means, an integration means, a provision means, a feedback transmission means, and a feedback analysis means.

[0580] Entering health data

[0581] Users enter health data using a smartphone or PC. The hardware used can be a smartphone, tablet, or desktop PC. A dedicated health management application is provided as software. Users launch this application and enter data on items such as "age," "gender," "height," "weight," "diet," "exercise habits," and "sleep time."

[0582] Data Transmission and Encryption

[0583] The terminal validates the entered data. Validation is a process to check whether the entered data is accurate. For example, it checks that the age is over 0 years old, that the weight is within a reasonable range, etc. If invalid data is detected, an error message is displayed to the user, prompting them to correct it.

[0584] After the data is verified to be accurate, the device encrypts it. The latest encryption algorithm (e.g., AES-256) is used for encryption. The encrypted data is then securely transmitted to the server. The HTTPS protocol is used for data transmission, ensuring data security.

[0585] Analysis by generative AI

[0586] The server decodes the received data and generates a prompt for the generative AI model. The generative AI model used is, for example, OpenAI's GPT-3, and this model is used to analyze the data. An example of a prompt might be, "45-year-old male, inactive, recent meals: toast and coffee for breakfast, salad for lunch, pizza for dinner, gym once a week, 6 hours of sleep. Please generate optimal health advice based on this data."

[0587] The server sends prompts to the generative AI model and receives the generated analysis results and health advice. For example, the model might generate advice such as, "We recommend walking 30 minutes three times a week. In addition, we recommend consuming fish and eggs as you may be deficient in vitamin D."

[0588] Generating and synthesizing personalized advice

[0589] The server then creates a specific health plan based on the analysis results. For example, it could generate a personalized plan such as "a plan for walking 30 minutes every morning," "recommended fish recipes," or "a plan for strength training at the gym."

[0590] In addition, gamification elements will be integrated into the health plan created by the server. For example, 10 points will be awarded for each exercise session, and a reward (such as virtual goods) will be provided once a certain number of points have been accumulated. Specifically, a reward system such as "100 points will earn you a virtual good" will be set up.

[0591] Information provision

[0592] The server encrypts the completed health advice and plan, including gamification elements, and sends it to the device. The device then decrypts the received information and displays it to the user through the application's UI. The display includes a dashboard with information such as "Next action: Walk 30 minutes every morning" and "Current score: 40 / 100."

[0593] Receiving and analyzing feedback

[0594] The user again inputs their daily exercise results, dietary habits, weight fluctuations, etc. into the application. For example, they input feedback data such as "Today's walking time: 30 minutes, walking distance: 3 km." The device encrypts this feedback data and sends it to the server.

[0595] The server decodes and analyzes the feedback data. Based on the analysis, it generates new personalized health advice and an improved health plan. For example, new advice might be provided, such as "Maintain your walking frequency and add one strength training session per week."

[0596] Through the above process, personalized health advice and plans are provided, enabling the user to efficiently and continuously manage their health.

[0597] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0598] Step 1:

[0599] The user enters health data using a smartphone or PC. The user launches a dedicated application and enters health information such as age, gender, height, weight, diet, exercise habits, and sleep duration into an input form. Input is done through a form on the application. The entered data is validated in the next step. Examples of input data include "Age: 45 years old," "Gender: Male," "Height: 175 cm," "Weight: 80 kg," "Diet: Breakfast - toast and coffee, lunch - salad, dinner - pizza," "Exercise habits: Gym once a week," and "Sleep time: 6 hours."

[0600] Step 2:

[0601] The terminal validates the health data entered. Validation is the process of making sure the entered data is accurate. For example, it checks that the age is over 0 years old and that the weight is within an appropriate range. It separates normal data from data with errors, and displays an error message to the user if any invalid data is found. An example of an error message might be "Your age is invalid. Please enter it again." Once the data has been validated, it moves on to the next step.

[0602] Step 3:

[0603] The terminal encrypts the data that passes validation and sends it securely to the server. The encryption algorithm used is AES-256, which increases the security of the data. The encrypted data is sent to the server using the HTTPS protocol. The format of the data sent is encrypted binary data.

[0604] Step 4:

[0605] The server decodes the received data and generates a prompt for the generative AI model. An example of a prompt might be, "45-year-old male, inactive, recent meals: toast and coffee for breakfast, salad for lunch, pizza for dinner. Goes to the gym once a week, sleeps 6 hours. Please generate optimal health advice based on this data." OpenAI's GPT-3 is used as an example of a generative AI model. The server sends the prompt to the generative AI model and receives the analysis results.

[0606] Step 5:

[0607] The server analyzes the analysis results received from the AI ​​model and generates optimal health advice and health plans for the user. For example, specific advice may be generated such as "We recommend walking 30 minutes three times a week. In addition, we recommend consuming fish and eggs as you may be deficient in vitamin D." The generated advice is integrated into gamification elements in the next step.

[0608] Step 6:

[0609] The server integrates gamification elements into the generated health plan. The gamification elements include a system that awards points for each exercise session and provides rewards when a certain number of points are accumulated. Specifically, this includes elements such as "receive virtual goods for every 10 points" and "receive a gift card for 100 points." The integrated data then proceeds to the next step.

[0610] Step 7:

[0611] The server encrypts the generated health advice and gamification plan and sends it to the device. The encryption algorithm used is AES-256, and the transmission protocol is HTTPS. The transmitted data is in encrypted binary data format.

[0612] Step 8:

[0613] The device decodes the received data and displays it to the user. Through the UI of the dedicated application, information such as "Next action: Walk 30 minutes every morning" and "Current points: 40 / 100" is displayed on the dashboard.

[0614] Step 9:

[0615] The user re-enters feedback data such as daily exercise results, dietary habits, and weight fluctuations into the dedicated application. For example, they enter data such as "Today's walking time: 30 minutes, walking distance: 3km." The entered feedback data then proceeds to the next step.

[0616] Step 10:

[0617] The terminal validates the feedback data, encrypts it, and sends it to the server. Validation is the process of confirming the accuracy of the input data. The encryption method is AES-256, and the transmission protocol is HTTPS.

[0618] Step 11:

[0619] The server decodes and analyzes the feedback data. A generative AI model is used to generate new health advice based on the feedback data. For example, new advice might be provided, such as "Maintain your walking frequency and add one strength training session per week."

[0620] Through the above process, the user can receive personalized health advice and continuously manage their health.

[0621] (Application example 1)

[0622] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0623] Conventional health management systems have the problem of making it difficult for users to voluntarily input data and provide continuous feedback. Furthermore, while they often provide personalized health plans and advice, they often lack the motivation to encourage users to follow these plans. In particular, there is a demand for a system that enables health management linked to activities in physical stores. Furthermore, when using the system in physical stores, ease of use of the device and secure handling of data are key issues.

[0624] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0625] In this invention, the server includes: an input means for inputting health data from a user; a transmission means for transmitting the health data to the server; an analysis means using a generative AI model to analyze the transmitted health data; a generation means for generating personalized health advice and a health plan based on the analysis means; an integration means for incorporating the health plan into gamification elements; a provision means for providing the generated health advice and plan to the user; a feedback transmission means for receiving feedback from the user and transmitting the feedback data to the server; a feedback analysis means for analyzing the feedback and generating updated health advice and a plan; a physical store integration means for allowing users to input health data using a terminal in a physical store and providing personalized health plans and gamification elements based on the analysis results at the physical store; and a feedback receiving means for inputting the results of implementing the health plan as feedback to the terminal and transmitting the feedback to the server. This enables each user to receive appropriate advice and a health management plan, facilitate their implementation, and provide the results as feedback, thereby enabling more accurate health management.

[0626] "User" refers to an individual who uses the Personal Health Advisor System.

[0627] "Health data" refers to information related to a user's health status and lifestyle, such as their age, gender, height, weight, exercise habits, diet, and sleep duration.

[0628] "Input means" refers to the interface through which users input health data using a smartphone, tablet, PC, etc.

[0629] "Transmission means" refers to the means for encrypting the entered health data and transmitting it securely to the server.

[0630] "Generative AI model" refers to an artificial intelligence model that analyzes health data and generates personalized health advice and health plans.

[0631] "Analysis Means" refers to the means for analyzing health data using a generative AI model.

[0632] "Generation means" refers to means for generating personalized health advice and health plans based on the analysis results by the analysis means.

[0633] "Integration means" refers to means for incorporating the generated health plan into gamification elements.

[0634] "Delivery means" refers to the means for providing the generated health advice and plan to the user.

[0635] "Feedback sending means" refers to means for receiving feedback from a user and sending feedback data to a server.

[0636] "Feedback analysis means" refers to means for analyzing received feedback and generating updated health advice and plans.

[0637] "Physical store integration means" refers to a means by which users can input health data using a terminal in a physical store and receive personalized health plans and gamification elements based on the analysis results in the physical store.

[0638] The "feedback receiving means" refers to a means for inputting the results of the health plan implementation into the terminal as feedback again and transmitting it to the server.

[0639] This invention is a personal health advisor system that utilizes generative AI, specifically designed for use in brick-and-mortar stores. This system inputs users' health data via terminals installed in the brick-and-mortar stores, and based on that data, provides personalized health advice and promotes continuous health management through gamification elements.

[0640] System configuration

[0641] 1. Input Method

[0642] Users enter their health data (age, gender, height, weight, exercise habits, eating habits) using devices such as smartphones, tablets, and PCs installed in physical stores.

[0643] 2. Transmission Method

[0644] The device validates the entered health data to ensure it is accurate, then encrypts it and securely transmits it to the server, using the HTTPS protocol for this process.

[0645] 3. Analysis Methods Using Generative AI Models

[0646] The server decodes the received health data and analyzes it using a generative AI model (e.g., a model using TensorFlow or PyTorch). This analysis evaluates the user's current health status and health risks.

[0647] 4. Means of generating personalized advice and health plans

[0648] The server then generates optimal health advice and plans for each user based on the analysis results. For example, if a user is deficient in a particular nutrient, a plan recommending foods containing that nutrient will be generated.

[0649] 5. Means of integration with gamification elements

[0650] The server integrates the generated health plan with gamification elements, such as a system that awards points for each exercise session and offers products or services from a store when a certain number of points are accumulated.

[0651] 6. Means of providing information

[0652] The server sends the generated advice, plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user, such as the next health action to be taken and the user's current progress.

[0653] 7. Feedback Submission Methods

[0654] The user acts according to the advice and then inputs the results and additional data (e.g., weight fluctuations and number of exercise sessions) into the device again. The device then encrypts the input feedback data and sends it to the server.

[0655] 8. Feedback Analysis Methods

[0656] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data.

[0657] System operation example

[0658] A case of a middle-aged man aiming to maintain his health

[0659] 1. User A (male, 45 years old) uses a terminal at a physical store to enter his recent meal details (breakfast: toast and coffee, lunch: salad, dinner: pizza), exercise habits (gym once a week), and sleep time (6 hours).

[0660] 2. The device validates this data and sends it to the server.

[0661] 3. The server analyzes the data and determines whether you are not getting enough exercise.

[0662] 4. The server generates a personalized health plan, such as "recommended 30 minutes of walking three times a week."

[0663] 5. Add a gamification element by awarding 10 points for each walk the server completes, and once they have accumulated 100 points they can receive a reward from the store (e.g. a free training session).

[0664] 6. The server sends this information to the terminal, which displays it to User A.

[0665] 7. User A walks and enters the results (e.g., walking time and distance) into the terminal.

[0666] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Maintain walking three times a week and add strength training once a week."

[0667] Prompt Sentence Examples

[0668] 1. "45 years old, male, height 175 cm, weight 85 kg, exercises once a week."

[0669] 2. "Eating habits: Breakfast (toast and coffee), lunch (salad), dinner (pizza). Added exercise time to feedback."

[0670] As described above, the present invention is a system that supports efficient and continuous health management in physical stores, and specifically includes the provision of personalized health advice using generative AI models and the integration of gamification elements.

[0671] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0672] Step 1:

[0673] Users enter their health data (age, gender, height, weight, diet, exercise habits, and sleep time) using a terminal at a physical store.

[0674] Input: User's health data

[0675] Output: Health data stored on the device

[0676] Step 2:

[0677] The terminal validates the entered data to ensure it is accurate, then encrypts it and securely transmits it to the server.

[0678] Input: Health data stored on the device

[0679] Output: Encrypted health data is sent to the server.

[0680] Step 3:

[0681] The server decodes the received data and analyzes it using a generative AI model, which assesses the user's health status and health risks.

[0682] Input: Encrypted health data

[0683] Output: Analysis results (user's health status and health risks)

[0684] Step 4:

[0685] Based on the analysis results, the server generates personalized health advice and plans that are best suited to the user, such as "walking three times a week is recommended."

[0686] Input: Analysis results

[0687] Output: Personalized health advice and health plans

[0688] Step 5:

[0689] The server then integrates the generated health plan with gamification elements, such as rewarding points for each exercise session and allowing users to receive special rewards at physical stores once they have accumulated a certain number of points.

[0690] Input: personalized health advice and health plans

[0691] Output: A health plan with integrated gamification elements

[0692] Step 6:

[0693] The server sends the generated advice, plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user, such as the next health action to be taken and the current progress.

[0694] Input: Health plans with integrated gamification elements

[0695] Output: Health advice and plan displayed on the device

[0696] Step 7:

[0697] The user follows the advice and then enters the results and additional data (e.g., walking time and distance) back into the device.

[0698] Input: User action results and additional data

[0699] Output: Feedback data stored on the device

[0700] Step 8:

[0701] The terminal encrypts the input feedback data and transmits it to the server.

[0702] Input: Feedback data

[0703] Output: Encrypted feedback data is sent to the server

[0704] Step 9:

[0705] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data. For example, the next advice provided might be, "Maintain walking three times a week and add strength training once a week."

[0706] Input: Feedback data

[0707] Output: The new personalized advice and health plan is sent to the terminal.

[0708] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0709] This invention combines an emotion engine with a personal health advisor system that utilizes generative AI to provide more adaptive and personalized health advice and health plans. This system analyzes the user's health data and emotional state, and generates and provides optimal advice based on that data.

[0710] System Overview

[0711] 1. Entering health and emotional data

[0712] Users use input devices (smartphones or PCs) to input personal information, lifestyle data, and emotional state data. Emotional data is acquired through questionnaires, facial recognition cameras, voice assistants, etc.

[0713] 2. Data transmission

[0714] The device validates the entered health and emotion data, checking the data format and range, and prompting the user to make corrections if necessary.

[0715] The terminal encrypts the data that has passed validation and sends it securely to the server.

[0716] 3. Analysis using generative AI and emotion engine

[0717] The server decodes and normalizes the received data and analyzes it using a generative AI model and an emotion engine. The generative AI model analyzes health data, and the emotion engine analyzes emotion data.

[0718] The server integrates the analysis results of the emotion engine with the generative AI model to comprehensively evaluate the user's health and emotional state.

[0719] 4. Generating personalized advice

[0720] Based on the analysis results, the server generates optimal health advice and health plans for the user. For example, if the user is under high stress, a relaxation plan will be provided.

[0721] The server adjusts the generated health plan based on your emotional state, recommending a more aggressive exercise plan when you're feeling energized and a lighter exercise plan when you're feeling down.

[0722] 5. Gamification Integration

[0723] The server integrates gamification elements into the generated health plan, specifically adding a points system and virtual rewards mechanism.

[0724] 6. Information provision

[0725] The server sends the generated advice, health plan, and gamification information to the device.

[0726] The device displays this information to the user in an easy-to-understand manner, and measures such as changing the message depending on the user's emotional state are taken into consideration.

