System

Abstract

A system is provided.SOLUTION: A system comprising: input means for inputting a user's health status and goals; transmission means for transmitting user data received from the input means to a server; analysis means for the server to analyze the user data and calculate individual required nutrients; generation means for generating appropriate meal and training menus based on the analysis results; output means for transmitting the menus generated by the generation means to a terminal; display means for displaying the menus to the user; and feedback means for inputting the progress of meals and exercises performed by the user.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 recent years, as the importance of health management has increased, there is a demand for specific advice and plans tailored to individual health conditions and goals. However, conventional methods lack a system that can automatically generate personalized menus suited to individual nutritional needs and athletic ability and enable users to easily implement them. Therefore, an efficient and effective method for providing optimal nutrition and training is needed. [Means for solving the problem]

[0005] The present invention provides a system including an input means for inputting a user's health condition and goals, a transmission means for transmitting the data to a server, an analysis means for analyzing the user data and calculating required nutrients, a generation means for generating appropriate meal and training menus based on the analysis results, an output means for transmitting the generated menus to a terminal, a display means for the terminal to display the menus to the user, and a feedback means for inputting the user's diet and exercise progress. This system can efficiently provide optimal nutrition and training menus based on each user's individual health condition and goals. Furthermore, the server includes an evaluation means for analyzing vital data and evaluating athletic ability, and the terminal includes a synchronization means for synchronizing data from vital sensors, enabling more accurate personalized advice.

[0006] "Input means" refers to a device or function that allows a user to input health status and goals.

[0007] The "transmission means" refers to a device or function for transmitting data input by a user to a server.

[0008] "Analysis means" refers to a device or function that allows the server to analyze user data and calculate individual nutrient needs.

[0009] The "generation means" refers to a device or function for generating appropriate meal menus and training menus based on the analysis results.

[0010] "Output means" refers to a device or function for transmitting the generated menu to a terminal.

[0011] The "display means" refers to a device or function that allows the terminal to display the menu received from the server to the user.

[0012] A "feedback means" is a device or function that allows a user to input progress on diet and exercise.

[0013] The "evaluation means" refers to a device or function that allows the server to analyze vital data and evaluate the user's athletic ability and muscle mass.

[0014] The "synchronization means" is a device or function that allows the terminal to automatically acquire data from the user's vital sensors. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is a system that provides an optimal health promotion plan based on the user's health condition and goals. Below, we will explain the program of this system and introduce its operation with specific examples.

[0037] Specific system construction

[0038] 1. User Roles

[0039] Users can launch the application on their smartphone or tablet and input their health status and goals, such as weight, height, allergy information, and goals such as weight loss or muscle gain. They can also input their favorite and least favorite foods.

[0040] 2. Role of the terminal

[0041] The terminal (smartphone or tablet device) receives data entered by the user and transmits it to the server, where it is properly formatted and sent via secure communication.

[0042] 3. Server Roles

[0043] The server analyzes the received user data and uses generative AI to calculate the nutritional needs of each individual, creating a personalized nutrition plan based on the user's health status and goals.

[0044] The server then accesses a nutrition database to select ingredients that meet the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. This recipe includes specific amounts of ingredients, cooking instructions, and the nutritional value of the dish.

[0045] Similarly, for training menus, the server analyzes data from vital sensors to evaluate the user's athletic ability and muscle mass. Based on the evaluation results, the generative AI creates an appropriate training menu. This menu includes specific exercise steps, loads, number of repetitions, and reference video links.

[0046] The generated menu information is transmitted from the server to the terminal.

[0047] 4. Terminal Processing

[0048] The device receives the data sent from the server and displays it to the user. The user can then check and follow the provided cooking recipes and training menus through the application. The device also periodically syncs data from vital sensors and records the user's progress.

[0049] 5. User Feedback

[0050] Users input their diet and exercise progress into the application. They can also automatically synchronize vital sensor values ​​with the application. The feedback data is sent from the device to a server and analyzed again. This cycle allows the device to adjust diet and training menus appropriately based on the user's progress.

[0051] Introduction of specific examples

[0052] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The server analyzes the user's current weight, height, allergies, food preferences, etc., and generates a low-calorie, nutritionally balanced meal menu. It also calculates basal metabolic rate and calories burned during exercise based on data from vital sensor data, and provides a menu of moderate-intensity aerobic exercise and muscle training that matches these.

[0053] In this way, the system of the present invention can flexibly respond to the health status and goals of each user and provide a personalized health promotion plan.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] Users turn on their smartphone or tablet and open a health management application. Within the application, they enter their basic information and health goals (e.g., weight loss, muscle gain, etc.), as well as food preferences and allergy information.

[0057] Step 2:

[0058] The device receives data entered by the user, formats it appropriately, and sends it to the server, including weight, height, goals, food preferences, and allergy information.

[0059] Step 3:

[0060] The server receives user data sent from the device, analyzes this data, and uses generative AI to calculate the appropriate nutrients for each user.

[0061] Step 4:

[0062] The server accesses a nutrition database and selects appropriate ingredients based on the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. The recipe includes ingredients, specific amounts, cooking instructions, and nutritional information.

[0063] Step 5:

[0064] The server analyzes the user's data obtained from vital sensors, assesses the user's athletic ability and muscle mass, and uses generative AI to create an appropriate training menu, including exercise procedures, loads, number of repetitions, and reference video links.

[0065] Step 6:

[0066] The server sends the generated cooking recipes and training menus to the terminal.

[0067] Step 7:

[0068] The device receives the data sent from the server and displays it to the user, who then checks the provided cooking recipes and training menus through the application and follows the instructions.

[0069] Step 8:

[0070] Users input their diet and exercise progress into the application, and the input data is stored on the device as feedback.

[0071] Step 9:

[0072] The device transmits the progress data entered by the user and the data from the vital sensors to the server.

[0073] Step 10:

[0074] The server re-analyzes the received progress data and vital sensor data, regenerates meal and training menus as needed based on the analysis results, and sends the improved plan to the device.

[0075] Step 11:

[0076] The terminal receives the regenerated plan and displays it to the user, who then proceeds with further execution based on it.

[0077] The above are the specific processing steps for the user, the terminal, and the server. This system efficiently provides the user with a meal and exercise plan that is optimal for their individual health goals.

[0078] Example 1

[0079] 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."

[0080] Conventional health management systems have struggled to provide appropriate meal and training menus based on individual users' health status and goals. They also lack the ability to synchronize vital data in real time, and they do not guarantee that generated plans are consistently updated based on the user's progress. This has led to problems that prevent users from fully utilizing their personalized, optimal health plans.

[0081] 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.

[0082] In this invention, the server includes an input means for inputting the user's health status and goals, a transmission means for transmitting the user data received from the input means to the server, an analysis means for the server to analyze the user data and calculate individual nutrient needs, a generation means for using a generative AI model to generate appropriate meal and training menus based on the analysis results, an output means for transmitting the menus generated by the generation means to a terminal, a display means for the terminal to display the menus to the user, a feedback means for inputting and providing feedback on the progress of the user's diet and exercise, and a synchronization means for synchronizing data from vital sensors in real time. This allows the user to obtain an optimal, personalized health promotion plan and realize specific and feasible health management.

[0083] "Input means" refers to a device or application software that allows a user to input their health status and goals.

[0084] The "transmission means" is a function or device that transmits user data received from the input means to the server.

[0085] The "analysis means" is a program or device that analyzes user data on the server and calculates the nutrients required for each user.