[0727] 7. Receiving and Analyzing Feedback

[0728] The user acts according to the advice and inputs the results into the terminal again, along with their new emotional state.

[0729] The terminal encrypts the input feedback data and emotion data and transmits them again to the server.

[0730] The server analyzes the feedback data and generates more accurate advice and health plans.

[0731] Specific examples

[0732] Case 1: Middle-aged women seeking health management

[0733] 1. User B (50 years old, female) uses her smartphone to enter her personal information, recent dietary habits, exercise habits, sleep time, and emotional state ("I'm feeling a little stressed today").

[0734] 2. The device validates this data and sends it to the server.

[0735] 3. The server analyzes the data and determines that the stress level is high.

[0736] 4. The server generates a personalized health plan, such as "Light yoga recommended three times a week to reduce stress."

[0737] 5. Add a gamification element where the server awards 10 points for each yoga session, and when you collect 100 points you can receive a virtual prize.

[0738] 6. The server sends this information to the device, which displays it to User B. An encouraging message tailored to User B's emotional state is also displayed.

[0739] 7. User B practices yoga and inputs the results (for example, yoga time and mood changes) into the terminal.

[0740] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Continue doing yoga twice a week and add walking once a week."

[0741] In this way, combining the emotion engine enables optimal health management according to the user's emotional state. This allows users to receive health management tailored to their emotions, making it easier for them to maintain motivation.

[0742] The processing flow will be explained below.

[0743] Step 1:

[0744] Users open their smartphones or PCs and enter personal information (age, gender, height, weight, etc.) and lifestyle data (dietary habits, exercise habits, sleep time, etc.). They also enter data on their emotional state. This emotional data is acquired through questionnaires, facial recognition, voice input, etc.

[0745] Step 2:

[0746] The terminal validates the entered data. Specifically, it checks the data format and range, and prompts the user to correct the entered data if necessary. For example, if the weight entered is out of range, the user is asked to re-enter it.

[0747] Step 3:

[0748] The device encrypts the health data and emotion data that have passed validation and transmits them securely to the server.

[0749] Step 4:

[0750] The server decrypts the received data and verifies that it was received correctly, thereby ensuring that the data has not been corrupted.

[0751] Step 5:

[0752] The health and emotion data received by the server is normalized, the data scale is unified, and missing values ​​are filled in. In this process, the data format is unified, making analysis easier.

[0753] Step 6:

[0754] The server analyzes the normalized health data using a generative AI model, which evaluates the user's health status and identifies health risks and areas for improvement. At the same time, it analyzes the emotional data using an emotion engine to evaluate the user's emotional state.

[0755] Step 7:

[0756] The server combines the results of the generative AI model and the emotion engine to perform a comprehensive evaluation, which generates advice and plans that take into account not only the user's health condition but also their emotional state.

[0757] Step 8:

[0758] The server generates personalized health advice and a health plan based on the analysis results. For example, if a user is gaining weight but experiencing high stress, it will suggest relaxation and light exercise focused on stress reduction.

[0759] Step 9:

[0760] The server integrates gamification elements into the generated health plan, specifically by awarding points for completing each health task and providing rewards for achieving certain goals.

[0761] Step 10:

[0762] The server sends the generated advice, health plan, and gamification information to the device.

[0763] Step 11:

[0764] The device then displays the information it receives to the user, including next health actions, current progress, points earned, and messages based on emotional state.

[0765] Step 12:

[0766] The user acts on the presented advice and inputs the results of the action and the new emotional state into the terminal, including feedback such as "Today's walk felt good."

[0767] Step 13:

[0768] The terminal encrypts the input feedback data and emotion data and transmits them again to the server.

[0769] Step 14:

[0770] The server analyzes the received feedback and emotion data, which is then used to generate more accurate advice and health plans using a generative AI model and emotion engine.

[0771] Step 15:

[0772] The server sends updated advice and health plans to the device, which then displays them to the user, allowing the user to continuously manage their health.

[0773] This is the specific processing flow of the HealthMate system, which combines an emotion engine. This system enables personalized health management that adapts not only to the user's health condition but also to their emotional state.

[0774] Example 2

[0775] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0776] Conventional health management systems only provide general advice based on users' health data and are unable to provide personalized health advice that takes into account the user's emotional state. Furthermore, they lacked gamification elements to maintain motivation, making it difficult to continue long-term health management. This prevented users from receiving effective health management tailored to their emotional state, reducing the efficiency of health improvement.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0778] In this invention, the server includes: an input means for inputting health data and emotional data from a user; a transmission means for transmitting the health data and emotional data to the server; a validation means including a terminal for validating and encrypting the transmitted health data and emotional data; an analysis means for decrypting and normalizing the encrypted health data and emotional data and analyzing them using a generative AI model and an emotion engine; a generation means for generating personalized health advice and a health plan based on the analysis means; an adjustment means for adjusting the generated health plan based on the user's emotional state; an integration means for incorporating gamification elements into the health plan; a provision means for providing the generated health advice and plan to the user; a feedback transmission means for receiving feedback from the user and transmitting the feedback data and emotional data to the server; and a feedback analysis means for analyzing the feedback and generating updated health advice and a health plan. This allows for the provision of personalized health advice and a health plan that takes the user's emotional state into consideration. Furthermore, incorporating gamification elements can maintain user motivation and encourage continued health management.

[0779] "Health data" refers to information such as a user's personal information, lifestyle data, diet, exercise habits, and sleep time.

[0780] "Emotional data" refers to data that represents a user's emotional state, and refers to information obtained through questionnaires, facial recognition cameras, voice assistants, etc.

[0781] "Input means" refers to a device or interface that allows a user to input health and emotional data into the system.

[0782] "Transmission means" refers to the functions and processes for transmitting the input health data and emotion data to the server.

[0783] "Validation measures" refer to functions that check the format and scope of health data and emotional data and prompt corrections if necessary.

[0784] "Encryption" refers to the process of using cryptography to protect health and emotional data in order to securely transmit it to a server.

[0785] "Decryption and Normalization" refers to the process by which the server decrypts encrypted data and converts it into a format suitable for the system.

[0786] "Generative AI model" refers to an artificial intelligence model that analyzes health data and generates health advice and health plans appropriate for users.

[0787] "Emotion engine" refers to a computer program that analyzes emotion data and assesses a user's emotional state.

[0788] "Analysis Means" refers to the process of analyzing received data using the generative AI model and emotion engine.

[0789] "Generation means" refers to a function that generates personalized health advice and health plans based on the analysis results.

[0790] "Adjustment means" refers to a function that adjusts the generated health plan based on the user's emotional state.

[0791] "Integration measures" refers to the ability to incorporate gamification elements into health plans.

[0792] "Gamification elements" refers to a system that incorporates game elements such as a point system and virtual rewards to increase user motivation.

[0793] "Delivery means" refers to the process by which the generated health advice and health plans are provided to the user.

[0794] "Feedback transmission means" refers to a function for transmitting feedback data and new emotion data from the user to the server.

[0795] "Feedback analysis means" refers to the process that analyzes received feedback data and generates updated health advice and health plans.

[0796] This invention combines an emotion engine with a personal health advisor system that utilizes generative AI to provide more adaptive and personalized health advice and health plans. This system analyzes the user's health data and emotional state, and generates and provides optimal advice based on that data.

[0797] System Overview

[0798] Entering health and emotional data

[0799] A user launches an application using a device such as a smartphone or PC. The user enters personal information (age, gender, etc.), lifestyle data (dietary habits, exercise habits, sleep duration, etc.), and emotional state (e.g., "I feel a little stressed today"). Emotional data is obtained using questionnaires, facial recognition cameras, voice assistants, etc.

[0800] Sending data

[0801] The terminal validates the entered data, checking the data format and range and prompting the user to make corrections if necessary. After that, the terminal encrypts the data that passes validation using AES encryption technology and sends it securely to the server.

[0802] Analysis using generative AI and emotion engine

[0803] The server decodes and normalizes the received data, analyzes the health data using a generative AI model, and simultaneously analyzes the emotional data using an emotion engine. The server then integrates the results of the generative AI model and the emotion engine to comprehensively evaluate the user's health and emotional state.

[0804] Generating personalized advice

[0805] Based on the analysis results, the server generates optimal health advice and health plans for users. For example, if a user is under high stress, a relaxation plan is provided, and if the user is feeling well, a more active exercise plan is recommended.

[0806] Gamification Integration

[0807] The server integrates gamification elements into the generated health plans, specifically by adding a points system and virtual rewards mechanism to increase motivation.

[0808] Information provision

[0809] The server sends the generated advice, health plan, and gamification information to the device, which then displays this information in an easy-to-understand manner for the user. For example, the device may change the message depending on the user's emotional state.

[0810] Receiving and analyzing feedback

[0811] The user acts on the advice and then inputs the results and new emotional state back into the device. The device then sends this feedback data back to the server, which analyzes the feedback data and generates more accurate advice and health plans. This process continuously optimizes the user's health management.

[0812] Specific examples

[0813] Case 1: Middle-aged women seeking health management

[0814] 1. User B (50 years old, female) uses her smartphone to enter her personal information, recent dietary habits, exercise habits, sleep time, and emotional state ("I'm feeling a little stressed today").

[0815] 2. The device validates this data and sends it to the server.

[0816] 3. The server analyzes the data and determines that the stress level is high.

[0817] 4. The server generates a personalized health plan, such as "Light yoga recommended three times a week to reduce stress."

[0818] 5. Add a gamification element where the server awards 10 points for each yoga session, and when you collect 100 points you can receive a virtual prize.

[0819] 6. The server sends this information to the device, which displays it to the user, along with encouraging messages tailored to the user's emotional state.

[0820] 7. User B practices yoga and again inputs the results (for example, yoga time and mood changes) into the terminal.

[0821] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Continue doing yoga twice a week and add walking once a week."

[0822] By combining this system with an emotion engine, it is possible to provide optimal health management according to the user's emotional state. This allows users to receive health management tailored to their emotions, making it easier for them to maintain motivation.

[0823] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0824] Step 1:

[0825] A user starts the application using a smartphone or PC. The user inputs personal information (e.g., age, gender), lifestyle data (e.g., dietary habits, exercise habits, sleep duration), and emotional state (e.g., "I feel a little stressed today"). The input data is saved in the application.

[0826] Input: Personal information, lifestyle data, emotional state

[0827] Output: Saving the entered data within the application

[0828] Specific behavior: The user enters data into the input form and clicks the submit button.

[0829] Step 2:

[0830] The device validates the entered health and emotion data. It checks the data format (for example, age is a number, gender is a string) and range (for example, sleep time is 0-24 hours), and asks the user to make corrections if necessary. Data that passes validation is encrypted in the next step.

[0831] Input: User-entered health and emotion data

[0832] Output: Data that has passed validation, and a request for correction if necessary

[0833] Specific operation: The terminal checks the data format and value range, and if an error message is displayed, prompts you to correct it.

[0834] Step 3:

[0835] The terminal encrypts the data that has passed validation using AES encryption technology, and the encrypted data is sent to the server.

[0836] Input: Data that passes validation

[0837] Output: Encrypted data, sent to server

[0838] Specific operation: The terminal encrypts the data using the AES algorithm and sends the encrypted data to the server over the network.

[0839] Step 4:

[0840] The server decrypts the received encrypted data and normalizes it, for example, standardizing date formats. The health data is then analyzed using a generative AI model. At the same time, the emotion engine analyzes the emotion data. The analysis results are integrated into an overall assessment.

[0841] Input: Encrypted data

[0842] Output: Decoded and normalized data, analysis results of the generative AI model, analysis results of the emotion engine, and overall evaluation

[0843] Specific operation: The server decrypts the encrypted data, unifies the data format, and applies the generative AI model and emotion engine to generate analysis results.

[0844] Step 5:

[0845] The server generates personalized health advice and health plans based on the overall assessment. For example, if the user is under high stress, a yoga plan may be generated. The generated health plan is tailored to the user's emotional state.

[0846] Input: Overall rating

[0847] Output: Personalized health advice and health plans

[0848] Specific operation: The server applies an algorithm based on the overall evaluation results to generate appropriate health advice and plans.

[0849] Step 6:

[0850] The server integrates gamification elements into the generated health plans, specifically adding a points system and virtual rewards mechanism.

[0851] Input: personalized health advice and health plans

[0852] Output: A health plan with integrated gamification elements

[0853] Specific operation: The server incorporates a points system and achievement rewards into the health plan.

[0854] Step 7:

[0855] The server sends the generated health advice, health plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user. It also displays messages based on the user's emotional state.

[0856] Input: Health plans with integrated gamification elements

[0857] Output: Displayed health advice and health plan

[0858] Specific operation: The server sends data to the device, and the device conveys the information to the user via push notification or in-app display.

[0859] Step 8:

[0860] The user acts according to the advice and then inputs the results and emotional state back into the device, which then re-encrypts this feedback data and sends it to the server.

[0861] Input: Feedback data and emotion data from users

[0862] Output: Encrypted feedback data, sent to server

[0863] Specific operation: The user fills out the feedback form and clicks the submit button. The device encrypts the data and sends it to the server.

[0864] Step 9:

[0865] The server analyzes the feedback data and generates new health advice and health plans, and further refines the health plans based on the analysis results.

[0866] Input: Encrypted feedback data

[0867] Output: Updated health advice and health plan

[0868] What happens: The server decodes the feedback data and applies analysis algorithms to generate new advice and plans.

[0869] (Application example 2)

[0870] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0871] In modern society, people often find it difficult to balance health management with their emotions. As lifestyles become more diverse and individuals' health and emotional states fluctuate daily, standardized health advice and plans are insufficient. Other issues include a lack of motivation and health advice tailored to individual emotional states. Therefore, there is a need for a system that provides adaptive and personalized health plans and advice based on each user's health and emotional data.

[0872] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for inputting health data and emotional data from the user, a transmission means for transmitting the health data and emotional data to the server, an analysis means using a generative AI model and an emotion engine to analyze the transmitted health data and emotional data, a generation means for generating personalized health advice and a health plan based on the analysis means, an integration means for incorporating the health plan into gamification elements, a provision means for providing the generated health advice and plan to the user, a feedback transmission means for receiving feedback from the user and transmitting the feedback data to the server, and a feedback analysis means for analyzing the feedback and generating updated health advice and a plan. This makes it possible to adaptively provide optimal health plans and advice based on the user's health and emotional state. Furthermore, the gamification elements can improve the user's motivation and support continuous health management.

[0873] "User" refers to an individual who uses the system.

[0874] "Health data" refers to information about an individual's health status, including, for example, dietary habits, exercise habits, and sleep duration.

[0875] "Emotional data" is information about an individual's emotional state, collected through surveys, facial recognition cameras, and voice assistants.

[0876] A "generative AI model" is an artificial intelligence model that analyzes a user's health data and generates personalized health plans and advice.

[0877] The "emotion engine" is an engine for analyzing emotion data and evaluating the user's emotional state.

[0878] "Gamification elements" are systems that incorporate game elements to increase user engagement, and examples include point systems and virtual rewards.

[0879] "Input means" refers to the means by which users input health data and emotional data into the system, and includes smartphones and PCs.

[0880] The "transmission means" is a means for transmitting the input health data and emotion data to the server.

[0881] "Analysis Means" means means for analyzing health data and emotion data using a generative AI model and emotion engine.

[0882] "Generation means" refers to means for generating personalized health advice and health plans based on the analysis results.

[0883] "Integration means" refers to means for integrating the generated health plan with gamification elements.

[0884] The "provision means" is a means for providing the generated health advice and plan to the user.

[0885] The "feedback sending means" is a means for receiving feedback from a user and sending it to the server.