[0086] The "generation means" is a program or device that uses a generative AI model to generate appropriate meal and training menus based on the results of the analysis means.

[0087] "Output means" refers to a function or device that transmits the generated menu to the terminal.

[0088] The "display means" refers to a device or program for visually presenting the menu sent from the server to the user.

[0089] The "feedback means" is a device or program that allows the user to input the progress of their diet and exercise and transmit that information to the server.

[0090] The "synchronization means" is a function or device for receiving data from the vital sensors in real time and synchronizing it with the server.

[0091] The system of the present invention provides an optimal health promotion plan based on the user's health condition and goals. The following provides an overview of this system with specific examples.

[0092] First, a user launches the application on a smartphone or tablet and inputs their health status and goals. This includes, for example, weight, height, allergy information, and goals such as weight loss or muscle gain. In addition, favorite and disliked foods can also be entered. For example, consider a user who weighs 70 kg, is 170 cm tall, has no allergies, and wants to lose 5 kg in one month.

[0093] The terminal receives data entered by the user, formats it appropriately, and sends it to the server. Here, the input data is serialized in JSON format or similar, and a secure communication method (such as HTTPS) is used.

[0094] The server then uses the generative AI model to analyze the received user data. The server sends the generative AI model a prompt like this:

[0095] User Profile:

[0096] Weight: 70kg

[0097] Height: 170cm

[0098] Allergies: None

[0099] Food preferences: Likes vegetables, dislikes fatty foods

[0100] Goal: Lose 5kg in 1 month

[0101] Output format:

[0102] Please suggest a daily meal plan, recipes, and exercise menu.

[0103] The generative AI model generates the optimal health plan (meal recipes and exercise menu) for the user. For example, it suggests a green smoothie for breakfast, a chicken breast salad for lunch, and tofu steak for dinner. It also generates an exercise menu based on vital sensor data, such as 30 minutes of running, 3 sets of 15 push-ups, and 3 sets of 20 squats every day.

[0104] The generated plan is sent from the server to the device, which displays it to the user. The user can then review the provided meal and training menu and put it into practice. The device also synchronizes data from vital sensors in real time to record the user's progress.

[0105] The user inputs their diet and exercise progress into the application, and this feedback data is sent from the device to the server. The server then analyzes this feedback data and adjusts the diet and training menu accordingly based on the user's progress.

[0106] This allows users to receive a personalized health plan based on their health status and goals, supporting continuous health management. The system synchronizes data in real time and utilizes generative AI models to provide users with the optimal health promotion plan.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1:

[0109] Users launch the application using a smartphone or tablet device and input their weight, height, allergy information, health goals such as weight loss or muscle gain, and favorite and disliked foods. This input data is later analyzed by the server, so accurate input is required.

[0110] Input: User's health status (weight, height, allergy information, etc.) and health goals

[0111] Output: Formatted user data

[0112] Step 2:

[0113] The terminal receives data entered by the user and converts it into an appropriate format, for example, serializing the input data into JSON format and preparing it for transmission to the server, while also checking the integrity of the data.

[0114] Input: Raw data entered by the user

[0115] Output: User data in JSON format

[0116] Step 3:

[0117] The terminal sends the formatted data to the server via a secure communication method (e.g., HTTPS), and receives a response from the server to confirm whether the communication was successful.

[0118] Input: User data in JSON format

[0119] Output: Data transmission result to server (success / failure)

[0120] Step 4:

[0121] The server analyzes the received user data and generates prompts for the generative AI model, which includes the user's health status and goals, and the generative AI model then generates an optimal health plan based on this.

[0122] Input: User data received by the server

[0123] Output: Prompt sentence to the generative AI model

[0124] Step 5:

[0125] The generative AI model generates optimal meal and training menus based on prompts, including specific ingredients, cooking instructions, nutritional information, exercise types, loads, repetitions, and reference video links.

[0126] Input: Prompt sentence for generative AI model

[0127] Output: A health plan (meal menu and training menu) that is optimal for the user

[0128] Step 6:

[0129] The server sends the generated health plan to the terminal in an appropriate format such as JSON, where the server checks the integrity of the data and ensures that the terminal receives it correctly.

[0130] Input: Health plan from a generative AI model

[0131] Output: Health plan transmission result to the device (success / failure)

[0132] Step 7:

[0133] The device analyzes the health plan received from the server and visually displays it to the user. The user can then check the provided meal and training menus on the application screen and put them into practice.

[0134] Input: Health plan received from the server

[0135] Output: Health plan user interface display

[0136] Step 8:

[0137] Users enter their diet and exercise progress into the application, which also syncs vital sensor data in real time to record their progress, including heart rate, steps taken, and calories burned.

[0138] Input: User's diet and exercise progress data, vital sensor data

[0139] Output: Formatted feedback data

[0140] Step 9:

[0141] The device sends the user's feedback data to a server, which then analyzes the data and adjusts the user's diet and training accordingly based on their progress, enabling the provision of a continuous, personalized health plan.

[0142] Input: User feedback data

[0143] Output: Coordinated Health Plan

[0144] (Application example 1)

[0145] 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."

[0146] While traditional health management systems suggest meal and training menus based on a user's individual health status and goals, they lack the ability to order specific meals. This requires users to purchase ingredients and cook the meals themselves to achieve the suggested menus, making them time-consuming and impractical. Furthermore, the lack of feedback and subsequent adjustments based on the user's individual progress makes effective health management in real time difficult.

[0147] 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.

[0148] In this invention, the server includes an input means for inputting the user's health condition and goals, a transmission means for transmitting the user data received from the input means to the server, an analysis means for the server to analyze the user data and calculate individual nutrient needs, and a generation means for generating appropriate meal menus and training menus based on the analysis results. This makes it possible to propose and directly order individualized meal menus according to the user's health condition and goals, thereby realizing efficient health management.

[0149] An "input means" is a device or method for a user to input information such as their health status and goals.

[0150] The "transmitting means" is a device or method for transmitting the user data received from the input means to the server.

[0151] The "analysis means" refers to a device or method by which the server analyzes user data and calculates the nutritional and exercise information required by each individual user.

[0152] The "generation means" is a device or method for generating appropriate meal menus and training menus based on the analysis results.

[0153] The "output means" is a device or method for transmitting the menu generated by the generating means to a terminal.

[0154] The "display means" is a device or method by which the terminal displays the menu to the user.

[0155] A "feedback means" is a device or method for inputting the progress of the diet and exercise a user has undertaken.

[0156] The "ordering means" refers to a device or method that allows a user to place an order with a service that provides the proposed meal menu as is.

[0157] This invention is a system that provides a personalized health promotion plan based on a user's health status and goals. The system transmits data entered by the user to a server, which analyzes the data to generate an optimal nutrition plan and training menu, which are then provided to the user via a terminal. A specific embodiment of this system is described below.

[0158] System configuration

[0159] 1. User Roles

[0160] Users can launch the application on their smartphone or tablet and input their health status and goals, such as weight, height, allergy information, and goals such as weight loss or muscle gain. They can also input their favorite and least favorite foods.

[0161] 2. Role of the terminal

[0162] The terminal is responsible for receiving data entered by the user and transmitting it to the server, where it is properly formatted and sent to the server via secure communications.

[0163] 3. Server Roles

[0164] The server analyzes the received user data and uses the generative AI to calculate the nutritional needs of each individual. This creates a personalized nutrition plan based on the user's health status and goals. The server also accesses a nutrition database to select ingredients that are appropriate for the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. This recipe includes specific amounts of ingredients, cooking instructions, and the nutritional value of the dish.

[0165] Similarly, when it comes to training menus, the server analyzes vital sensor data to evaluate the user's athletic ability and muscle mass. Based on the evaluation results, the generation AI creates an appropriate training menu. This menu includes specific exercise steps, load, number of repetitions, and reference video links. This generated menu information is sent from the server to the device.

[0166] 4. Terminal Processing

[0167] The device receives the data sent from the server and displays it to the user. The user can then check and follow the cooking recipes and training menus provided through the application. The device also periodically syncs data from vital sensors and records the user's progress.

[0168] 5. User Feedback

[0169] Users input their diet and exercise progress into the application. They can then place an order from the service, which provides the suggested meal menu as is. It is also possible to automatically synchronize vital sensor values ​​with the application. The feedback data is sent from the device to the server and analyzed again. This cycle allows the diet and training menu to be adjusted appropriately according to the user's progress.

[0170] Specific examples

[0171] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The server analyzes the user's current weight, height, allergies, food preferences, etc., and generates a low-calorie, nutritionally balanced meal menu. It also calculates basal metabolic rate and calories burned during exercise based on data from vital sensor data, and provides a menu of moderate-intensity aerobic exercise and muscle training that matches these.

[0172] Prompt Sentence Examples

[0173] Create a daily meal plan based on your health and goals.

[0174] Current weight: 70kg

[0175] Height: 175cm

[0176] Goal: Lose 5kg in 1 month

[0177] Allergies: None

[0178] Favorite foods: Chicken, broccoli, yogurt

[0179] Disliked ingredients: None

[0180] In this way, the system of the present invention can flexibly respond to the health status and goals of each user and provide a personalized health promotion plan, which is useful for supporting the user's life in a healthier and more efficient manner.

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Step 1:

[0183] A user launches the application using a smartphone or tablet and inputs their health status and goals, including weight, height, allergies, food preferences, and goals such as weight loss or muscle gain. This generates user input data.

[0184] Step 2:

[0185] The terminal receives the user's input data and sends it to the server in an appropriate format. The input data is formatted in JSON format or similar and sent to the server via a secure communication method (e.g., HTTPS). This transfers the data to the server.

[0186] Step 3:

[0187] The server analyzes the received user data and uses a generative AI model to calculate individual nutrient needs. In this step, the user data is converted into a prompt sentence and input into a generative AI model (e.g., GPT-3 (registered trademark)). The generative AI model performs analysis based on the prompt sentence and outputs information on required nutrients and exercise.

[0188] Step 4:

[0189] The server generates appropriate meal and training menus based on the analysis results. The server accesses a nutritional database and selects ingredients that are appropriate for the user's nutritional needs. It also analyzes data from vital sensors to evaluate the user's athletic ability and muscle mass. This allows it to generate specific menus and training plans.

[0190] Step 5:

[0191] The server sends the generated menu to the terminal, where it is properly formatted and sent to the terminal via secure communication means, thereby transferring the menu information to the terminal.

[0192] Step 6:

[0193] The device displays the received data to the user. The provided cooking recipes and training menus can be viewed on the application screen. The user can then refer to the specific steps for carrying out these menus.

[0194] Step 7:

[0195] The user orders the suggested meal menu from the food delivery service. The user selects the suggested meal menu from the terminal and completes the order procedure with the food delivery service. The necessary ingredients and food are then delivered to the user's home.

[0196] Step 8:

[0197] The user inputs the progress of their diet and exercise, and the device sends it to the server. The user inputs the details of their exercise into the application, and the device sends it to the server. This transfers the progress data to the server.

[0198] Step 9:

[0199] The server analyzes the progress data and generates new menus and training plans as appropriate. The server then analyzes the vital sensor data again and generates new menus and plans based on the user's progress. This allows the user's health promotion plan to be updated as appropriate.

[0200] Through these steps, a personalized health promotion plan is provided based on the user's health condition and goals, enabling the user to effectively manage their health.

[0201] 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.

[0202] This invention is a system that provides an optimal health promotion plan based on the user's health condition, goals, and even emotional state. Below, we will explain the program of this system and introduce its operation with specific examples.

[0203] Specific system construction

[0204] 1. User Roles

[0205] A user opens a health management application on a smartphone or tablet. Within the application, the user enters their basic information, health goals (e.g., weight loss, muscle gain, etc.), food preferences, allergy information, etc. The user can also communicate their emotions by inputting voice and facial expressions using a microphone and camera. Emotions can also be input using the text chat function.

[0206] 2. Role of the terminal

[0207] The device receives basic data and health goals entered by the user, as well as voice, facial expressions, and text data, and then appropriately formats and transmits the data to the server. This data includes weight, height, goals, food preferences, allergy information, and the user's emotional state.

[0208] 3. Server Roles

[0209] The server receives user data sent from the device. The server first analyzes basic data and health goals, and uses generative AI to calculate the appropriate nutrients for each user. Next, the server's evaluation means analyzes vital data and evaluates athletic ability and muscle mass.

[0210] Furthermore, the server uses an emotion engine to analyze emotions from the user's voice, facial expressions, and text data, and uses this emotional information as auxiliary data for the generative AI to calculate nutritional intake and evaluate athletic ability.

[0211] 4. Create a meal and training menu

[0212] The server selects appropriate ingredients based on the analysis results, and the generative AI creates a cooking recipe tailored to the user based on the nutrition database and emotional data. The recipe includes ingredients, specific amounts, cooking instructions, and nutritional information.

[0213] Similarly, training menus are generated based on vital sensor data and emotional data, and include exercise instructions, load, number of repetitions, and reference video links.

[0214] The generated menu information is transmitted from the server to the terminal.

[0215] 5. Terminal Processing

[0216] The device receives the data sent from the server and displays it to the user. The user can then use the application to check the provided cooking recipes and training menus and follow the instructions. The device also periodically syncs data from vital sensors to record the user's progress.

[0217] 6. User Feedback

[0218] Users input their diet and exercise progress into the application. The input data is saved on the device as feedback and sent to the server. Users can also input emotional information using voice, facial expressions, and text.

[0219] 7. Reanalysis and Adjustment

[0220] The server reanalyzes the received progress data and emotional information, regenerates meal and training menus as needed, and sends the improved plans to the device, providing a flexible health promotion plan that also takes the user's emotional state into account.

[0221] Introduction of specific examples

[0222] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The system analyzes the user's emotional state each day (e.g., if the stress level is high) and adjusts the menu accordingly. On days when stress is high, it will provide recipes using ingredients that have a relaxing effect and suggest light exercise menus.

[0223] In this way, the system can provide a personalized health promotion plan that takes into account not only the user's health status and goals, but also their emotional state.

[0224] The processing flow will be explained below.

[0225] Step 1:

[0226] Users turn on their smartphone or tablet and open a health management application. Within the application, they input their health status (weight, height, allergies, etc.) and goals (e.g., weight loss, muscle gain, etc.), as well as their favorite and least favorite foods.

[0227] Step 2:

[0228] Users can communicate their emotional state to the system by inputting voice and facial expressions using a microphone and camera, and can also input emotions using the text chat function.

[0229] Step 3:

[0230] The device receives data entered by the user (health status, goals, food preferences, allergy information, voice, facial expressions, text data), formats it appropriately, and sends it to the server.