[0886] "Feedback analysis means" means for analyzing received feedback and generating updated health advice and plans.

[0887] System Overview

[0888] The system according to the present invention is a system for providing personalized health advice and health plans using health and emotional data of a user. Specifically, the system includes the following elements:

[0889] 1. Input means for inputting health data and emotion data from the user

[0890] Users use their smartphones or PCs to enter personal information, lifestyle data, recent dietary habits, exercise habits, sleep time, emotional state, etc. Emotional data is collected through questionnaires, facial recognition cameras, and voice assistants.

[0891] 2. Means for transmitting health data and emotion data to the server

[0892] The device validates the entered health and emotion data, encrypts it, and securely transmits it to the server.

[0893] 3. Analysis methods using generative AI models and emotion engines to analyze data

[0894] The server decodes and normalizes the received data and analyzes the health data using a generative AI model. At the same time, it analyzes the emotion data using an emotion engine. The generative AI model evaluates the user's health status, and the emotion engine evaluates their emotional state.

[0895] 4. Means for generating personalized health advice and health plans

[0896] Based on the analysis results, the server generates optimal health advice and health plans for the user. For example, if the user is experiencing high stress, a relaxation plan will be provided.

[0897] 5. Integrating health plans into gamification elements

[0898] The server integrates gamification elements into the generated health plan, specifically adding a points system and virtual rewards mechanism.

[0899] 6. Means for providing generated health advice and plans to users

[0900] The server transmits the generated advice, health plan, and gamification information to the terminal, which then displays this information to the user.

[0901] 7. Feedback sending means for receiving feedback and sending feedback data to the server

[0902] The user then inputs the results of their actions based on the advice into the terminal, including their new emotional state. The terminal then transmits this feedback data and emotional data to the server.

[0903] 8. Feedback analysis means for analyzing the feedback and generating updated health advice and plans.

[0904] The server analyzes the feedback data and generates new health advice and plans, which allows for more accurate advice and health plans to be provided to the user.

[0905] Hardware and software used

[0906] Hardware:

[0907] Smartphone, PC: Used to enter and display user data and receive feedback.

[0908] software:

[0909] Cloud server: Used to analyze data and generate advice.

[0910] Generative AI models: Analyze health data and generate personalized advice.

[0911] Emotion engine: Analyzes emotional data and generates appropriate messages based on emotional state.

[0912] Specific examples

[0913] User A opens his smartphone and inputs his personal information, recent dietary habits, exercise habits, and sleep time, as well as his emotional state, such as "I feel a little stressed today." This data is sent to the server and analyzed by a generative AI model and emotion engine. The server generates a personalized plan for stress reduction, such as "Light yoga recommended three times a week," and adds a message based on the user's emotional state, such as "Relax today." This information is then displayed on the smartphone. When the user practices yoga, points are awarded, and when a certain number of points are reached, virtual rewards are earned.

[0914] Prompt Sentence Examples

[0915] Age: 30

[0916] Gender: Male

[0917] Recent meal: Chicken, salad, pasta

[0918] Exercise habits: Running twice a month

[0919] Sleep time: 7 hours

[0920] Emotional state: Very tired

[0921] In this way, it is possible to provide users with appropriate health plans and emotional support tailored to their specific circumstances.

[0922] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0923] Step 1:

[0924] Users use a smartphone or PC to input personal information, recent dietary habits, exercise habits, sleep duration, and emotional state (e.g., "I feel a little stressed today") using a dedicated application. This input method generates input data.

[0925] Step 2:

[0926] The device validates the entered health and emotion data. For example, it checks the data format and range, and prompts the user to make corrections if necessary. Once validated, the data is encrypted and sent to the cloud server. Encryption is performed using technologies such as SSL / TLS.

[0927] Step 3:

[0928] The server decrypts the received encrypted data and normalizes it. This data includes personal information, health data, and emotional data. The server then analyzes the health data using a generative AI model to generate a personalized health plan. At the same time, it analyzes the emotional data using an emotional engine to generate appropriate emotional messages.

[0929] Step 4:

[0930] The server combines the analysis results of the generative AI model and the emotion engine to generate optimal health advice and plans for users. For example, if a user is in a high stress state, the server generates a plan such as "recommended light yoga three times a week to reduce stress."

[0931] Step 5:

[0932] The server then integrates the generated health plan with gamification elements, such as a points system and virtual rewards system. For example, points are awarded for each yoga session, and virtual goods can be awarded once a certain number of points are accumulated.

[0933] Step 6:

[0934] The server sends the generated health plan, advice, and gamification information to the end user's device, which displays the received information in an easy-to-understand format, including encouraging messages based on the user's emotional state.

[0935] Step 7:

[0936] The user acts according to the advice and health plan provided and inputs the results as feedback into the device, including the time spent doing yoga and changes in mood during that time.

[0937] Step 8:

[0938] The terminal again transmits the feedback data and new emotion data to the server, which is then encrypted before transmission.

[0939] Step 9:

[0940] The server analyzes the feedback data and generates new health advice and health plans based on the most recent data, thereby providing more accurate advice to the user.

[0941] Through these steps, the system can adaptively provide optimal health plans and advice according to the user's health and emotional state. Furthermore, the gamification element can increase user motivation and support continuous health management.

[0942] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0943] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0944] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0945] [Third embodiment]

[0946] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0947] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0948] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0949] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0950] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0951] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0952] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0953] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0954] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0955] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0956] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0957] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0958] The present invention is a personal health advisor system that utilizes generative AI, specifically, a system that allows users to input health data, analyzes the data, and provides personalized health advice and health plans. The system includes the following major elements: input means, transmission means, analysis means, generation means, integration means, provision means, feedback transmission means, and feedback analysis means.

[0959] System Overview

[0960] 1. Enter your health data

[0961] Users use input methods (such as smartphones or PCs) to enter personal information and lifestyle data, such as age, gender, height, weight, diet, exercise habits, and sleep duration.

[0962] 2. Data transmission

[0963] The terminal validates the entered data to ensure it is accurate.

[0964] The device then encrypts the data and sends it securely to the server.

[0965] 3. Analysis by generative AI

[0966] The server decodes the received data and analyzes it using the generative AI model. The analysis method evaluates the user's health status and identifies individual health risks and necessary measures.

[0967] 4. Generating personalized advice

[0968] The server generates optimal health advice and health plans for users based on the analysis results. For example, if a user is deficient in a particular nutrient, a plan recommending foods containing that nutrient will be generated.

[0969] 5. Gamification Integration

[0970] The server then integrates the generated health plan with gamification elements, such as rewards once a certain number of points are accumulated.

[0971] 6. Information provision

[0972] The server transmits the generated advice and health plan, as well as gamification information, to the terminal.

[0973] The device displays this information to the user in an easy-to-understand manner, for example showing the next health action to be taken and the current progress.

[0974] 7. Receiving and Analyzing Feedback

[0975] The user acts on the advice and then enters the results and additional data (e.g., weight fluctuations and number of exercises) back into the device.

[0976] The terminal encrypts the input feedback data and transmits it to the server.

[0977] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data.

[0978] Specific examples

[0979] Case 1: Middle-aged men seeking to maintain their health

[0980] 1. User A (45 years old, male) uses his smartphone to enter his recent meal history (breakfast: toast and coffee, lunch: salad, dinner: pizza), exercise habits (gym once a week), and sleep time (6 hours).

[0981] 2. The device validates this data and sends it to the server.

[0982] 3. The server analyzes the data and determines whether you are not getting enough exercise.

[0983] 4. The server generates a personalized health plan, such as "recommended 30 minutes of walking three times a week."

[0984] 5. Add a gamification element where the server awards 10 points for each walk and when you collect 100 points you can receive virtual goods.

[0985] 6. The server sends this information to the terminal, which displays it to User A.

[0986] 7. User A walks and inputs the results (e.g., walking time and distance) into the terminal.

[0987] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Maintain walking three times a week and add strength training once a week."

[0988] As described above, this invention combines generative AI and gamification elements to support users in efficiently and continuously managing their health. This system makes it possible to provide optimal advice based on an individual's health condition and lifestyle, contributing to the maintenance of health and disease prevention for users.

[0989] The processing flow will be explained below.

[0990] Step 1:

[0991] The user opens their smartphone or PC and enters their personal information (age, gender, height, weight, etc.) and lifestyle data (dietary habits, exercise habits, sleep time, etc.).

[0992] Step 2:

[0993] The terminal validates the entered data, specifically checking the data format and range, and prompting the user to correct the input if necessary.

[0994] Step 3:

[0995] The terminal encrypts the data that has passed validation and sends it securely to the server.

[0996] Step 4:

[0997] The server decrypts the received data and checks whether it has been received correctly, thereby confirming that the data has been delivered to the server correctly.

[0998] Step 5:

[0999] The server normalizes the health data received, unifying the data scale and filling in missing values.

[1000] Step 6:

[1001] The server analyzes the normalized data using a generative AI model, which assesses the user's health status and identifies key health risks.

[1002] Step 7:

[1003] The server generates personalized health advice and health plans based on the analysis results. For example, if a person is deficient in a particular nutrient, it creates a plan that recommends foods containing that nutrient.

[1004] Step 8:

[1005] The server will integrate gamification elements into the generated health plan, such as adding points for completing health tasks and rewards for achieving certain goals.

[1006] Step 9:

[1007] The server transmits the generated advice, health plan, and gamification information to the terminal.

[1008] Step 10:

[1009] The device displays the received information to the user, including next health actions to take, current progress, and earned points.

[1010] Step 11:

[1011] The user acts on the advice provided and then enters the results and additional data (e.g., exercise time and weight fluctuations) back into the device.

[1012] Step 12:

[1013] The terminal encrypts the input feedback data and transmits it again to the server.

[1014] Step 13:

[1015] The server analyzes the received feedback data and generates more accurate advice and health plans based on the analysis results, updating the information as needed.

[1016] Step 14:

[1017] The server sends updated advice and health plans to the terminal, which displays them to the user.

[1018] This is the specific process flow of the HealthMate system, which helps users manage their health efficiently and maintain their motivation.

[1019] Example 1

[1020] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1021] In order to provide personalized health management in response to the increasing number of lifestyle-related diseases and health risks in modern society, it is necessary to provide specific and effective advice based on the user's lifestyle and health condition. However, with conventional health management systems, it is difficult to provide advice and plans that meet individual needs, and continuous feedback and improvement are not provided, resulting in insufficient health management for users.

[1022] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1023] In this invention, the server includes an input means for inputting health data from a user, a transmission means for transmitting the health data, a means for encrypting the transmitted health data and transmitting it to the server, an analysis means using a generative AI model to analyze the transmitted health data, a generation means for generating personalized health advice and a health plan based on the analysis means, an integration means for integrating the health plan with gamification elements, a provision means for providing the generated health advice and plan to the user, a feedback transmission means for receiving feedback data from the user and transmitting the feedback data to the server, and a feedback analysis means for analyzing the feedback and generating updated health advice and plans. This enables the provision of personalized health advice and plans to users, and continuous health management and feedback.

[1024] "Health data" refers to information about a user's health status and lifestyle, such as age, gender, height, weight, diet, exercise habits, and sleep duration.

[1025] "Input means" refers to the device or interface through which users input health data, including terminals such as smartphones and PCs.

[1026] "Transmission means" refers to a function for transmitting input health data to a server.

[1027] "Encryption method" refers to the mechanism used to encrypt data to keep it secure.

[1028] "Server" is a central computer system that receives and analyzes the transmitted data and provides generated health advice and plans.

[1029] A "generative AI model" refers to an artificial intelligence algorithm or program that analyzes and generates advice based on input data.

[1030] "Analysis means" refers to the function of analyzing the transmitted health data using a generative AI model.

[1031] "Generation means" refers to the function of generating personalized health advice and health plans based on the analysis results.

[1032] "Integration means" refers to methods and functions for integrating the generated health plan with gamification elements.

[1033] "Gamification elements" refer to incorporating game elements to increase user motivation, including point awarding and reward provision.

[1034] "Provision means" refers to the function for providing the generated health advice and plans to users.

[1035] "Feedback transmission means" refers to a function for transmitting feedback and additional data from users to the server.

[1036] "Feedback analysis means" refers to a function that analyzes received feedback data and generates updated health advice and plans.

[1037] The present invention is a personal health advisor system that uses generative AI to provide personalized health advice. Specifically, the system allows users to input health data, analyzes the data, and provides personalized health advice and health plans. Detailed embodiments of the system are described below.

[1038] The system includes an input means, a transmission means, an encryption means, a server, a generative AI model, an analysis means, a generation means, an integration means, a provision means, a feedback transmission means, and a feedback analysis means.

[1039] Entering health data

[1040] Users enter health data using a smartphone or PC. The hardware used can be a smartphone, tablet, or desktop PC. A dedicated health management application is provided as software. Users launch this application and enter data on items such as "age," "gender," "height," "weight," "diet," "exercise habits," and "sleep time."

[1041] Data Transmission and Encryption

[1042] The terminal validates the entered data. Validation is a process to check whether the entered data is accurate. For example, it checks that the age is over 0 years old, that the weight is within a reasonable range, etc. If invalid data is detected, an error message is displayed to the user, prompting them to correct it.

[1043] After the data is verified to be accurate, the device encrypts it. The latest encryption algorithm (e.g., AES-256) is used for encryption. The encrypted data is then securely transmitted to the server. The HTTPS protocol is used for data transmission, ensuring data security.

[1044] Analysis by generative AI

[1045] The server decodes the received data and generates a prompt for the generative AI model. The generative AI model used is, for example, OpenAI's GPT-3, and this model is used to analyze the data. An example of a prompt might be, "45-year-old male, inactive, recent meals: toast and coffee for breakfast, salad for lunch, pizza for dinner, gym once a week, 6 hours of sleep. Please generate optimal health advice based on this data."

[1046] The server sends prompts to the generative AI model and receives the generated analysis results and health advice. For example, the model might generate advice such as, "We recommend walking 30 minutes three times a week. In addition, we recommend consuming fish and eggs as you may be deficient in vitamin D."

[1047] Generating and synthesizing personalized advice

[1048] The server then creates a specific health plan based on the analysis results. For example, it could generate a personalized plan such as "a plan for walking 30 minutes every morning," "recommended fish recipes," or "a plan for strength training at the gym."

[1049] In addition, gamification elements will be integrated into the health plan created by the server. For example, 10 points will be awarded for each exercise session, and a reward (such as virtual goods) will be provided once a certain number of points have been accumulated. Specifically, a reward system such as "100 points will earn you a virtual good" will be set up.

[1050] Information provision

[1051] The server encrypts the completed health advice and plan, including gamification elements, and sends it to the device. The device then decrypts the received information and displays it to the user through the application's UI. The display includes a dashboard with information such as "Next action: Walk 30 minutes every morning" and "Current score: 40 / 100."

[1052] Receiving and analyzing feedback

[1053] The user again inputs their daily exercise results, dietary habits, weight fluctuations, etc. into the application. For example, they input feedback data such as "Today's walking time: 30 minutes, walking distance: 3 km." The device encrypts this feedback data and sends it to the server.

[1054] The server decodes and analyzes the feedback data. Based on the analysis, it generates new personalized health advice and an improved health plan. For example, new advice might be provided, such as "Maintain your walking frequency and add one strength training session per week."

[1055] Through the above process, personalized health advice and plans are provided, enabling the user to efficiently and continuously manage their health.

[1056] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1057] Step 1:

[1058] The user enters health data using a smartphone or PC. The user launches a dedicated application and enters health information such as age, gender, height, weight, diet, exercise habits, and sleep duration into an input form. Input is done through a form on the application. The entered data is validated in the next step. Examples of input data include "Age: 45 years old," "Gender: Male," "Height: 175 cm," "Weight: 80 kg," "Diet: Breakfast - toast and coffee, lunch - salad, dinner - pizza," "Exercise habits: Gym once a week," and "Sleep time: 6 hours."