[0231] Step 4:

[0232] The server receives user data sent from the device, analyzes it regarding health status and goals, and then uses generative AI to calculate appropriate nutrients.

[0233] Step 5:

[0234] The server then uses an emotion engine to analyze emotions from the user's voice, facial expressions, and text data. Based on the analysis results, the emotional information is used as auxiliary data for the generative AI.

[0235] Step 6:

[0236] The server accesses a nutrition database and selects appropriate ingredients based on the user's nutritional needs. Taking into account the user's emotional data, the generative AI creates a cooking recipe tailored to the user. The recipe includes ingredients, specific serving sizes, cooking instructions, and nutritional information.

[0237] Step 7:

[0238] The server analyzes data from vital sensors to evaluate athletic ability and muscle mass. Taking emotional data into account, the generative AI creates an appropriate training menu. The menu includes exercise instructions, load, number of repetitions, and links to reference videos.

[0239] Step 8:

[0240] The server sends the generated cooking recipes and training menus to the terminal.

[0241] Step 9:

[0242] The device receives the data sent from the server and displays it to the user, who can then use the application to check and implement the provided cooking recipes and training menus.

[0243] Step 10:

[0244] The user inputs the progress of their diet and exercise into the application. They also update their emotional information by inputting voice and facial expressions. This data is stored on the device.

[0245] Step 11:

[0246] The terminal transmits the progress data and updated emotion information to the server.

[0247] Step 12:

[0248] The server reanalyzes the received progress data and emotional information, regenerates meal and training menus as needed, and sends the improved plans to the device.

[0249] Step 13:

[0250] The terminal receives the regenerated plan and displays it to the user, who then proceeds with the next execution based on it.

[0251] The above are the specific processing steps for the user, the terminal, and the server. This system provides an optimal personalized health promotion plan that takes into account the user's health condition, goals, and daily emotional state.

[0252] Example 2

[0253] 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."

[0254] In modern society, it is important to create effective health promotion plans tailored to individual health conditions and goals. However, these plans require the consideration of a wide range of individual data, making it particularly difficult to reflect the user's emotional state. Furthermore, there is a need for real-time feedback on whether the menus and training plans provided are appropriate for the user's current condition, and adjustments should be made accordingly. This will enable more personalized health management.

[0255] 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.

[0256] In this invention, the server includes input means for inputting the user's health condition and goals, transmission means for transmitting the user data received from the input means, analysis means for analyzing the user data and calculating each individual's required nutrients, emotion analysis means for analyzing the user's voice, facial expression, and text data and evaluating their emotional state, generation means for generating appropriate meal and training menus based on the analysis results and their emotional state, output means for transmitting the generated menus to the terminal, display means for the terminal to display the menus to the user, and feedback means for inputting the user's progress in diet and exercise. This makes it possible to provide a personalized health promotion plan that takes into account the user's individual health condition and emotional state.

[0257] "Input means" refers to a device or function that allows a user to input their health status and goals.

[0258] "Transmission means" refers to a device or function for transmitting data received from a user to a server.

[0259] "Analysis means" refers to a device or function for analyzing received user data and calculating individual required nutrients.

[0260] "Emotion analysis means" refers to a device or function for analyzing a user's voice, facial expression, and text data to evaluate their emotional state.

[0261] "Generation means" refers to a device or function for generating appropriate meal menus and training menus based on the analysis results and emotional state.

[0262] The "output means" refers to a device or function for transmitting the menu generated by the generation means to the terminal.

[0263] "Display means" refers to a device or function for displaying the transmitted menu to the user.

[0264] "Feedback means" refers to a device or function that allows a user to input progress on diet and exercise.

[0265] "Vital data" refers to data containing the user's biological information, such as heart rate and calorie consumption.

[0266] "Synchronization means" refers to a device or function for synchronizing data from a user's vital sensors with a terminal.

[0267] This invention is a system that provides an optimal health promotion plan based on a user's health status, goals, and even emotional state. This system is built around a health management application that uses a smartphone or tablet device, and related server-side processing.

[0268] User Roles

[0269] First, the user launches the health management application on their smartphone or tablet. Within the app, they enter the following information:

[0270] Basic information (e.g. weight, height)

[0271] Health goals (e.g., lose 5 kg in one month)

[0272] Food preferences (e.g., mainly Japanese food, lots of vegetables)

[0273] Allergy information (e.g., peanut allergy)

[0274] Emotional information (e.g., voice input via microphone, facial expression recognition via camera, or text chat)

[0275] Device Role

[0276] The terminal takes this information entered by the user and formats the data as follows:

[0277] Basic information, health goals, food preferences, allergy information: formatted in JSON format

[0278] Emotional information: Analyzes audio and image data and converts them into numerical values ​​or text

[0279] The formatted data is sent to the server using the HTTPS protocol.

[0280] Server Roles

[0281] The server receives the data sent from the device and analyzes it through the following process:

[0282] Store basic information and health goals in a database

[0283] Uses generative AI models to calculate appropriate nutrients based on health goals

[0284] Analyze vital data (e.g., heart rate, calorie consumption) to evaluate athletic ability and muscle mass

[0285] Use an emotion engine to analyze emotions from voice, facial expressions, and text data and store them as numerical values.

[0286] Examples of prompts to input to a generative AI model include:

[0287] "A 30-year-old, 170cm tall, 70kg person wants to lose 5kg in one month. Their stress level is high. Please provide them with an appropriate diet and exercise plan."

[0288] Menu generation

[0289] The server generates the following information based on the analysis:

[0290] Cooking recipes: ingredients, specific amounts, cooking instructions, nutritional information

[0291] Training plan: Exercise procedure, load, number of repetitions, reference video link

[0292] The system takes emotional data into consideration and creates a plan that includes foods with a relaxing effect and light exercise. The generated information is sent to the device in JSON format.

[0293] Displaying data on the device

[0294] The device receives the meal and training menu sent from the server and displays it to the user as follows:

[0295] The recipe screen displays the name of the dish, ingredients, serving size, cooking instructions, and nutritional information in a list format.

[0296] The training screen displays the exercise name, load, number of repetitions, and a link to a reference video.

[0297] Based on this information, users can put the provided meal recipes and training menus into practice.

[0298] User Feedback

[0299] The user then re-enters their diet and exercise progress into the application using checkboxes, text, voice input, facial expression recognition, etc. This feedback data is also sent back to the server, which then re-analyzes it.

[0300] Reanalysis and adjustments

[0301] The server then analyzes the received feedback data and generates new diet and training plans as needed, providing an optimal health promotion plan tailored to the individual user's health and emotional state.

[0302] As described above, this system provides more personalized health management for users, and supports them effectively and flexibly.

[0303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0304] Step 1:

[0305] The user launches the health management application using a smartphone or tablet device and enters the following information within the application:

[0306] Basic information (e.g. weight, height)

[0307] Health goals (e.g., lose 5 kg in one month)

[0308] Food preferences (e.g., mainly Japanese food, lots of vegetables)

[0309] Allergy information (e.g., peanut allergy)

[0310] Emotional information (e.g., voice input via microphone, facial expression recognition via camera, or text chat)

[0311] Input: User's individual data (basic information, health goals, food preferences, allergy information, emotional information)

[0312] Output: None (data entry complete)

[0313] Step 2:

[0314] The terminal takes the information entered by the user and formats the data as follows:

[0315] Basic information, health goals, food preferences, allergy information: formatted in JSON format

[0316] Emotional information: Analyzes audio and image data and converts them into numerical values ​​or text

[0317] Input: Individual data entered by the user

[0318] Output: Formatted data (JSON format)

[0319] Step 3:

[0320] The terminal sends the formatted data to the server using the HTTPS protocol.