[1059] Step 2:

[1060] The terminal validates the health data entered. Validation is the process of making sure the entered data is accurate. For example, it checks that the age is over 0 years old and that the weight is within an appropriate range. It separates normal data from data with errors, and displays an error message to the user if any invalid data is found. An example of an error message might be "Your age is invalid. Please enter it again." Once the data has been validated, it moves on to the next step.

[1061] Step 3:

[1062] The terminal encrypts the data that passes validation and sends it securely to the server. The encryption algorithm used is AES-256, which increases the security of the data. The encrypted data is sent to the server using the HTTPS protocol. The format of the data sent is encrypted binary data.

[1063] Step 4:

[1064] The server decodes the received data and generates a prompt for the generative AI model. An example of a prompt might be, "45-year-old male, inactive, recent meals: toast and coffee for breakfast, salad for lunch, pizza for dinner. Goes to the gym once a week, sleeps 6 hours. Please generate optimal health advice based on this data." OpenAI's GPT-3 is used as an example of a generative AI model. The server sends the prompt to the generative AI model and receives the analysis results.

[1065] Step 5:

[1066] The server analyzes the analysis results received from the AI ​​model and generates optimal health advice and health plans for the user. For example, specific advice may be generated such as "We recommend walking 30 minutes three times a week. In addition, we recommend consuming fish and eggs as you may be deficient in vitamin D." The generated advice is integrated into gamification elements in the next step.

[1067] Step 6:

[1068] The server integrates gamification elements into the generated health plan. The gamification elements include a system that awards points for each exercise session and provides rewards when a certain number of points are accumulated. Specifically, this includes elements such as "receive virtual goods for every 10 points" and "receive a gift card for 100 points." The integrated data then proceeds to the next step.

[1069] Step 7:

[1070] The server encrypts the generated health advice and gamification plan and sends it to the device. The encryption algorithm used is AES-256, and the transmission protocol is HTTPS. The transmitted data is in encrypted binary data format.

[1071] Step 8:

[1072] The device decodes the received data and displays it to the user. Through the UI of the dedicated application, information such as "Next action: Walk 30 minutes every morning" and "Current points: 40 / 100" is displayed on the dashboard.

[1073] Step 9:

[1074] The user re-enters feedback data such as daily exercise results, dietary habits, and weight fluctuations into the dedicated application. For example, they enter data such as "Today's walking time: 30 minutes, walking distance: 3km." The entered feedback data then proceeds to the next step.

[1075] Step 10:

[1076] The terminal validates the feedback data, encrypts it, and sends it to the server. Validation is the process of confirming the accuracy of the input data. The encryption method is AES-256, and the transmission protocol is HTTPS.

[1077] Step 11:

[1078] The server decodes and analyzes the feedback data. A generative AI model is used to generate new health advice based on the feedback data. For example, new advice might be provided, such as "Maintain your walking frequency and add one strength training session per week."

[1079] Through the above process, the user can receive personalized health advice and continuously manage their health.

[1080] (Application example 1)

[1081] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1082] Conventional health management systems have the problem of making it difficult for users to voluntarily input data and provide continuous feedback. Furthermore, while they often provide personalized health plans and advice, they often lack the motivation to encourage users to follow these plans. In particular, there is a demand for a system that enables health management linked to activities in physical stores. Furthermore, when using the system in physical stores, ease of use of the device and secure handling of data are key issues.

[1083] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1084] In this invention, the server includes: an input means for inputting health data from a user; a transmission means for transmitting the health data to the server; an analysis means using a generative AI model to analyze the transmitted health data; a generation means for generating personalized health advice and a health plan based on the analysis means; an integration means for incorporating the health plan into gamification elements; a provision means for providing the generated health advice and plan to the user; a feedback transmission means for receiving feedback from the user and transmitting the feedback data to the server; a feedback analysis means for analyzing the feedback and generating updated health advice and a plan; a physical store integration means for allowing users to input health data using a terminal in a physical store and providing personalized health plans and gamification elements based on the analysis results at the physical store; and a feedback receiving means for inputting the results of implementing the health plan as feedback to the terminal and transmitting the feedback to the server. This enables each user to receive appropriate advice and a health management plan, facilitate their implementation, and provide the results as feedback, thereby enabling more accurate health management.

[1085] "User" refers to an individual who uses the Personal Health Advisor System.

[1086] "Health data" refers to information related to a user's health status and lifestyle, such as their age, gender, height, weight, exercise habits, diet, and sleep duration.

[1087] "Input means" refers to the interface through which users input health data using a smartphone, tablet, PC, etc.

[1088] "Transmission means" refers to the means for encrypting the entered health data and transmitting it securely to the server.

[1089] "Generative AI model" refers to an artificial intelligence model that analyzes health data and generates personalized health advice and health plans.

[1090] "Analysis Means" refers to the means for analyzing health data using a generative AI model.

[1091] "Generation means" refers to means for generating personalized health advice and health plans based on the analysis results by the analysis means.

[1092] "Integration means" refers to means for incorporating the generated health plan into gamification elements.

[1093] "Delivery means" refers to the means for providing the generated health advice and plan to the user.

[1094] "Feedback sending means" refers to means for receiving feedback from a user and sending feedback data to a server.

[1095] "Feedback analysis means" refers to means for analyzing received feedback and generating updated health advice and plans.

[1096] "Physical store integration means" refers to a means by which users can input health data using a terminal in a physical store and receive personalized health plans and gamification elements based on the analysis results in the physical store.

[1097] The "feedback receiving means" refers to a means for inputting the results of the health plan implementation into the terminal as feedback again and transmitting it to the server.

[1098] This invention is a personal health advisor system that utilizes generative AI, specifically designed for use in brick-and-mortar stores. This system inputs users' health data via terminals installed in the brick-and-mortar stores, and based on that data, provides personalized health advice and promotes continuous health management through gamification elements.

[1099] System configuration

[1100] 1. Input Method

[1101] Users enter their health data (age, gender, height, weight, exercise habits, eating habits) using devices such as smartphones, tablets, and PCs installed in physical stores.

[1102] 2. Transmission Method

[1103] The device validates the entered health data to ensure it is accurate, then encrypts it and securely transmits it to the server, using the HTTPS protocol for this process.

[1104] 3. Analysis Methods Using Generative AI Models

[1105] The server decodes the received health data and analyzes it using a generative AI model (e.g., a model using TensorFlow or PyTorch). This analysis evaluates the user's current health status and health risks.

[1106] 4. Means of generating personalized advice and health plans

[1107] The server then generates optimal health advice and plans for each user based on the analysis results. For example, if a user is deficient in a particular nutrient, a plan recommending foods containing that nutrient will be generated.

[1108] 5. Means of integration with gamification elements

[1109] The server integrates the generated health plan with gamification elements, such as a system that awards points for each exercise session and offers products or services from a store when a certain number of points are accumulated.

[1110] 6. Means of providing information

[1111] The server sends the generated advice, plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user, such as the next health action to be taken and the user's current progress.

[1112] 7. Feedback Submission Methods

[1113] The user acts according to the advice and then inputs the results and additional data (e.g., weight fluctuations and number of exercise sessions) into the device again. The device then encrypts the input feedback data and sends it to the server.

[1114] 8. Feedback Analysis Methods

[1115] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data.

[1116] System operation example

[1117] A case of a middle-aged man aiming to maintain his health

[1118] 1. User A (male, 45 years old) uses a terminal at a physical store to enter his recent meal details (breakfast: toast and coffee, lunch: salad, dinner: pizza), exercise habits (gym once a week), and sleep time (6 hours).

[1119] 2. The device validates this data and sends it to the server.

[1120] 3. The server analyzes the data and determines whether you are not getting enough exercise.

[1121] 4. The server generates a personalized health plan, such as "recommended 30 minutes of walking three times a week."

[1122] 5. Add a gamification element by awarding 10 points for each walk the server completes, and once they have accumulated 100 points they can receive a reward from the store (e.g. a free training session).

[1123] 6. The server sends this information to the terminal, which displays it to User A.

[1124] 7. User A walks and enters the results (e.g., walking time and distance) into the terminal.

[1125] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Maintain walking three times a week and add strength training once a week."

[1126] Prompt Sentence Examples

[1127] 1. "45 years old, male, height 175 cm, weight 85 kg, exercises once a week."

[1128] 2. "Eating habits: Breakfast (toast and coffee), lunch (salad), dinner (pizza). Added exercise time to feedback."

[1129] As described above, the present invention is a system that supports efficient and continuous health management in physical stores, and specifically includes the provision of personalized health advice using generative AI models and the integration of gamification elements.

[1130] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1131] Step 1:

[1132] Users enter their health data (age, gender, height, weight, diet, exercise habits, and sleep time) using a terminal at a physical store.

[1133] Input: User's health data

[1134] Output: Health data stored on the device

[1135] Step 2:

[1136] The terminal validates the entered data to ensure it is accurate, then encrypts it and securely transmits it to the server.

[1137] Input: Health data stored on the device

[1138] Output: Encrypted health data is sent to the server.

[1139] Step 3:

[1140] The server decodes the received data and analyzes it using a generative AI model, which assesses the user's health status and health risks.

[1141] Input: Encrypted health data

[1142] Output: Analysis results (user's health status and health risks)

[1143] Step 4:

[1144] Based on the analysis results, the server generates personalized health advice and plans that are best suited to the user, such as "walking three times a week is recommended."

[1145] Input: Analysis results

[1146] Output: Personalized health advice and health plans

[1147] Step 5:

[1148] The server then integrates the generated health plan with gamification elements, such as rewarding points for each exercise session and allowing users to receive special rewards at physical stores once they have accumulated a certain number of points.

[1149] Input: personalized health advice and health plans

[1150] Output: A health plan with integrated gamification elements

[1151] Step 6:

[1152] The server sends the generated advice, plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user, such as the next health action to be taken and the current progress.

[1153] Input: Health plans with integrated gamification elements

[1154] Output: Health advice and plan displayed on the device

[1155] Step 7:

[1156] The user follows the advice and then enters the results and additional data (e.g., walking time and distance) back into the device.

[1157] Input: User action results and additional data

[1158] Output: Feedback data stored on the device

[1159] Step 8:

[1160] The terminal encrypts the input feedback data and transmits it to the server.

[1161] Input: Feedback data

[1162] Output: Encrypted feedback data is sent to the server

[1163] Step 9:

[1164] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data. For example, the next advice provided might be, "Maintain walking three times a week and add strength training once a week."

[1165] Input: Feedback data

[1166] Output: The new personalized advice and health plan is sent to the terminal.

[1167] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1168] This invention combines an emotion engine with a personal health advisor system that utilizes generative AI to provide more adaptive and personalized health advice and health plans. This system analyzes the user's health data and emotional state, and generates and provides optimal advice based on that data.

[1169] System Overview

[1170] 1. Entering health and emotional data

[1171] Users use input devices (smartphones or PCs) to input personal information, lifestyle data, and emotional state data. Emotional data is acquired through questionnaires, facial recognition cameras, voice assistants, etc.

[1172] 2. Data transmission

[1173] The device validates the entered health and emotion data, checking the data format and range, and prompting the user to make corrections if necessary.

[1174] The terminal encrypts the data that has passed validation and sends it securely to the server.

[1175] 3. Analysis using generative AI and emotion engine

[1176] The server decodes and normalizes the received data and analyzes it using a generative AI model and an emotion engine. The generative AI model analyzes health data, and the emotion engine analyzes emotion data.

[1177] The server integrates the analysis results of the emotion engine with the generative AI model to comprehensively evaluate the user's health and emotional state.

[1178] 4. Generating personalized advice

[1179] Based on the analysis results, the server generates optimal health advice and health plans for the user. For example, if the user is under high stress, a relaxation plan will be provided.

[1180] The server adjusts the generated health plan based on your emotional state, recommending a more aggressive exercise plan when you're feeling energized and a lighter exercise plan when you're feeling down.

[1181] 5. Gamification Integration

[1182] The server integrates gamification elements into the generated health plan, specifically adding a points system and virtual rewards mechanism.

[1183] 6. Information provision

[1184] The server sends the generated advice, health plan, and gamification information to the device.

[1185] The device displays this information to the user in an easy-to-understand manner, and measures such as changing the message depending on the user's emotional state are taken into consideration.

[1186] 7. Receiving and Analyzing Feedback

[1187] The user acts according to the advice and inputs the results into the terminal again, along with their new emotional state.

[1188] The terminal encrypts the input feedback data and emotion data and transmits them again to the server.

[1189] The server analyzes the feedback data and generates more accurate advice and health plans.

[1190] Specific examples

[1191] Case 1: Middle-aged women seeking health management

[1192] 1. User B (50 years old, female) uses her smartphone to enter her personal information, recent dietary habits, exercise habits, sleep time, and emotional state ("I'm feeling a little stressed today").

[1193] 2. The device validates this data and sends it to the server.

[1194] 3. The server analyzes the data and determines that the stress level is high.

[1195] 4. The server generates a personalized health plan, such as "Light yoga recommended three times a week to reduce stress."

[1196] 5. Add a gamification element where the server awards 10 points for each yoga session, and when you collect 100 points you can receive a virtual prize.

[1197] 6. The server sends this information to the device, which displays it to User B. An encouraging message tailored to User B's emotional state is also displayed.

[1198] 7. User B practices yoga and inputs the results (for example, yoga time and mood changes) into the terminal.

[1199] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Continue doing yoga twice a week and add walking once a week."

[1200] In this way, combining the emotion engine enables optimal health management according to the user's emotional state. This allows users to receive health management tailored to their emotions, making it easier for them to maintain motivation.

[1201] The processing flow will be explained below.

[1202] Step 1:

[1203] Users open their smartphones or PCs and enter personal information (age, gender, height, weight, etc.) and lifestyle data (dietary habits, exercise habits, sleep time, etc.). They also enter data on their emotional state. This emotional data is acquired through questionnaires, facial recognition, voice input, etc.

[1204] Step 2:

[1205] The terminal validates the entered data. Specifically, it checks the data format and range, and prompts the user to correct the entered data if necessary. For example, if the weight entered is out of range, the user is asked to re-enter it.

[1206] Step 3:

[1207] The device encrypts the health data and emotion data that have passed validation and transmits them securely to the server.

[1208] Step 4:

[1209] The server decrypts the received data and verifies that it was received correctly, thereby ensuring that the data has not been corrupted.

[1210] Step 5:

[1211] The health and emotion data received by the server is normalized, the data scale is unified, and missing values ​​are filled in. In this process, the data format is unified, making analysis easier.

[1212] Step 6:

[1213] The server analyzes the normalized health data using a generative AI model, which evaluates the user's health status and identifies health risks and areas for improvement. At the same time, it analyzes the emotional data using an emotion engine to evaluate the user's emotional state.

[1214] Step 7:

[1215] The server combines the results of the generative AI model and the emotion engine to perform a comprehensive evaluation, which generates advice and plans that take into account not only the user's health condition but also their emotional state.

[1216] Step 8:

[1217] The server generates personalized health advice and a health plan based on the analysis results. For example, if a user is gaining weight but experiencing high stress, it will suggest relaxation and light exercise focused on stress reduction.

[1218] Step 9:

[1219] The server integrates gamification elements into the generated health plan, specifically by awarding points for completing each health task and providing rewards for achieving certain goals.

[1220] Step 10:

[1221] The server sends the generated advice, health plan, and gamification information to the device.

[1222] Step 11:

[1223] The device then displays the information it receives to the user, including next health actions, current progress, points earned, and messages based on emotional state.

[1224] Step 12:

[1225] The user acts on the presented advice and inputs the results of the action and the new emotional state into the terminal, including feedback such as "Today's walk felt good."

[1226] Step 13:

[1227] The terminal encrypts the input feedback data and emotion data and transmits them again to the server.

[1228] Step 14:

[1229] The server analyzes the received feedback and emotion data, which is then used to generate more accurate advice and health plans using a generative AI model and emotion engine.

[1230] Step 15:

[1231] The server sends updated advice and health plans to the device, which then displays them to the user, allowing the user to continuously manage their health.

[1232] This is the specific processing flow of the HealthMate system, which combines an emotion engine. This system enables personalized health management that adapts not only to the user's health condition but also to their emotional state.

[1233] Example 2

[1234] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1235] Conventional health management systems only provide general advice based on users' health data and are unable to provide personalized health advice that takes into account the user's emotional state. Furthermore, they lacked gamification elements to maintain motivation, making it difficult to continue long-term health management. This prevented users from receiving effective health management tailored to their emotional state, reducing the efficiency of health improvement.

[1236] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1237] In this invention, the server includes: an input means for inputting health data and emotional data from a user; a transmission means for transmitting the health data and emotional data to the server; a validation means including a terminal for validating and encrypting the transmitted health data and emotional data; an analysis means for decrypting and normalizing the encrypted health data and emotional data and analyzing them using a generative AI model and an emotion engine; a generation means for generating personalized health advice and a health plan based on the analysis means; an adjustment means for adjusting the generated health plan based on the user's emotional state; an integration means for incorporating gamification elements into the health plan; a provision means for providing the generated health advice and plan to the user; a feedback transmission means for receiving feedback from the user and transmitting the feedback data and emotional data to the server; and a feedback analysis means for analyzing the feedback and generating updated health advice and a health plan. This allows for the provision of personalized health advice and a health plan that takes the user's emotional state into consideration. Furthermore, incorporating gamification elements can maintain user motivation and encourage continued health management.

[1238] "Health data" refers to information such as a user's personal information, lifestyle data, diet, exercise habits, and sleep time.

[1239] "Emotional data" refers to data that represents a user's emotional state, and refers to information obtained through questionnaires, facial recognition cameras, voice assistants, etc.

[1240] "Input means" refers to a device or interface that allows a user to input health and emotional data into the system.

[1241] "Transmission means" refers to the functions and processes for transmitting the input health data and emotion data to the server.

[1242] "Validation measures" refer to functions that check the format and scope of health data and emotional data and prompt corrections if necessary.

[1243] "Encryption" refers to the process of using cryptography to protect health and emotional data in order to securely transmit it to a server.

[1244] "Decryption and Normalization" refers to the process by which the server decrypts encrypted data and converts it into a format suitable for the system.

[1245] "Generative AI model" refers to an artificial intelligence model that analyzes health data and generates health advice and health plans appropriate for users.

[1246] "Emotion engine" refers to a computer program that analyzes emotion data and assesses a user's emotional state.

[1247] "Analysis Means" refers to the process of analyzing received data using the generative AI model and emotion engine.

[1248] "Generation means" refers to a function that generates personalized health advice and health plans based on the analysis results.

[1249] "Adjustment means" refers to a function that adjusts the generated health plan based on the user's emotional state.

[1250] "Integration measures" refers to the ability to incorporate gamification elements into health plans.

[1251] "Gamification elements" refers to a system that incorporates game elements such as a point system and virtual rewards to increase user motivation.

[1252] "Delivery means" refers to the process by which the generated health advice and health plans are provided to the user.

[1253] "Feedback transmission means" refers to a function for transmitting feedback data and new emotion data from the user to the server.

[1254] "Feedback analysis means" refers to the process that analyzes received feedback data and generates updated health advice and health plans.

[1255] This invention combines an emotion engine with a personal health advisor system that utilizes generative AI to provide more adaptive and personalized health advice and health plans. This system analyzes the user's health data and emotional state, and generates and provides optimal advice based on that data.

[1256] System Overview

[1257] Entering health and emotional data

[1258] A user launches an application using a device such as a smartphone or PC. The user enters personal information (age, gender, etc.), lifestyle data (dietary habits, exercise habits, sleep duration, etc.), and emotional state (e.g., "I feel a little stressed today"). Emotional data is obtained using questionnaires, facial recognition cameras, voice assistants, etc.

[1259] Sending data

[1260] The terminal validates the entered data, checking the data format and range and prompting the user to make corrections if necessary. After that, the terminal encrypts the data that passes validation using AES encryption technology and sends it securely to the server.

[1261] Analysis using generative AI and emotion engine

[1262] The server decodes and normalizes the received data, analyzes the health data using a generative AI model, and simultaneously analyzes the emotional data using an emotion engine. The server then integrates the results of the generative AI model and the emotion engine to comprehensively evaluate the user's health and emotional state.

[1263] Generating personalized advice

[1264] Based on the analysis results, the server generates optimal health advice and health plans for users. For example, if a user is under high stress, a relaxation plan is provided, and if the user is feeling well, a more active exercise plan is recommended.

[1265] Gamification Integration

[1266] The server integrates gamification elements into the generated health plans, specifically by adding a points system and virtual rewards mechanism to increase motivation.

[1267] Information provision

[1268] The server sends the generated advice, health plan, and gamification information to the device, which then displays this information in an easy-to-understand manner for the user. For example, the device may change the message depending on the user's emotional state.

[1269] Receiving and analyzing feedback

[1270] The user acts on the advice and then inputs the results and new emotional state back into the device. The device then sends this feedback data back to the server, which analyzes the feedback data and generates more accurate advice and health plans. This process continuously optimizes the user's health management.

[1271] Specific examples

[1272] Case 1: Middle-aged women seeking health management

[1273] 1. User B (50 years old, female) uses her smartphone to enter her personal information, recent dietary habits, exercise habits, sleep time, and emotional state ("I'm feeling a little stressed today").

[1274] 2. The device validates this data and sends it to the server.

[1275] 3. The server analyzes the data and determines that the stress level is high.

[1276] 4. The server generates a personalized health plan, such as "Light yoga recommended three times a week to reduce stress."

[1277] 5. Add a gamification element where the server awards 10 points for each yoga session, and when you collect 100 points you can receive a virtual prize.

[1278] 6. The server sends this information to the device, which displays it to the user, along with encouraging messages tailored to the user's emotional state.

[1279] 7. User B practices yoga and again inputs the results (for example, yoga time and mood changes) into the terminal.

[1280] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Continue doing yoga twice a week and add walking once a week."

[1281] By combining this system with an emotion engine, it is possible to provide optimal health management according to the user's emotional state. This allows users to receive health management tailored to their emotions, making it easier for them to maintain motivation.

[1282] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1283] Step 1:

[1284] A user starts the application using a smartphone or PC. The user inputs personal information (e.g., age, gender), lifestyle data (e.g., dietary habits, exercise habits, sleep duration), and emotional state (e.g., "I feel a little stressed today"). The input data is saved in the application.

[1285] Input: Personal information, lifestyle data, emotional state

[1286] Output: Saving the entered data within the application

[1287] Specific behavior: The user enters data into the input form and clicks the submit button.

[1288] Step 2:

[1289] The device validates the entered health and emotion data. It checks the data format (for example, age is a number, gender is a string) and range (for example, sleep time is 0-24 hours), and asks the user to make corrections if necessary. Data that passes validation is encrypted in the next step.

[1290] Input: User-entered health and emotion data

[1291] Output: Data that has passed validation, and a request for correction if necessary

[1292] Specific operation: The terminal checks the data format and value range, and if an error message is displayed, prompts you to correct it.

[1293] Step 3:

[1294] The terminal encrypts the data that has passed validation using AES encryption technology, and the encrypted data is sent to the server.

[1295] Input: Data that passes validation

[1296] Output: Encrypted data, sent to server

[1297] Specific operation: The terminal encrypts the data using the AES algorithm and sends the encrypted data to the server over the network.

[1298] Step 4:

[1299] The server decrypts the received encrypted data and normalizes it, for example, standardizing date formats. The health data is then analyzed using a generative AI model. At the same time, the emotion engine analyzes the emotion data. The analysis results are integrated into an overall assessment.

[1300] Input: Encrypted data

[1301] Output: Decoded and normalized data, analysis results of the generative AI model, analysis results of the emotion engine, and overall evaluation

[1302] Specific operation: The server decrypts the encrypted data, unifies the data format, and applies the generative AI model and emotion engine to generate analysis results.

[1303] Step 5:

[1304] The server generates personalized health advice and health plans based on the overall assessment. For example, if the user is under high stress, a yoga plan may be generated. The generated health plan is tailored to the user's emotional state.

[1305] Input: Overall rating

[1306] Output: Personalized health advice and health plans

[1307] Specific operation: The server applies an algorithm based on the overall evaluation results to generate appropriate health advice and plans.

[1308] Step 6:

[1309] The server integrates gamification elements into the generated health plans, specifically adding a points system and virtual rewards mechanism.

[1310] Input: personalized health advice and health plans

[1311] Output: A health plan with integrated gamification elements

[1312] Specific operation: The server incorporates a points system and achievement rewards into the health plan.

[1313] Step 7:

[1314] The server sends the generated health advice, health plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user. It also displays messages based on the user's emotional state.

[1315] Input: Health plans with integrated gamification elements

[1316] Output: Displayed health advice and health plan

[1317] Specific operation: The server sends data to the device, and the device conveys the information to the user via push notification or in-app display.

[1318] Step 8:

[1319] The user acts according to the advice and then inputs the results and emotional state back into the device, which then re-encrypts this feedback data and sends it to the server.

[1320] Input: Feedback data and emotion data from users

[1321] Output: Encrypted feedback data, sent to server

[1322] Specific operation: The user fills out the feedback form and clicks the submit button. The device encrypts the data and sends it to the server.

[1323] Step 9:

[1324] The server analyzes the feedback data and generates new health advice and health plans, and further refines the health plans based on the analysis results.

[1325] Input: Encrypted feedback data

[1326] Output: Updated health advice and health plan

[1327] What happens: The server decodes the feedback data and applies analysis algorithms to generate new advice and plans.

[1328] (Application example 2)

[1329] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1330] In modern society, people often find it difficult to balance health management with their emotions. As lifestyles become more diverse and individuals' health and emotional states fluctuate daily, standardized health advice and plans are insufficient. Other issues include a lack of motivation and health advice tailored to individual emotional states. Therefore, there is a need for a system that provides adaptive and personalized health plans and advice based on each user's health and emotional data.

[1331] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for inputting health data and emotional data from the user, a transmission means for transmitting the health data and emotional data to the server, an analysis means using a generative AI model and an emotion engine to analyze the transmitted health data and emotional data, a generation means for generating personalized health advice and a health plan based on the analysis means, an integration means for incorporating the health plan into gamification elements, a provision means for providing the generated health advice and plan to the user, a feedback transmission means for receiving feedback from the user and transmitting the feedback data to the server, and a feedback analysis means for analyzing the feedback and generating updated health advice and a plan. This makes it possible to adaptively provide optimal health plans and advice based on the user's health and emotional state. Furthermore, the gamification elements can improve the user's motivation and support continuous health management.

[1332] "User" refers to an individual who uses the system.

[1333] "Health data" refers to information about an individual's health status, including, for example, dietary habits, exercise habits, and sleep duration.

[1334] "Emotional data" is information about an individual's emotional state, collected through surveys, facial recognition cameras, and voice assistants.

[1335] A "generative AI model" is an artificial intelligence model that analyzes a user's health data and generates personalized health plans and advice.

[1336] The "emotion engine" is an engine for analyzing emotion data and evaluating the user's emotional state.

[1337] "Gamification elements" are systems that incorporate game elements to increase user engagement, and examples include point systems and virtual rewards.

[1338] "Input means" refers to the means by which users input health data and emotional data into the system, and includes smartphones and PCs.

[1339] The "transmission means" is a means for transmitting the input health data and emotion data to the server.

[1340] "Analysis Means" means means for analyzing health data and emotion data using a generative AI model and emotion engine.

[1341] "Generation means" refers to means for generating personalized health advice and health plans based on the analysis results.

[1342] "Integration means" refers to means for integrating the generated health plan with gamification elements.

[1343] The "provision means" is a means for providing the generated health advice and plan to the user.

[1344] The "feedback sending means" is a means for receiving feedback from a user and sending it to the server.

[1345] "Feedback analysis means" means for analyzing received feedback and generating updated health advice and plans.

[1346] System Overview

[1347] The system according to the present invention is a system for providing personalized health advice and health plans using health and emotional data of a user. Specifically, the system includes the following elements:

[1348] 1. Input means for inputting health data and emotion data from the user

[1349] Users use their smartphones or PCs to enter personal information, lifestyle data, recent dietary habits, exercise habits, sleep time, emotional state, etc. Emotional data is collected through questionnaires, facial recognition cameras, and voice assistants.

[1350] 2. Means for transmitting health data and emotion data to the server

[1351] The device validates the entered health and emotion data, encrypts it, and securely transmits it to the server.

[1352] 3. Analysis methods using generative AI models and emotion engines to analyze data

[1353] The server decodes and normalizes the received data and analyzes the health data using a generative AI model. At the same time, it analyzes the emotion data using an emotion engine. The generative AI model evaluates the user's health status, and the emotion engine evaluates their emotional state.

[1354] 4. Means for generating personalized health advice and health plans

[1355] Based on the analysis results, the server generates optimal health advice and health plans for the user. For example, if the user is experiencing high stress, a relaxation plan will be provided.

[1356] 5. Integrating health plans into gamification elements

[1357] The server integrates gamification elements into the generated health plan, specifically adding a points system and virtual rewards mechanism.

[1358] 6. Means for providing generated health advice and plans to users

[1359] The server transmits the generated advice, health plan, and gamification information to the terminal, which then displays this information to the user.

[1360] 7. Feedback sending means for receiving feedback and sending feedback data to the server

[1361] The user then inputs the results of their actions based on the advice into the terminal, including their new emotional state. The terminal then transmits this feedback data and emotional data to the server.

[1362] 8. Feedback analysis means for analyzing the feedback and generating updated health advice and plans.

[1363] The server analyzes the feedback data and generates new health advice and plans, which allows for more accurate advice and health plans to be provided to the user.

[1364] Hardware and software used

[1365] Hardware:

[1366] Smartphone, PC: Used to enter and display user data and receive feedback.

[1367] software:

[1368] Cloud server: Used to analyze data and generate advice.

[1369] Generative AI models: Analyze health data and generate personalized advice.

[1370] Emotion engine: Analyzes emotional data and generates appropriate messages based on emotional state.

[1371] Specific examples

[1372] User A opens his smartphone and inputs his personal information, recent dietary habits, exercise habits, and sleep time, as well as his emotional state, such as "I feel a little stressed today." This data is sent to the server and analyzed by a generative AI model and emotion engine. The server generates a personalized plan for stress reduction, such as "Light yoga recommended three times a week," and adds a message based on the user's emotional state, such as "Relax today." This information is then displayed on the smartphone. When the user practices yoga, points are awarded, and when a certain number of points are reached, virtual rewards are earned.

[1373] Prompt Sentence Examples

[1374] Age: 30

[1375] Gender: Male

[1376] Recent meal: Chicken, salad, pasta

[1377] Exercise habits: Running twice a month

[1378] Sleep time: 7 hours

[1379] Emotional state: Very tired

[1380] In this way, it is possible to provide users with appropriate health plans and emotional support tailored to their specific circumstances.