[0321] Input: Formatted data (JSON format)

[0322] Output: Data sent to the server

[0323] Step 4:

[0324] The server receives the data sent from the device and performs the following processes:

[0325] Store basic information and health goals in a database

[0326] Uses generative AI models to calculate appropriate nutrients based on health goals

[0327] Analyze vital data (e.g., heart rate, calorie consumption) to evaluate athletic ability and muscle mass

[0328] Use an emotion engine to analyze emotions from voice, facial expressions, and text data and store them as numerical values.

[0329] Input: Data sent from the terminal

[0330] Output: Analysis results (nutrient calculation, vital data evaluation, emotion analysis results)

[0331] Step 5:

[0332] The server generates the following information based on the analysis:

[0333] Cooking recipes: ingredients, specific amounts, cooking instructions, nutritional information

[0334] Training plan: Exercise procedure, load, number of repetitions, reference video link

[0335] Input: Analysis results

[0336] Output: Generated menu information (cooking recipes, training plans)

[0337] Step 6:

[0338] The server sends the generated menu information to the terminal in JSON format.

[0339] Input: Generated menu information (cooking recipes, training plans)

[0340] Output: Menu information sent to the device

[0341] Step 7:

[0342] The terminal receives the menu information sent from the server and displays it to the user as follows:

[0343] The recipe screen displays the name of the dish, ingredients, serving size, cooking instructions, and nutritional information in a list format.

[0344] The training screen displays the exercise name, load, number of repetitions, and a link to a reference video.

[0345] Input: Menu information sent from the server

[0346] Output: Menu information displayed to the user

[0347] Step 8:

[0348] Users can enter their progress in diet and exercise into the application. For example, they can record their dietary achievements and changes in their physical condition using text or check boxes, and provide feedback using voice or facial expressions.

[0349] Input: User progress data (diet and exercise achievements and feedback)

[0350] Output: Feedback data input completed

[0351] Step 9:

[0352] The terminal transmits the feedback data input by the user to the server.

[0353] Input: Feedback data

[0354] Output: Feedback data sent to the server

[0355] Step 10:

[0356] The server then re-analyzes the received feedback data and generates new diet and training plans as needed, again involving generative AI models and sentiment analysis.

[0357] Input: Feedback data

[0358] Output: Revised Health Promotion Plan

[0359] As described above, the program of this system performs specific data processing and calculation for each step, and provides the user with an optimal health promotion plan.

[0360] (Application example 2)

[0361] 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."

[0362] Modern health management systems focus on providing dietary and training menus tailored to the user's health status and goals, but lack comprehensive support that takes into account the user's emotional state. In particular, it is difficult for physical stores to provide real-time product recommendations tailored to the user's current health and emotional state. This makes it difficult for users to easily find the products that best suit them, leading to ineffective health management.

[0363] The identification process by the identification 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 emotion analysis means that analyzes the emotional state of the user in real time and proposes product information according to the emotional state, and a presentation means that provides product information in the store. This allows the user to receive product proposals in real time in the physical store that are tailored to their health condition and emotional state.

[0364] Definitions of important words

[0365] A "user" is someone who inputs information such as health status, goals, and emotional state.

[0366] "Health status" refers to the user's current physical condition.

[0367] A "goal" is a health-related objective that a user wishes to achieve.

[0368] "Input means" refers to a method or device that allows a user to input information such as health status and goals.

[0369] The "transmission means" refers to a method or device for transmitting input user data to a server.

[0370] A "server" is a device or system that receives and analyzes user data and provides the necessary information.

[0371] The "analysis means" refers to a method or device by which the server analyzes user data and calculates the necessary information.

[0372] The "generation means" refers to a method or device that generates an appropriate menu based on the analysis results.

[0373] "Output means" refers to a method or device for transmitting the generated menu to a terminal.

[0374] A "terminal" is a device used by a user, such as a smartphone or tablet.

[0375] "Display means" refers to a method or device by which the terminal displays a menu to the user.

[0376] "Feedback means" refers to a method or device for inputting the progress of the user's diet and exercise.

[0377] "Emotional state" refers to the user's mental and emotional state.

[0378] An "emotion analysis means" is a method or device for analyzing a user's emotional state in real time.

[0379] "Presentation means" refers to a method or device for providing product information within a store.

[0380] "Product information" is information about products that are suggested based on the user's health condition and emotional state.

[0381] The "synchronization means" is a method or device for matching data from vital sensors between the terminal and the server.

[0382] MODE FOR CARRYING OUT THE INVENTION

[0383] The system embodying the present invention provides a personalized health shopping assistant using smart glasses. The specific system construction method and operation will be described below.

[0384] System Configuration

[0385] 1. Hardware and Software

[0386] Smart Glasses: Use common smart glasses (e.g., Google® Glass®, Vuzix Blade).

[0387] Backend server: Uses cloud services (e.g., AWS (registered trademark) EC2, Amazon RDS) to deploy generative AI models (e.g., TENSORFLOW (registered trademark)).

[0388] Front-end app: Uses a smartphone application (e.g., ANDROID (registered trademark) / iOS application, React Native).

[0389] Sensor devices: Use common vital sensors such as heart rate monitors and blood pressure monitors.

[0390] Data analysis tools: Use Python, Pandas, Scikit-learn, etc.

[0391] System Processing

[0392] 2. Data Entry

[0393] Through a smartphone application, users input their health status, health goals, food preferences, allergy information, etc. This data is then synced to the smart glasses via Bluetooth.

[0394] 3. Server Processing

[0395] The server receives user data and analyzes it using a generative AI model. The analysis includes the user's health status, vital data, and emotional state. The generative AI model calculates optimal nutrients for each individual and generates appropriate meal and training menus.

[0396] 4. Emotion analysis

[0397] The smart glasses use a built-in camera and microphone to capture the user's facial expressions and voice in real time, and then analyze them using an emotion analysis engine. The analysis results are sent to a server and used to adjust the health promotion plan.

[0398] 5. Product proposal

[0399] When a user approaches a particular product in a store, the smart glasses' display will display information about the product and its fit with health goals, such as, "This spinach is rich in vitamin C, which helps relieve stress."

[0400] Specific examples

[0401] If a user is looking to "relieve stress," the smart glasses will display a message while browsing vegetables in a store saying, "This banana is rich in potassium, which helps to relieve fatigue," allowing users to quickly find products that fit their health goals.

[0402] Prompt Sentence Examples

[0403] For example, you can use the following prompt sentences in your generative AI model:

[0404] User information: Age 35, Gender female, Weight 60kg, Height 165cm

[0405] Goal: Lose 3kg in 2 months and relieve stress

[0406] Food preferences: Vegetable-loving, gluten-free

[0407] Emotional state: High stress level (facial recognition), low audio tension

[0408] Recipe provided:

[0409] This user is recommended a banana and spinach smoothie, which is rich in potassium and vitamin C. Generate the following recipe:

[0410] (Banana and spinach smoothie)

[0411] material:

[0412] 1 banana

[0413] 1 cup spinach

[0414] 200ml almond milk

[0415] 1 teaspoon honey

[0416] Instructions:

[0417] 1. Cut the banana into small pieces.