[1381] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1382] Step 1:

[1383] Users use a smartphone or PC to input personal information, recent dietary habits, exercise habits, sleep duration, and emotional state (e.g., "I feel a little stressed today") using a dedicated application. This input method generates input data.

[1384] Step 2:

[1385] The device validates the entered health and emotion data. For example, it checks the data format and range, and prompts the user to make corrections if necessary. Once validated, the data is encrypted and sent to the cloud server. Encryption is performed using technologies such as SSL / TLS.

[1386] Step 3:

[1387] The server decrypts the received encrypted data and normalizes it. This data includes personal information, health data, and emotional data. The server then analyzes the health data using a generative AI model to generate a personalized health plan. At the same time, it analyzes the emotional data using an emotional engine to generate appropriate emotional messages.

[1388] Step 4:

[1389] The server combines the analysis results of the generative AI model and the emotion engine to generate optimal health advice and plans for users. For example, if a user is in a high stress state, the server generates a plan such as "recommended light yoga three times a week to reduce stress."

[1390] Step 5:

[1391] The server then integrates the generated health plan with gamification elements, such as a points system and virtual rewards system. For example, points are awarded for each yoga session, and virtual goods can be awarded once a certain number of points are accumulated.

[1392] Step 6:

[1393] The server sends the generated health plan, advice, and gamification information to the end user's device, which displays the received information in an easy-to-understand format, including encouraging messages based on the user's emotional state.

[1394] Step 7:

[1395] The user acts according to the advice and health plan provided and inputs the results as feedback into the device, including the time spent doing yoga and changes in mood during that time.

[1396] Step 8:

[1397] The terminal again transmits the feedback data and new emotion data to the server, which is then encrypted before transmission.

[1398] Step 9:

[1399] The server analyzes the feedback data and generates new health advice and health plans based on the most recent data, thereby providing more accurate advice to the user.

[1400] Through these steps, the system can adaptively provide optimal health plans and advice according to the user's health and emotional state. Furthermore, the gamification element can increase user motivation and support continuous health management.

[1401] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1402] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1403] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1404] [Fourth embodiment]

[1405] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1406] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1407] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1408] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1409] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1410] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1411] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1412] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1413] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1414] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1415] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1416] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1417] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1418] The present invention is a personal health advisor system that utilizes generative AI, specifically, a system that allows users to input health data, analyzes the data, and provides personalized health advice and health plans. The system includes the following major elements: input means, transmission means, analysis means, generation means, integration means, provision means, feedback transmission means, and feedback analysis means.

[1419] System Overview

[1420] 1. Enter your health data

[1421] Users use input methods (such as smartphones or PCs) to enter personal information and lifestyle data, such as age, gender, height, weight, diet, exercise habits, and sleep duration.

[1422] 2. Data transmission

[1423] The terminal validates the entered data to ensure it is accurate.

[1424] The device then encrypts the data and sends it securely to the server.

[1425] 3. Analysis by generative AI

[1426] The server decodes the received data and analyzes it using the generative AI model. The analysis method evaluates the user's health status and identifies individual health risks and necessary measures.

[1427] 4. Generating personalized advice

[1428] The server generates optimal health advice and health plans for users based on the analysis results. For example, if a user is deficient in a particular nutrient, a plan recommending foods containing that nutrient will be generated.

[1429] 5. Gamification Integration

[1430] The server then integrates the generated health plan with gamification elements, such as rewards once a certain number of points are accumulated.

[1431] 6. Information provision

[1432] The server transmits the generated advice and health plan, as well as gamification information, to the terminal.

[1433] The device displays this information to the user in an easy-to-understand manner, for example showing the next health action to be taken and the current progress.

[1434] 7. Receiving and Analyzing Feedback

[1435] The user acts on the advice and then enters the results and additional data (e.g., weight fluctuations and number of exercises) back into the device.

[1436] The terminal encrypts the input feedback data and transmits it to the server.

[1437] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data.

[1438] Specific examples

[1439] Case 1: Middle-aged men seeking to maintain their health

[1440] 1. User A (45 years old, male) uses his smartphone to enter his recent meal history (breakfast: toast and coffee, lunch: salad, dinner: pizza), exercise habits (gym once a week), and sleep time (6 hours).

[1441] 2. The device validates this data and sends it to the server.

[1442] 3. The server analyzes the data and determines whether you are not getting enough exercise.

[1443] 4. The server generates a personalized health plan, such as "recommended 30 minutes of walking three times a week."

[1444] 5. Add a gamification element where the server awards 10 points for each walk and when you collect 100 points you can receive virtual goods.

[1445] 6. The server sends this information to the terminal, which displays it to User A.

[1446] 7. User A walks and inputs the results (e.g., walking time and distance) into the terminal.

[1447] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Maintain walking three times a week and add strength training once a week."

[1448] As described above, this invention combines generative AI and gamification elements to support users in efficiently and continuously managing their health. This system makes it possible to provide optimal advice based on an individual's health condition and lifestyle, contributing to the maintenance of health and disease prevention for users.

[1449] The processing flow will be explained below.

[1450] Step 1:

[1451] The user opens their smartphone or PC and enters their personal information (age, gender, height, weight, etc.) and lifestyle data (dietary habits, exercise habits, sleep time, etc.).

[1452] Step 2:

[1453] The terminal validates the entered data, specifically checking the data format and range, and prompting the user to correct the input if necessary.

[1454] Step 3:

[1455] The terminal encrypts the data that has passed validation and sends it securely to the server.

[1456] Step 4:

[1457] The server decrypts the received data and checks whether it has been received correctly, thereby confirming that the data has been delivered to the server correctly.

[1458] Step 5:

[1459] The server normalizes the health data received, unifying the data scale and filling in missing values.

[1460] Step 6:

[1461] The server analyzes the normalized data using a generative AI model, which assesses the user's health status and identifies key health risks.

[1462] Step 7:

[1463] The server generates personalized health advice and health plans based on the analysis results. For example, if a person is deficient in a particular nutrient, it creates a plan that recommends foods containing that nutrient.

[1464] Step 8:

[1465] The server will integrate gamification elements into the generated health plan, such as adding points for completing health tasks and rewards for achieving certain goals.

[1466] Step 9:

[1467] The server transmits the generated advice, health plan, and gamification information to the terminal.

[1468] Step 10:

[1469] The device displays the received information to the user, including next health actions to take, current progress, and earned points.

[1470] Step 11:

[1471] The user acts on the advice provided and then enters the results and additional data (e.g., exercise time and weight fluctuations) back into the device.

[1472] Step 12:

[1473] The terminal encrypts the input feedback data and transmits it again to the server.

[1474] Step 13:

[1475] The server analyzes the received feedback data and generates more accurate advice and health plans based on the analysis results, updating the information as needed.

[1476] Step 14:

[1477] The server sends updated advice and health plans to the terminal, which displays them to the user.

[1478] This is the specific process flow of the HealthMate system, which helps users manage their health efficiently and maintain their motivation.

[1479] Example 1

[1480] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1481] In order to provide personalized health management in response to the increasing number of lifestyle-related diseases and health risks in modern society, it is necessary to provide specific and effective advice based on the user's lifestyle and health condition. However, with conventional health management systems, it is difficult to provide advice and plans that meet individual needs, and continuous feedback and improvement are not provided, resulting in insufficient health management for users.

[1482] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1483] In this invention, the server includes an input means for inputting health data from a user, a transmission means for transmitting the health data, a means for encrypting the transmitted health data and transmitting it to the server, an analysis means using a generative AI model to analyze the transmitted health data, a generation means for generating personalized health advice and a health plan based on the analysis means, an integration means for integrating the health plan with gamification elements, a provision means for providing the generated health advice and plan to the user, a feedback transmission means for receiving feedback data from the user and transmitting the feedback data to the server, and a feedback analysis means for analyzing the feedback and generating updated health advice and plans. This enables the provision of personalized health advice and plans to users, and continuous health management and feedback.

[1484] "Health data" refers to information about a user's health status and lifestyle, such as age, gender, height, weight, diet, exercise habits, and sleep duration.

[1485] "Input means" refers to the device or interface through which users input health data, including terminals such as smartphones and PCs.

[1486] "Transmission means" refers to a function for transmitting input health data to a server.

[1487] "Encryption method" refers to the mechanism used to encrypt data to keep it secure.

[1488] "Server" is a central computer system that receives and analyzes the transmitted data and provides generated health advice and plans.

[1489] A "generative AI model" refers to an artificial intelligence algorithm or program that analyzes and generates advice based on input data.

[1490] "Analysis means" refers to the function of analyzing the transmitted health data using a generative AI model.

[1491] "Generation means" refers to the function of generating personalized health advice and health plans based on the analysis results.

[1492] "Integration means" refers to methods and functions for integrating the generated health plan with gamification elements.

[1493] "Gamification elements" refer to incorporating game elements to increase user motivation, including point awarding and reward provision.

[1494] "Provision means" refers to the function for providing the generated health advice and plans to users.

[1495] "Feedback transmission means" refers to a function for transmitting feedback and additional data from users to the server.

[1496] "Feedback analysis means" refers to a function that analyzes received feedback data and generates updated health advice and plans.

[1497] The present invention is a personal health advisor system that uses generative AI to provide personalized health advice. Specifically, the system allows users to input health data, analyzes the data, and provides personalized health advice and health plans. Detailed embodiments of the system are described below.

[1498] The system includes an input means, a transmission means, an encryption means, a server, a generative AI model, an analysis means, a generation means, an integration means, a provision means, a feedback transmission means, and a feedback analysis means.

[1499] Entering health data

[1500] Users enter health data using a smartphone or PC. The hardware used can be a smartphone, tablet, or desktop PC. A dedicated health management application is provided as software. Users launch this application and enter data on items such as "age," "gender," "height," "weight," "diet," "exercise habits," and "sleep time."

[1501] Data Transmission and Encryption

[1502] The terminal validates the entered data. Validation is a process to check whether the entered data is accurate. For example, it checks that the age is over 0 years old, that the weight is within a reasonable range, etc. If invalid data is detected, an error message is displayed to the user, prompting them to correct it.

[1503] After the data is verified to be accurate, the device encrypts it. The latest encryption algorithm (e.g., AES-256) is used for encryption. The encrypted data is then securely transmitted to the server. The HTTPS protocol is used for data transmission, ensuring data security.

[1504] Analysis by generative AI

[1505] The server decodes the received data and generates a prompt for the generative AI model. The generative AI model used is, for example, OpenAI's GPT-3, and this model is used to analyze the data. An example of a prompt might be, "45-year-old male, inactive, recent meals: toast and coffee for breakfast, salad for lunch, pizza for dinner, gym once a week, 6 hours of sleep. Please generate optimal health advice based on this data."

[1506] The server sends prompts to the generative AI model and receives the generated analysis results and health advice. For example, the model might generate advice such as, "We recommend walking 30 minutes three times a week. In addition, we recommend consuming fish and eggs as you may be deficient in vitamin D."

[1507] Generating and synthesizing personalized advice

[1508] The server then creates a specific health plan based on the analysis results. For example, it could generate a personalized plan such as "a plan for walking 30 minutes every morning," "recommended fish recipes," or "a plan for strength training at the gym."

[1509] In addition, gamification elements will be integrated into the health plan created by the server. For example, 10 points will be awarded for each exercise session, and a reward (such as virtual goods) will be provided once a certain number of points have been accumulated. Specifically, a reward system such as "100 points will earn you a virtual good" will be set up.

[1510] Information provision

[1511] The server encrypts the completed health advice and plan, including gamification elements, and sends it to the device. The device then decrypts the received information and displays it to the user through the application's UI. The display includes a dashboard with information such as "Next action: Walk 30 minutes every morning" and "Current score: 40 / 100."

[1512] Receiving and analyzing feedback

[1513] The user again inputs their daily exercise results, dietary habits, weight fluctuations, etc. into the application. For example, they input feedback data such as "Today's walking time: 30 minutes, walking distance: 3 km." The device encrypts this feedback data and sends it to the server.

[1514] The server decodes and analyzes the feedback data. Based on the analysis, it generates new personalized health advice and an improved health plan. For example, new advice might be provided, such as "Maintain your walking frequency and add one strength training session per week."

[1515] Through the above process, personalized health advice and plans are provided, enabling the user to efficiently and continuously manage their health.

[1516] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1517] Step 1:

[1518] The user enters health data using a smartphone or PC. The user launches a dedicated application and enters health information such as age, gender, height, weight, diet, exercise habits, and sleep duration into an input form. Input is done through a form on the application. The entered data is validated in the next step. Examples of input data include "Age: 45 years old," "Gender: Male," "Height: 175 cm," "Weight: 80 kg," "Diet: Breakfast - toast and coffee, lunch - salad, dinner - pizza," "Exercise habits: Gym once a week," and "Sleep time: 6 hours."

[1519] Step 2:

[1520] The terminal validates the health data entered. Validation is the process of making sure the entered data is accurate. For example, it checks that the age is over 0 years old and that the weight is within an appropriate range. It separates normal data from data with errors, and displays an error message to the user if any invalid data is found. An example of an error message might be "Your age is invalid. Please enter it again." Once the data has been validated, it moves on to the next step.

[1521] Step 3:

[1522] The terminal encrypts the data that passes validation and sends it securely to the server. The encryption algorithm used is AES-256, which increases the security of the data. The encrypted data is sent to the server using the HTTPS protocol. The format of the data sent is encrypted binary data.

[1523] Step 4:

[1524] The server decodes the received data and generates a prompt for the generative AI model. An example of a prompt might be, "45-year-old male, inactive, recent meals: toast and coffee for breakfast, salad for lunch, pizza for dinner. Goes to the gym once a week, sleeps 6 hours. Please generate optimal health advice based on this data." OpenAI's GPT-3 is used as an example of a generative AI model. The server sends the prompt to the generative AI model and receives the analysis results.

[1525] Step 5:

[1526] The server analyzes the analysis results received from the AI ​​model and generates optimal health advice and health plans for the user. For example, specific advice may be generated such as "We recommend walking 30 minutes three times a week. In addition, we recommend consuming fish and eggs as you may be deficient in vitamin D." The generated advice is integrated into gamification elements in the next step.

[1527] Step 6:

[1528] The server integrates gamification elements into the generated health plan. The gamification elements include a system that awards points for each exercise session and provides rewards when a certain number of points are accumulated. Specifically, this includes elements such as "receive virtual goods for every 10 points" and "receive a gift card for 100 points." The integrated data then proceeds to the next step.

[1529] Step 7:

[1530] The server encrypts the generated health advice and gamification plan and sends it to the device. The encryption algorithm used is AES-256, and the transmission protocol is HTTPS. The transmitted data is in encrypted binary data format.

[1531] Step 8:

[1532] The device decodes the received data and displays it to the user. Through the UI of the dedicated application, information such as "Next action: Walk 30 minutes every morning" and "Current points: 40 / 100" is displayed on the dashboard.

[1533] Step 9:

[1534] The user re-enters feedback data such as daily exercise results, dietary habits, and weight fluctuations into the dedicated application. For example, they enter data such as "Today's walking time: 30 minutes, walking distance: 3km." The entered feedback data then proceeds to the next step.

[1535] Step 10:

[1536] The terminal validates the feedback data, encrypts it, and sends it to the server. Validation is the process of confirming the accuracy of the input data. The encryption method is AES-256, and the transmission protocol is HTTPS.

[1537] Step 11:

[1538] The server decodes and analyzes the feedback data. A generative AI model is used to generate new health advice based on the feedback data. For example, new advice might be provided, such as "Maintain your walking frequency and add one strength training session per week."

[1539] Through the above process, the user can receive personalized health advice and continuously manage their health.