[0418] 2. Add to blender along with spinach.

[0419] 3. Add the almond milk and blend until smooth.

[0420] 4. Add honey and blend again.

[0421] 5. Chill before drinking.

[0422] This invention allows users to receive product suggestions in real time within a physical store that are tailored to their health and emotional state, enabling them to manage their health more effectively.

[0423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0424] Program processing steps

[0425] Step 1:

[0426] Data Entry

[0427] A user inputs data such as health status, health goals, food preferences, and allergy information through a smartphone application. This data includes, for example, weight, height, and goals (e.g., weight loss, muscle gain). This input data is synchronized to the smart glasses via Bluetooth. The input data is sent from the smartphone application to a server by a transmission means and stored on the server.

[0428] Step 2:

[0429] Data analysis on the server

[0430] The server analyzes the received user data. What is particularly important here is the use of a generative AI model. The generative AI model analyzes the user's basic data (e.g., weight, height, goals) and calculates the optimal nutrients for each individual. It receives the user's basic data as input and outputs data that is useful for nutrient balance and weight management. The analysis results are stored on the server for use in the next step.

[0431] Step 3:

[0432] Emotion analysis

[0433] The device (smart glasses) uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. This data is sent to a server via an emotion analysis engine, which analyzes the user's emotional state. It receives the user's facial and voice data as input and outputs their emotional state (e.g., stress level, elation, depression). The analysis results of the emotional state are used as auxiliary data for the generative AI model.

[0434] Step 4:

[0435] Meal and training menu generation

[0436] The server uses the generative AI model to generate appropriate meal and training menus based on the analysis results. Specifically, it references a nutrition database and automatically generates optimal cooking recipes and exercise plans for the user. It receives the nutrition database and the user's analysis results as input, and outputs meal and exercise menus. The generated menu information is stored on the server.

[0437] Step 5:

[0438] Sending and displaying menu information

[0439] The server transmits the generated menu information to a terminal (smartphone or smart glasses) via a transmission means. The terminal displays the received menu information to the user using a display means. This allows the user to refer to and carry out the suggested meal menu or training menu. The menu information is received as input from the server and displayed via a user interface.

[0440] Step 6:

[0441] Real-time product information display

[0442] The terminal (smart glasses) uses RFID tags and beacons to display product information in real time within the store. When a user approaches a particular product, the smart glasses display information about the product and its suitability for health goals. For example, the glasses may display information such as, "This spinach is rich in vitamin C, which helps relieve stress." The glasses receive data from beacons and RFID tags as input and display product information.

[0443] Step 7:

[0444] Feedback collection

[0445] The user inputs their diet and training progress as feedback via a smartphone application. The data input through the feedback means is sent back to the server and stored on the server. This data is used for the next adjustment of the health plan. The system receives the user's feedback data as input and sends the progress to the server.

[0446] Step 8:

[0447] Reanalysis and plan adjustments

[0448] The server re-analyzes the feedback data and emotional data and re-generates the meal and training menu as necessary. This makes it possible to provide an optimal plan that takes into account the user's health and emotional state. It receives the feedback data and emotional data as input and re-outputs the adjusted menu.

[0449] 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.

[0450] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0451] 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.

[0452] [Second embodiment]

[0453] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0454] 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.

[0455] 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).

[0456] 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.

[0457] 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.

[0458] 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).

[0459] 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. 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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."

[0465] This invention is a system that provides an optimal health promotion plan based on the user's health condition and goals. Below, we will explain the program of this system and introduce its operation with specific examples.

[0466] Specific system construction

[0467] 1. User Roles

[0468] Users can launch the application on their smartphone or tablet and input their health status and goals, such as weight, height, allergy information, and goals such as weight loss or muscle gain. They can also input their favorite and least favorite foods.

[0469] 2. Role of the terminal

[0470] The terminal (smartphone or tablet device) receives data entered by the user and transmits it to the server, where it is properly formatted and sent via secure communication.

[0471] 3. Server Roles

[0472] The server analyzes the received user data and uses generative AI to calculate the nutritional needs of each individual, creating a personalized nutrition plan based on the user's health status and goals.

[0473] The server then accesses a nutrition database to select ingredients that meet the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. This recipe includes specific amounts of ingredients, cooking instructions, and the nutritional value of the dish.

[0474] Similarly, for training menus, the server analyzes data from vital sensors to evaluate the user's athletic ability and muscle mass. Based on the evaluation results, the generative AI creates an appropriate training menu. This menu includes specific exercise steps, loads, number of repetitions, and reference video links.

[0475] The generated menu information is transmitted from the server to the terminal.

[0476] 4. Terminal Processing

[0477] The device receives the data sent from the server and displays it to the user. The user can then check and follow the provided cooking recipes and training menus through the application. The device also periodically syncs data from vital sensors and records the user's progress.

[0478] 5. User Feedback

[0479] Users input their diet and exercise progress into the application. They can also automatically synchronize vital sensor values ​​with the application. The feedback data is sent from the device to a server and analyzed again. This cycle allows the device to adjust diet and training menus appropriately based on the user's progress.

[0480] Introduction of specific examples

[0481] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The server analyzes the user's current weight, height, allergies, food preferences, etc., and generates a low-calorie, nutritionally balanced meal menu. It also calculates basal metabolic rate and calories burned during exercise based on data from vital sensor data, and provides a menu of moderate-intensity aerobic exercise and muscle training that matches these.

[0482] In this way, the system of the present invention can flexibly respond to the health status and goals of each user and provide a personalized health promotion plan.

[0483] The processing flow will be explained below.

[0484] Step 1:

[0485] Users turn on their smartphone or tablet and open a health management application. Within the application, they enter their basic information and health goals (e.g., weight loss, muscle gain, etc.), as well as food preferences and allergy information.

[0486] Step 2:

[0487] The device receives data entered by the user, formats it appropriately, and sends it to the server, including weight, height, goals, food preferences, and allergy information.

[0488] Step 3:

[0489] The server receives user data sent from the device, analyzes this data, and uses generative AI to calculate the appropriate nutrients for each user.

[0490] Step 4:

[0491] The server accesses a nutrition database and selects appropriate ingredients based on the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. The recipe includes ingredients, specific amounts, cooking instructions, and nutritional information.

[0492] Step 5:

[0493] The server analyzes the user's data obtained from vital sensors, assesses the user's athletic ability and muscle mass, and uses generative AI to create an appropriate training menu, including exercise procedures, loads, number of repetitions, and reference video links.

[0494] Step 6:

[0495] The server sends the generated cooking recipes and training menus to the terminal.

[0496] Step 7:

[0497] The device receives the data sent from the server and displays it to the user, who then checks the provided cooking recipes and training menus through the application and follows the instructions.

[0498] Step 8:

[0499] Users input their diet and exercise progress into the application, and the input data is stored on the device as feedback.

[0500] Step 9:

[0501] The device transmits the progress data entered by the user and the data from the vital sensors to the server.

[0502] Step 10:

[0503] The server re-analyzes the received progress data and vital sensor data, regenerates meal and training menus as needed based on the analysis results, and sends the improved plan to the device.

[0504] Step 11:

[0505] The terminal receives the regenerated plan and displays it to the user, who then proceeds with further execution based on it.

[0506] The above are the specific processing steps for the user, the terminal, and the server. This system efficiently provides the user with a meal and exercise plan that is optimal for their individual health goals.