[1540] (Application example 1)

[1541] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1542] Conventional health management systems have the problem of making it difficult for users to voluntarily input data and provide continuous feedback. Furthermore, while they often provide personalized health plans and advice, they often lack the motivation to encourage users to follow these plans. In particular, there is a demand for a system that enables health management linked to activities in physical stores. Furthermore, when using the system in physical stores, ease of use of the device and secure handling of data are key issues.

[1543] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1544] In this invention, the server includes: an input means for inputting health data from a user; a transmission means for transmitting the health data to the server; an analysis means using a generative AI model to analyze the transmitted health data; a generation means for generating personalized health advice and a health plan based on the analysis means; an integration means for incorporating the health plan into gamification elements; a provision means for providing the generated health advice and plan to the user; a feedback transmission means for receiving feedback from the user and transmitting the feedback data to the server; a feedback analysis means for analyzing the feedback and generating updated health advice and a plan; a physical store integration means for allowing users to input health data using a terminal in a physical store and providing personalized health plans and gamification elements based on the analysis results at the physical store; and a feedback receiving means for inputting the results of implementing the health plan as feedback to the terminal and transmitting the feedback to the server. This enables each user to receive appropriate advice and a health management plan, facilitate their implementation, and provide the results as feedback, thereby enabling more accurate health management.

[1545] "User" refers to an individual who uses the Personal Health Advisor System.

[1546] "Health data" refers to information related to a user's health status and lifestyle, such as their age, gender, height, weight, exercise habits, diet, and sleep duration.

[1547] "Input means" refers to the interface through which users input health data using a smartphone, tablet, PC, etc.

[1548] "Transmission means" refers to the means for encrypting the entered health data and transmitting it securely to the server.

[1549] "Generative AI model" refers to an artificial intelligence model that analyzes health data and generates personalized health advice and health plans.

[1550] "Analysis Means" refers to the means for analyzing health data using a generative AI model.

[1551] "Generation means" refers to means for generating personalized health advice and health plans based on the analysis results by the analysis means.

[1552] "Integration means" refers to means for incorporating the generated health plan into gamification elements.

[1553] "Delivery means" refers to the means for providing the generated health advice and plan to the user.

[1554] "Feedback sending means" refers to means for receiving feedback from a user and sending feedback data to a server.

[1555] "Feedback analysis means" refers to means for analyzing received feedback and generating updated health advice and plans.

[1556] "Physical store integration means" refers to a means by which users can input health data using a terminal in a physical store and receive personalized health plans and gamification elements based on the analysis results in the physical store.

[1557] The "feedback receiving means" refers to a means for inputting the results of the health plan implementation into the terminal as feedback again and transmitting it to the server.

[1558] This invention is a personal health advisor system that utilizes generative AI, specifically designed for use in brick-and-mortar stores. This system inputs users' health data via terminals installed in the brick-and-mortar stores, and based on that data, provides personalized health advice and promotes continuous health management through gamification elements.

[1559] System configuration

[1560] 1. Input Method

[1561] Users enter their health data (age, gender, height, weight, exercise habits, eating habits) using devices such as smartphones, tablets, and PCs installed in physical stores.

[1562] 2. Transmission Method

[1563] The device validates the entered health data to ensure it is accurate, then encrypts it and securely transmits it to the server, using the HTTPS protocol for this process.

[1564] 3. Analysis Methods Using Generative AI Models

[1565] The server decodes the received health data and analyzes it using a generative AI model (e.g., a model using TensorFlow or PyTorch). This analysis evaluates the user's current health status and health risks.

[1566] 4. Means of generating personalized advice and health plans

[1567] The server then generates optimal health advice and plans for each user based on the analysis results. For example, if a user is deficient in a particular nutrient, a plan recommending foods containing that nutrient will be generated.

[1568] 5. Means of integration with gamification elements

[1569] The server integrates the generated health plan with gamification elements, such as a system that awards points for each exercise session and offers products or services from a store when a certain number of points are accumulated.

[1570] 6. Means of providing information

[1571] The server sends the generated advice, plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user, such as the next health action to be taken and the user's current progress.

[1572] 7. Feedback Submission Methods

[1573] The user acts according to the advice and then inputs the results and additional data (e.g., weight fluctuations and number of exercise sessions) into the device again. The device then encrypts the input feedback data and sends it to the server.

[1574] 8. Feedback Analysis Methods

[1575] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data.

[1576] System operation example

[1577] A case of a middle-aged man aiming to maintain his health

[1578] 1. User A (male, 45 years old) uses a terminal at a physical store to enter his recent meal details (breakfast: toast and coffee, lunch: salad, dinner: pizza), exercise habits (gym once a week), and sleep time (6 hours).

[1579] 2. The device validates this data and sends it to the server.

[1580] 3. The server analyzes the data and determines whether you are not getting enough exercise.

[1581] 4. The server generates a personalized health plan, such as "recommended 30 minutes of walking three times a week."

[1582] 5. Add a gamification element by awarding 10 points for each walk the server completes, and once they have accumulated 100 points they can receive a reward from the store (e.g. a free training session).

[1583] 6. The server sends this information to the terminal, which displays it to User A.

[1584] 7. User A walks and enters the results (e.g., walking time and distance) into the terminal.

[1585] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Maintain walking three times a week and add strength training once a week."

[1586] Prompt Sentence Examples

[1587] 1. "45 years old, male, height 175 cm, weight 85 kg, exercises once a week."

[1588] 2. "Eating habits: Breakfast (toast and coffee), lunch (salad), dinner (pizza). Added exercise time to feedback."

[1589] As described above, the present invention is a system that supports efficient and continuous health management in physical stores, and specifically includes the provision of personalized health advice using generative AI models and the integration of gamification elements.

[1590] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1591] Step 1:

[1592] Users enter their health data (age, gender, height, weight, diet, exercise habits, and sleep time) using a terminal at a physical store.

[1593] Input: User's health data

[1594] Output: Health data stored on the device

[1595] Step 2:

[1596] The terminal validates the entered data to ensure it is accurate, then encrypts it and securely transmits it to the server.

[1597] Input: Health data stored on the device

[1598] Output: Encrypted health data is sent to the server.

[1599] Step 3:

[1600] The server decodes the received data and analyzes it using a generative AI model, which assesses the user's health status and health risks.

[1601] Input: Encrypted health data

[1602] Output: Analysis results (user's health status and health risks)

[1603] Step 4:

[1604] Based on the analysis results, the server generates personalized health advice and plans that are best suited to the user, such as "walking three times a week is recommended."

[1605] Input: Analysis results

[1606] Output: Personalized health advice and health plans

[1607] Step 5:

[1608] The server then integrates the generated health plan with gamification elements, such as rewarding points for each exercise session and allowing users to receive special rewards at physical stores once they have accumulated a certain number of points.

[1609] Input: personalized health advice and health plans

[1610] Output: A health plan with integrated gamification elements

[1611] Step 6:

[1612] The server sends the generated advice, plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user, such as the next health action to be taken and the current progress.

[1613] Input: Health plans with integrated gamification elements

[1614] Output: Health advice and plan displayed on the device

[1615] Step 7:

[1616] The user follows the advice and then enters the results and additional data (e.g., walking time and distance) back into the device.

[1617] Input: User action results and additional data

[1618] Output: Feedback data stored on the device

[1619] Step 8:

[1620] The terminal encrypts the input feedback data and transmits it to the server.

[1621] Input: Feedback data

[1622] Output: Encrypted feedback data is sent to the server

[1623] Step 9:

[1624] The server analyzes the feedback data and provides more accurate advice and health plans based on the new data. For example, the next advice provided might be, "Maintain walking three times a week and add strength training once a week."

[1625] Input: Feedback data

[1626] Output: The new personalized advice and health plan is sent to the terminal.

[1627] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1628] This invention combines an emotion engine with a personal health advisor system that utilizes generative AI to provide more adaptive and personalized health advice and health plans. This system analyzes the user's health data and emotional state, and generates and provides optimal advice based on that data.

[1629] System Overview

[1630] 1. Entering health and emotional data

[1631] Users use input devices (smartphones or PCs) to input personal information, lifestyle data, and emotional state data. Emotional data is acquired through questionnaires, facial recognition cameras, voice assistants, etc.

[1632] 2. Data transmission

[1633] The device validates the entered health and emotion data, checking the data format and range, and prompting the user to make corrections if necessary.

[1634] The terminal encrypts the data that has passed validation and sends it securely to the server.

[1635] 3. Analysis using generative AI and emotion engine

[1636] The server decodes and normalizes the received data and analyzes it using a generative AI model and an emotion engine. The generative AI model analyzes health data, and the emotion engine analyzes emotion data.

[1637] The server integrates the analysis results of the emotion engine with the generative AI model to comprehensively evaluate the user's health and emotional state.

[1638] 4. Generating personalized advice

[1639] Based on the analysis results, the server generates optimal health advice and health plans for the user. For example, if the user is under high stress, a relaxation plan will be provided.

[1640] The server adjusts the generated health plan based on your emotional state, recommending a more aggressive exercise plan when you're feeling energized and a lighter exercise plan when you're feeling down.

[1641] 5. Gamification Integration

[1642] The server integrates gamification elements into the generated health plan, specifically adding a points system and virtual rewards mechanism.

[1643] 6. Information provision

[1644] The server sends the generated advice, health plan, and gamification information to the device.

[1645] The device displays this information to the user in an easy-to-understand manner, and measures such as changing the message depending on the user's emotional state are taken into consideration.

[1646] 7. Receiving and Analyzing Feedback

[1647] The user acts according to the advice and inputs the results into the terminal again, along with their new emotional state.

[1648] The terminal encrypts the input feedback data and emotion data and transmits them again to the server.

[1649] The server analyzes the feedback data and generates more accurate advice and health plans.

[1650] Specific examples

[1651] Case 1: Middle-aged women seeking health management

[1652] 1. User B (50 years old, female) uses her smartphone to enter her personal information, recent dietary habits, exercise habits, sleep time, and emotional state ("I'm feeling a little stressed today").

[1653] 2. The device validates this data and sends it to the server.

[1654] 3. The server analyzes the data and determines that the stress level is high.

[1655] 4. The server generates a personalized health plan, such as "Light yoga recommended three times a week to reduce stress."

[1656] 5. Add a gamification element where the server awards 10 points for each yoga session, and when you collect 100 points you can receive a virtual prize.

[1657] 6. The server sends this information to the device, which displays it to User B. An encouraging message tailored to User B's emotional state is also displayed.

[1658] 7. User B practices yoga and inputs the results (for example, yoga time and mood changes) into the terminal.

[1659] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Continue doing yoga twice a week and add walking once a week."

[1660] In this way, combining the emotion engine enables optimal health management according to the user's emotional state. This allows users to receive health management tailored to their emotions, making it easier for them to maintain motivation.

[1661] The processing flow will be explained below.

[1662] Step 1:

[1663] Users open their smartphones or PCs and enter personal information (age, gender, height, weight, etc.) and lifestyle data (dietary habits, exercise habits, sleep time, etc.). They also enter data on their emotional state. This emotional data is acquired through questionnaires, facial recognition, voice input, etc.

[1664] Step 2:

[1665] The terminal validates the entered data. Specifically, it checks the data format and range, and prompts the user to correct the entered data if necessary. For example, if the weight entered is out of range, the user is asked to re-enter it.

[1666] Step 3:

[1667] The device encrypts the health data and emotion data that have passed validation and transmits them securely to the server.

[1668] Step 4:

[1669] The server decrypts the received data and verifies that it was received correctly, thereby ensuring that the data has not been corrupted.

[1670] Step 5:

[1671] The health and emotion data received by the server is normalized, the data scale is unified, and missing values ​​are filled in. In this process, the data format is unified, making analysis easier.

[1672] Step 6:

[1673] The server analyzes the normalized health data using a generative AI model, which evaluates the user's health status and identifies health risks and areas for improvement. At the same time, it analyzes the emotional data using an emotion engine to evaluate the user's emotional state.

[1674] Step 7:

[1675] The server combines the results of the generative AI model and the emotion engine to perform a comprehensive evaluation, which generates advice and plans that take into account not only the user's health condition but also their emotional state.

[1676] Step 8:

[1677] The server generates personalized health advice and a health plan based on the analysis results. For example, if a user is gaining weight but experiencing high stress, it will suggest relaxation and light exercise focused on stress reduction.

[1678] Step 9:

[1679] The server integrates gamification elements into the generated health plan, specifically by awarding points for completing each health task and providing rewards for achieving certain goals.

[1680] Step 10:

[1681] The server sends the generated advice, health plan, and gamification information to the device.

[1682] Step 11:

[1683] The device then displays the information it receives to the user, including next health actions, current progress, points earned, and messages based on emotional state.

[1684] Step 12:

[1685] The user acts on the presented advice and inputs the results of the action and the new emotional state into the terminal, including feedback such as "Today's walk felt good."

[1686] Step 13:

[1687] The terminal encrypts the input feedback data and emotion data and transmits them again to the server.

[1688] Step 14:

[1689] The server analyzes the received feedback and emotion data, which is then used to generate more accurate advice and health plans using a generative AI model and emotion engine.

[1690] Step 15:

[1691] The server sends updated advice and health plans to the device, which then displays them to the user, allowing the user to continuously manage their health.

[1692] This is the specific processing flow of the HealthMate system, which combines an emotion engine. This system enables personalized health management that adapts not only to the user's health condition but also to their emotional state.

[1693] Example 2

[1694] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1695] Conventional health management systems only provide general advice based on users' health data and are unable to provide personalized health advice that takes into account the user's emotional state. Furthermore, they lacked gamification elements to maintain motivation, making it difficult to continue long-term health management. This prevented users from receiving effective health management tailored to their emotional state, reducing the efficiency of health improvement.

[1696] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1697] In this invention, the server includes: an input means for inputting health data and emotional data from a user; a transmission means for transmitting the health data and emotional data to the server; a validation means including a terminal for validating and encrypting the transmitted health data and emotional data; an analysis means for decrypting and normalizing the encrypted health data and emotional data and analyzing them using a generative AI model and an emotion engine; a generation means for generating personalized health advice and a health plan based on the analysis means; an adjustment means for adjusting the generated health plan based on the user's emotional state; an integration means for incorporating gamification elements into the health plan; a provision means for providing the generated health advice and plan to the user; a feedback transmission means for receiving feedback from the user and transmitting the feedback data and emotional data to the server; and a feedback analysis means for analyzing the feedback and generating updated health advice and a health plan. This allows for the provision of personalized health advice and a health plan that takes the user's emotional state into consideration. Furthermore, incorporating gamification elements can maintain user motivation and encourage continued health management.

[1698] "Health data" refers to information such as a user's personal information, lifestyle data, diet, exercise habits, and sleep time.

[1699] "Emotional data" refers to data that represents a user's emotional state, and refers to information obtained through questionnaires, facial recognition cameras, voice assistants, etc.

[1700] "Input means" refers to a device or interface that allows a user to input health and emotional data into the system.

[1701] "Transmission means" refers to the functions and processes for transmitting the input health data and emotion data to the server.

[1702] "Validation measures" refer to functions that check the format and scope of health data and emotional data and prompt corrections if necessary.

[1703] "Encryption" refers to the process of using cryptography to protect health and emotional data in order to securely transmit it to a server.

[1704] "Decryption and Normalization" refers to the process by which the server decrypts encrypted data and converts it into a format suitable for the system.

[1705] "Generative AI model" refers to an artificial intelligence model that analyzes health data and generates health advice and health plans appropriate for users.

[1706] "Emotion engine" refers to a computer program that analyzes emotion data and assesses a user's emotional state.