[0507] Example 1

[0508] 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."

[0509] Conventional health management systems have struggled to provide appropriate meal and training menus based on individual users' health status and goals. They also lack the ability to synchronize vital data in real time, and they do not guarantee that generated plans are consistently updated based on the user's progress. This has led to problems that prevent users from fully utilizing their personalized, optimal health plans.

[0510] 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.

[0511] In this invention, the server includes an input means for inputting the user's health status and goals, a transmission means for transmitting the user data received from the input means to the server, an analysis means for the server to analyze the user data and calculate individual nutrient needs, a generation means for using a generative AI model to generate appropriate meal and training menus based on the analysis results, an output means for transmitting the menus generated by the generation means to a terminal, a display means for the terminal to display the menus to the user, a feedback means for inputting and providing feedback on the progress of the user's diet and exercise, and a synchronization means for synchronizing data from vital sensors in real time. This allows the user to obtain an optimal, personalized health promotion plan and realize specific and feasible health management.

[0512] "Input means" refers to a device or application software that allows a user to input their health status and goals.

[0513] The "transmission means" is a function or device that transmits user data received from the input means to the server.

[0514] The "analysis means" is a program or device that analyzes user data on the server and calculates the nutrients required for each user.

[0515] The "generation means" is a program or device that uses a generative AI model to generate appropriate meal and training menus based on the results of the analysis means.

[0516] "Output means" refers to a function or device that transmits the generated menu to the terminal.

[0517] The "display means" refers to a device or program for visually presenting the menu sent from the server to the user.

[0518] The "feedback means" is a device or program that allows the user to input the progress of their diet and exercise and transmit that information to the server.

[0519] The "synchronization means" is a function or device for receiving data from the vital sensors in real time and synchronizing it with the server.

[0520] The system of the present invention provides an optimal health promotion plan based on the user's health condition and goals. The following provides an overview of this system with specific examples.

[0521] First, a user launches the application on a smartphone or tablet and inputs their health status and goals. This includes, for example, weight, height, allergy information, and goals such as weight loss or muscle gain. In addition, favorite and disliked foods can also be entered. For example, consider a user who weighs 70 kg, is 170 cm tall, has no allergies, and wants to lose 5 kg in one month.

[0522] The terminal receives data entered by the user, formats it appropriately, and sends it to the server. Here, the input data is serialized in JSON format or similar, and a secure communication method (such as HTTPS) is used.

[0523] The server then uses the generative AI model to analyze the received user data. The server sends the generative AI model a prompt like this:

[0524] User Profile:

[0525] Weight: 70kg

[0526] Height: 170cm

[0527] Allergies: None

[0528] Food preferences: Likes vegetables, dislikes fatty foods

[0529] Goal: Lose 5kg in 1 month

[0530] Output format:

[0531] Please suggest a daily meal plan, recipes, and exercise menu.

[0532] The generative AI model generates the optimal health plan (meal recipes and exercise menu) for the user. For example, it suggests a green smoothie for breakfast, a chicken breast salad for lunch, and tofu steak for dinner. It also generates an exercise menu based on vital sensor data, such as 30 minutes of running, 3 sets of 15 push-ups, and 3 sets of 20 squats every day.

[0533] The generated plan is sent from the server to the device, which displays it to the user. The user can then review the provided meal and training menu and put it into practice. The device also synchronizes data from vital sensors in real time to record the user's progress.

[0534] The user inputs their diet and exercise progress into the application, and this feedback data is sent from the device to the server. The server then analyzes this feedback data and adjusts the diet and training menu accordingly based on the user's progress.

[0535] This allows users to receive a personalized health plan based on their health status and goals, supporting continuous health management. The system synchronizes data in real time and utilizes generative AI models to provide users with the optimal health promotion plan.

[0536] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0537] Step 1:

[0538] Users launch the application using a smartphone or tablet device and input their weight, height, allergy information, health goals such as weight loss or muscle gain, and favorite and disliked foods. This input data is later analyzed by the server, so accurate input is required.

[0539] Input: User's health status (weight, height, allergy information, etc.) and health goals

[0540] Output: Formatted user data

[0541] Step 2:

[0542] The terminal receives data entered by the user and converts it into an appropriate format, for example, serializing the input data into JSON format and preparing it for transmission to the server, while also checking the integrity of the data.

[0543] Input: Raw data entered by the user

[0544] Output: User data in JSON format

[0545] Step 3:

[0546] The terminal sends the formatted data to the server via a secure communication method (e.g., HTTPS), and receives a response from the server to confirm whether the communication was successful.

[0547] Input: User data in JSON format

[0548] Output: Data transmission result to server (success / failure)

[0549] Step 4:

[0550] The server analyzes the received user data and generates prompts for the generative AI model, which includes the user's health status and goals, and the generative AI model then generates an optimal health plan based on this.

[0551] Input: User data received by the server

[0552] Output: Prompt sentence to the generative AI model

[0553] Step 5:

[0554] The generative AI model generates optimal meal and training menus based on prompts, including specific ingredients, cooking instructions, nutritional information, exercise types, loads, repetitions, and reference video links.

[0555] Input: Prompt sentence for generative AI model

[0556] Output: A health plan (meal menu and training menu) that is optimal for the user

[0557] Step 6:

[0558] The server sends the generated health plan to the terminal in an appropriate format such as JSON, where the server checks the integrity of the data and ensures that the terminal receives it correctly.

[0559] Input: Health plan from a generative AI model

[0560] Output: Health plan transmission result to the device (success / failure)

[0561] Step 7:

[0562] The device analyzes the health plan received from the server and visually displays it to the user. The user can then check the provided meal and training menus on the application screen and put them into practice.

[0563] Input: Health plan received from the server

[0564] Output: Health plan user interface display

[0565] Step 8:

[0566] Users enter their diet and exercise progress into the application, which also syncs vital sensor data in real time to record their progress, including heart rate, steps taken, and calories burned.

[0567] Input: User's diet and exercise progress data, vital sensor data

[0568] Output: Formatted feedback data

[0569] Step 9:

[0570] The device sends the user's feedback data to a server, which then analyzes the data and adjusts the user's diet and training accordingly based on their progress, enabling the provision of a continuous, personalized health plan.

[0571] Input: User feedback data

[0572] Output: Coordinated Health Plan

[0573] (Application example 1)

[0574] 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."

[0575] While traditional health management systems suggest meal and training menus based on a user's individual health status and goals, they lack the ability to order specific meals. This requires users to purchase ingredients and cook the meals themselves to achieve the suggested menus, making them time-consuming and impractical. Furthermore, the lack of feedback and subsequent adjustments based on the user's individual progress makes effective health management in real time difficult.

[0576] 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.

[0577] In this invention, the server includes an input means for inputting the user's health condition and goals, a transmission means for transmitting the user data received from the input means to the server, an analysis means for the server to analyze the user data and calculate individual nutrient needs, and a generation means for generating appropriate meal menus and training menus based on the analysis results. This makes it possible to propose and directly order individualized meal menus according to the user's health condition and goals, thereby realizing efficient health management.

[0578] An "input means" is a device or method for a user to input information such as their health status and goals.

[0579] The "transmitting means" is a device or method for transmitting the user data received from the input means to the server.

[0580] The "analysis means" refers to a device or method by which the server analyzes user data and calculates the nutritional and exercise information required by each individual user.

[0581] The "generation means" is a device or method for generating appropriate meal menus and training menus based on the analysis results.