[1707] "Analysis Means" refers to the process of analyzing received data using the generative AI model and emotion engine.

[1708] "Generation means" refers to a function that generates personalized health advice and health plans based on the analysis results.

[1709] "Adjustment means" refers to a function that adjusts the generated health plan based on the user's emotional state.

[1710] "Integration measures" refers to the ability to incorporate gamification elements into health plans.

[1711] "Gamification elements" refers to a system that incorporates game elements such as a point system and virtual rewards to increase user motivation.

[1712] "Delivery means" refers to the process by which the generated health advice and health plans are provided to the user.

[1713] "Feedback transmission means" refers to a function for transmitting feedback data and new emotion data from the user to the server.

[1714] "Feedback analysis means" refers to the process that analyzes received feedback data and generates updated health advice and health plans.

[1715] This invention combines an emotion engine with a personal health advisor system that utilizes generative AI to provide more adaptive and personalized health advice and health plans. This system analyzes the user's health data and emotional state, and generates and provides optimal advice based on that data.

[1716] System Overview

[1717] Entering health and emotional data

[1718] A user launches an application using a device such as a smartphone or PC. The user enters personal information (age, gender, etc.), lifestyle data (dietary habits, exercise habits, sleep duration, etc.), and emotional state (e.g., "I feel a little stressed today"). Emotional data is obtained using questionnaires, facial recognition cameras, voice assistants, etc.

[1719] Sending data

[1720] The terminal validates the entered data, checking the data format and range and prompting the user to make corrections if necessary. After that, the terminal encrypts the data that passes validation using AES encryption technology and sends it securely to the server.

[1721] Analysis using generative AI and emotion engine

[1722] The server decodes and normalizes the received data, analyzes the health data using a generative AI model, and simultaneously analyzes the emotional data using an emotion engine. The server then integrates the results of the generative AI model and the emotion engine to comprehensively evaluate the user's health and emotional state.

[1723] Generating personalized advice

[1724] Based on the analysis results, the server generates optimal health advice and health plans for users. For example, if a user is under high stress, a relaxation plan is provided, and if the user is feeling well, a more active exercise plan is recommended.

[1725] Gamification Integration

[1726] The server integrates gamification elements into the generated health plans, specifically by adding a points system and virtual rewards mechanism to increase motivation.

[1727] Information provision

[1728] The server sends the generated advice, health plan, and gamification information to the device, which then displays this information in an easy-to-understand manner for the user. For example, the device may change the message depending on the user's emotional state.

[1729] Receiving and analyzing feedback

[1730] The user acts on the advice and then inputs the results and new emotional state back into the device. The device then sends this feedback data back to the server, which analyzes the feedback data and generates more accurate advice and health plans. This process continuously optimizes the user's health management.

[1731] Specific examples

[1732] Case 1: Middle-aged women seeking health management

[1733] 1. User B (50 years old, female) uses her smartphone to enter her personal information, recent dietary habits, exercise habits, sleep time, and emotional state ("I'm feeling a little stressed today").

[1734] 2. The device validates this data and sends it to the server.

[1735] 3. The server analyzes the data and determines that the stress level is high.

[1736] 4. The server generates a personalized health plan, such as "Light yoga recommended three times a week to reduce stress."

[1737] 5. Add a gamification element where the server awards 10 points for each yoga session, and when you collect 100 points you can receive a virtual prize.

[1738] 6. The server sends this information to the device, which displays it to the user, along with encouraging messages tailored to the user's emotional state.

[1739] 7. User B practices yoga and again inputs the results (for example, yoga time and mood changes) into the terminal.

[1740] 8. The device sends the feedback data to the server, which analyzes the data and generates new advice, such as "Continue doing yoga twice a week and add walking once a week."

[1741] By combining this system with an emotion engine, it is possible to provide optimal health management according to the user's emotional state. This allows users to receive health management tailored to their emotions, making it easier for them to maintain motivation.

[1742] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1743] Step 1:

[1744] A user starts the application using a smartphone or PC. The user inputs personal information (e.g., age, gender), lifestyle data (e.g., dietary habits, exercise habits, sleep duration), and emotional state (e.g., "I feel a little stressed today"). The input data is saved in the application.

[1745] Input: Personal information, lifestyle data, emotional state

[1746] Output: Saving the entered data within the application

[1747] Specific behavior: The user enters data into the input form and clicks the submit button.

[1748] Step 2:

[1749] The device validates the entered health and emotion data. It checks the data format (for example, age is a number, gender is a string) and range (for example, sleep time is 0-24 hours), and asks the user to make corrections if necessary. Data that passes validation is encrypted in the next step.

[1750] Input: User-entered health and emotion data

[1751] Output: Data that has passed validation, and a request for correction if necessary

[1752] Specific operation: The terminal checks the data format and value range, and if an error message is displayed, prompts you to correct it.

[1753] Step 3:

[1754] The terminal encrypts the data that has passed validation using AES encryption technology, and the encrypted data is sent to the server.

[1755] Input: Data that passes validation

[1756] Output: Encrypted data, sent to server

[1757] Specific operation: The terminal encrypts the data using the AES algorithm and sends the encrypted data to the server over the network.

[1758] Step 4:

[1759] The server decrypts the received encrypted data and normalizes it, for example, standardizing date formats. The health data is then analyzed using a generative AI model. At the same time, the emotion engine analyzes the emotion data. The analysis results are integrated into an overall assessment.

[1760] Input: Encrypted data

[1761] Output: Decoded and normalized data, analysis results of the generative AI model, analysis results of the emotion engine, and overall evaluation

[1762] Specific operation: The server decrypts the encrypted data, unifies the data format, and applies the generative AI model and emotion engine to generate analysis results.

[1763] Step 5:

[1764] The server generates personalized health advice and health plans based on the overall assessment. For example, if the user is under high stress, a yoga plan may be generated. The generated health plan is tailored to the user's emotional state.

[1765] Input: Overall rating

[1766] Output: Personalized health advice and health plans

[1767] Specific operation: The server applies an algorithm based on the overall evaluation results to generate appropriate health advice and plans.

[1768] Step 6:

[1769] The server integrates gamification elements into the generated health plans, specifically adding a points system and virtual rewards mechanism.

[1770] Input: personalized health advice and health plans

[1771] Output: A health plan with integrated gamification elements

[1772] Specific operation: The server incorporates a points system and achievement rewards into the health plan.

[1773] Step 7:

[1774] The server sends the generated health advice, health plan, and gamification information to the device, which then displays this information in an easy-to-understand manner to the user. It also displays messages based on the user's emotional state.

[1775] Input: Health plans with integrated gamification elements

[1776] Output: Displayed health advice and health plan

[1777] Specific operation: The server sends data to the device, and the device conveys the information to the user via push notification or in-app display.

[1778] Step 8:

[1779] The user acts according to the advice and then inputs the results and emotional state back into the device, which then re-encrypts this feedback data and sends it to the server.

[1780] Input: Feedback data and emotion data from users

[1781] Output: Encrypted feedback data, sent to server

[1782] Specific operation: The user fills out the feedback form and clicks the submit button. The device encrypts the data and sends it to the server.

[1783] Step 9:

[1784] The server analyzes the feedback data and generates new health advice and health plans, and further refines the health plans based on the analysis results.

[1785] Input: Encrypted feedback data

[1786] Output: Updated health advice and health plan

[1787] What happens: The server decodes the feedback data and applies analysis algorithms to generate new advice and plans.

[1788] (Application example 2)

[1789] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1790] In modern society, people often find it difficult to balance health management with their emotions. As lifestyles become more diverse and individuals' health and emotional states fluctuate daily, standardized health advice and plans are insufficient. Other issues include a lack of motivation and health advice tailored to individual emotional states. Therefore, there is a need for a system that provides adaptive and personalized health plans and advice based on each user's health and emotional data.

[1791] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for inputting health data and emotional data from the user, a transmission means for transmitting the health data and emotional data to the server, an analysis means using a generative AI model and an emotion engine to analyze the transmitted health data and emotional data, a generation means for generating personalized health advice and a health plan based on the analysis means, an integration means for incorporating the health plan into gamification elements, a provision means for providing the generated health advice and plan to the user, a feedback transmission means for receiving feedback from the user and transmitting the feedback data to the server, and a feedback analysis means for analyzing the feedback and generating updated health advice and a plan. This makes it possible to adaptively provide optimal health plans and advice based on the user's health and emotional state. Furthermore, the gamification elements can improve the user's motivation and support continuous health management.

[1792] "User" refers to an individual who uses the system.

[1793] "Health data" refers to information about an individual's health status, including, for example, dietary habits, exercise habits, and sleep duration.

[1794] "Emotional data" is information about an individual's emotional state, collected through surveys, facial recognition cameras, and voice assistants.

[1795] A "generative AI model" is an artificial intelligence model that analyzes a user's health data and generates personalized health plans and advice.

[1796] The "emotion engine" is an engine for analyzing emotion data and evaluating the user's emotional state.

[1797] "Gamification elements" are systems that incorporate game elements to increase user engagement, and examples include point systems and virtual rewards.

[1798] "Input means" refers to the means by which users input health data and emotional data into the system, and includes smartphones and PCs.

[1799] The "transmission means" is a means for transmitting the input health data and emotion data to the server.

[1800] "Analysis Means" means means for analyzing health data and emotion data using a generative AI model and emotion engine.

[1801] "Generation means" refers to means for generating personalized health advice and health plans based on the analysis results.

[1802] "Integration means" refers to means for integrating the generated health plan with gamification elements.

[1803] The "provision means" is a means for providing the generated health advice and plan to the user.

[1804] The "feedback sending means" is a means for receiving feedback from a user and sending it to the server.

[1805] "Feedback analysis means" means for analyzing received feedback and generating updated health advice and plans.

[1806] System Overview

[1807] The system according to the present invention is a system for providing personalized health advice and health plans using health and emotional data of a user. Specifically, the system includes the following elements:

[1808] 1. Input means for inputting health data and emotion data from the user

[1809] Users use their smartphones or PCs to enter personal information, lifestyle data, recent dietary habits, exercise habits, sleep time, emotional state, etc. Emotional data is collected through questionnaires, facial recognition cameras, and voice assistants.

[1810] 2. Means for transmitting health data and emotion data to the server

[1811] The device validates the entered health and emotion data, encrypts it, and securely transmits it to the server.

[1812] 3. Analysis methods using generative AI models and emotion engines to analyze data

[1813] The server decodes and normalizes the received data and analyzes the health data using a generative AI model. At the same time, it analyzes the emotion data using an emotion engine. The generative AI model evaluates the user's health status, and the emotion engine evaluates their emotional state.

[1814] 4. Means for generating personalized health advice and health plans

[1815] Based on the analysis results, the server generates optimal health advice and health plans for the user. For example, if the user is experiencing high stress, a relaxation plan will be provided.

[1816] 5. Integrating health plans into gamification elements

[1817] The server integrates gamification elements into the generated health plan, specifically adding a points system and virtual rewards mechanism.

[1818] 6. Means for providing generated health advice and plans to users

[1819] The server transmits the generated advice, health plan, and gamification information to the terminal, which then displays this information to the user.

[1820] 7. Feedback sending means for receiving feedback and sending feedback data to the server

[1821] The user then inputs the results of their actions based on the advice into the terminal, including their new emotional state. The terminal then transmits this feedback data and emotional data to the server.

[1822] 8. Feedback analysis means for analyzing the feedback and generating updated health advice and plans.

[1823] The server analyzes the feedback data and generates new health advice and plans, which allows for more accurate advice and health plans to be provided to the user.

[1824] Hardware and software used

[1825] Hardware:

[1826] Smartphone, PC: Used to enter and display user data and receive feedback.

[1827] software:

[1828] Cloud server: Used to analyze data and generate advice.

[1829] Generative AI models: Analyze health data and generate personalized advice.

[1830] Emotion engine: Analyzes emotional data and generates appropriate messages based on emotional state.

[1831] Specific examples

[1832] User A opens his smartphone and inputs his personal information, recent dietary habits, exercise habits, and sleep time, as well as his emotional state, such as "I feel a little stressed today." This data is sent to the server and analyzed by a generative AI model and emotion engine. The server generates a personalized plan for stress reduction, such as "Light yoga recommended three times a week," and adds a message based on the user's emotional state, such as "Relax today." This information is then displayed on the smartphone. When the user practices yoga, points are awarded, and when a certain number of points are reached, virtual rewards are earned.

[1833] Prompt Sentence Examples

[1834] Age: 30

[1835] Gender: Male

[1836] Recent meal: Chicken, salad, pasta

[1837] Exercise habits: Running twice a month

[1838] Sleep time: 7 hours

[1839] Emotional state: Very tired

[1840] In this way, it is possible to provide users with appropriate health plans and emotional support tailored to their specific circumstances.

[1841] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1842] Step 1:

[1843] Users use a smartphone or PC to input personal information, recent dietary habits, exercise habits, sleep duration, and emotional state (e.g., "I feel a little stressed today") using a dedicated application. This input method generates input data.

[1844] Step 2:

[1845] The device validates the entered health and emotion data. For example, it checks the data format and range, and prompts the user to make corrections if necessary. Once validated, the data is encrypted and sent to the cloud server. Encryption is performed using technologies such as SSL / TLS.

[1846] Step 3:

[1847] The server decrypts the received encrypted data and normalizes it. This data includes personal information, health data, and emotional data. The server then analyzes the health data using a generative AI model to generate a personalized health plan. At the same time, it analyzes the emotional data using an emotional engine to generate appropriate emotional messages.

[1848] Step 4:

[1849] The server combines the analysis results of the generative AI model and the emotion engine to generate optimal health advice and plans for users. For example, if a user is in a high stress state, the server generates a plan such as "recommended light yoga three times a week to reduce stress."

[1850] Step 5:

[1851] The server then integrates the generated health plan with gamification elements, such as a points system and virtual rewards system. For example, points are awarded for each yoga session, and virtual goods can be awarded once a certain number of points are accumulated.

[1852] Step 6:

[1853] The server sends the generated health plan, advice, and gamification information to the end user's device, which displays the received information in an easy-to-understand format, including encouraging messages based on the user's emotional state.

[1854] Step 7:

[1855] The user acts according to the advice and health plan provided and inputs the results as feedback into the device, including the time spent doing yoga and changes in mood during that time.

[1856] Step 8:

[1857] The terminal again transmits the feedback data and new emotion data to the server, which is then encrypted before transmission.

[1858] Step 9:

[1859] The server analyzes the feedback data and generates new health advice and health plans based on the most recent data, thereby providing more accurate advice to the user.

[1860] Through these steps, the system can adaptively provide optimal health plans and advice according to the user's health and emotional state. Furthermore, the gamification element can increase user motivation and support continuous health management.

[1861] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1862] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1863] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1864] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1865] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1866] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1867] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1868] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1869] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1870] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1871] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1872] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1873] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1874] 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.

[1875] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1876] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1877] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1878] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned vario...

Claims

1. an input means for inputting health data from a user; a transmitting means for transmitting the health data to a server; an analysis means using a generative AI model to analyze the transmitted health data; generating means for generating a personalized health advice and health plan based on said analyzing means; an integration means for incorporating gamification elements into said health plan; providing means for providing the generated health advice and plan to a user; a feedback sending means for receiving feedback from a user and sending the feedback data to a server; a feedback analysis means for analyzing the feedback and generating updated health advice and plans; A system including:

2. 10. The system of claim 1, wherein the integration means includes means for awarding points to a user for completing health tasks and for providing rewards for achieving specified goals.

3. The system according to claim 1 , wherein the providing means includes means for transmitting the generated health plan and gamification information to a terminal of the user, and the terminal displays the information.

Images