[0582] The "output means" is a device or method for transmitting the menu generated by the generating means to a terminal.

[0583] The "display means" is a device or method by which the terminal displays the menu to the user.

[0584] A "feedback means" is a device or method for inputting the progress of the diet and exercise a user has undertaken.

[0585] The "ordering means" refers to a device or method that allows a user to place an order with a service that provides the proposed meal menu as is.

[0586] This invention is a system that provides a personalized health promotion plan based on a user's health status and goals. The system transmits data entered by the user to a server, which analyzes the data to generate an optimal nutrition plan and training menu, which are then provided to the user via a terminal. A specific embodiment of this system is described below.

[0587] System configuration

[0588] 1. User Roles

[0589] Users can launch the application on their smartphone or tablet and input their health status and goals, such as weight, height, allergy information, and goals such as weight loss or muscle gain. They can also input their favorite and least favorite foods.

[0590] 2. Role of the terminal

[0591] The terminal is responsible for receiving data entered by the user and transmitting it to the server, where it is properly formatted and sent to the server via secure communications.

[0592] 3. Server Roles

[0593] The server analyzes the received user data and uses the generative AI to calculate the nutritional needs of each individual. This creates a personalized nutrition plan based on the user's health status and goals. The server also accesses a nutrition database to select ingredients that are appropriate for the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. This recipe includes specific amounts of ingredients, cooking instructions, and the nutritional value of the dish.

[0594] Similarly, when it comes to training menus, the server analyzes vital sensor data to evaluate the user's athletic ability and muscle mass. Based on the evaluation results, the generation AI creates an appropriate training menu. This menu includes specific exercise steps, load, number of repetitions, and reference video links. This generated menu information is sent from the server to the device.

[0595] 4. Terminal Processing

[0596] The device receives the data sent from the server and displays it to the user. The user can then check and follow the cooking recipes and training menus provided through the application. The device also periodically syncs data from vital sensors and records the user's progress.

[0597] 5. User Feedback

[0598] Users input their diet and exercise progress into the application. They can then place an order from the service, which provides the suggested meal menu as is. It is also possible to automatically synchronize vital sensor values ​​with the application. The feedback data is sent from the device to the server and analyzed again. This cycle allows the diet and training menu to be adjusted appropriately according to the user's progress.

[0599] Specific examples

[0600] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The server analyzes the user's current weight, height, allergies, food preferences, etc., and generates a low-calorie, nutritionally balanced meal menu. It also calculates basal metabolic rate and calories burned during exercise based on data from vital sensor data, and provides a menu of moderate-intensity aerobic exercise and muscle training that matches these.

[0601] Prompt Sentence Examples

[0602] Create a daily meal plan based on your health and goals.

[0603] Current weight: 70kg

[0604] Height: 175cm

[0605] Goal: Lose 5kg in 1 month

[0606] Allergies: None

[0607] Favorite foods: Chicken, broccoli, yogurt

[0608] Disliked ingredients: None

[0609] In this way, the system of the present invention can flexibly respond to the health status and goals of each user and provide a personalized health promotion plan, which is useful for supporting the user's life in a healthier and more efficient manner.

[0610] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0611] Step 1:

[0612] A user launches the application using a smartphone or tablet and inputs their health status and goals, including weight, height, allergies, food preferences, and goals such as weight loss or muscle gain. This generates user input data.

[0613] Step 2:

[0614] The terminal receives the user's input data and sends it to the server in an appropriate format. The input data is formatted in JSON format or similar and sent to the server via a secure communication method (e.g., HTTPS). This transfers the data to the server.

[0615] Step 3:

[0616] The server analyzes the received user data and uses a generative AI model to calculate individual nutritional needs. In this step, the user data is converted into a prompt sentence and input into a generative AI model (e.g., GPT-3). The generative AI model analyzes the prompt sentence and outputs information on required nutrients and exercise.

[0617] Step 4:

[0618] The server generates appropriate meal and training menus based on the analysis results. The server accesses a nutritional database and selects ingredients that are appropriate for the user's nutritional needs. It also analyzes data from vital sensors to evaluate the user's athletic ability and muscle mass. This allows it to generate specific menus and training plans.

[0619] Step 5:

[0620] The server sends the generated menu to the terminal, where it is properly formatted and sent to the terminal via secure communication means, thereby transferring the menu information to the terminal.

[0621] Step 6:

[0622] The device displays the received data to the user. The provided cooking recipes and training menus can be viewed on the application screen. The user can then refer to the specific steps for carrying out these menus.

[0623] Step 7:

[0624] The user orders the suggested meal menu from the food delivery service. The user selects the suggested meal menu from the terminal and completes the order procedure with the food delivery service. The necessary ingredients and food are then delivered to the user's home.

[0625] Step 8:

[0626] The user inputs the progress of their diet and exercise, and the device sends it to the server. The user inputs the details of their exercise into the application, and the device sends it to the server. This transfers the progress data to the server.

[0627] Step 9:

[0628] The server analyzes the progress data and generates new menus and training plans as appropriate. The server then analyzes the vital sensor data again and generates new menus and plans based on the user's progress. This allows the user's health promotion plan to be updated as appropriate.

[0629] Through these steps, a personalized health promotion plan is provided based on the user's health condition and goals, enabling the user to effectively manage their health.

[0630] 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.

[0631] This invention is a system that provides an optimal health promotion plan based on the user's health condition, goals, and even emotional state. Below, we will explain the program of this system and introduce its operation with specific examples.

[0632] Specific system construction

[0633] 1. User Roles

[0634] A user opens a health management application on a smartphone or tablet. Within the application, the user enters their basic information, health goals (e.g., weight loss, muscle gain, etc.), food preferences, allergy information, etc. The user can also communicate their emotions by inputting voice and facial expressions using a microphone and camera. Emotions can also be input using the text chat function.

[0635] 2. Role of the terminal

[0636] The device receives basic data and health goals entered by the user, as well as voice, facial expressions, and text data, and then appropriately formats and transmits the data to the server. This data includes weight, height, goals, food preferences, allergy information, and the user's emotional state.

[0637] 3. Server Roles

[0638] The server receives user data sent from the device. The server first analyzes basic data and health goals, and uses generative AI to calculate the appropriate nutrients for each user. Next, the server's evaluation means analyzes vital data and evaluates athletic ability and muscle mass.

[0639] Furthermore, the server uses an emotion engine to analyze emotions from the user's voice, facial expressions, and text data, and uses this emotional information as auxiliary data for the generative AI to calculate nutritional intake and evaluate athletic ability.

[0640] 4. Create a meal and training menu

[0641] The server selects appropriate ingredients based on the analysis results, and the generative AI creates a cooking recipe tailored to the user based on the nutrition database and emotional data. The recipe includes ingredients, specific amounts, cooking instructions, and nutritional information.

[0642] Similarly, training menus are generated based on vital sensor data and emotional data, and include exercise instructions, load, number of repetitions, and reference video links.

[0643] The generated menu information is transmitted from the server to the terminal.

[0644] 5. Terminal Processing

[0645] The device receives the data sent from the server and displays it to the user. The user can then use the application to check the provided cooking recipes and training menus and follow the instructions. The device also periodically syncs data from vital sensors to record the user's progress.

[0646] 6. User Feedback

[0647] Users input their diet and exercise progress into the application. The input data is saved on the device as feedback and sent to the server. Users can also input emotional information using voice, facial expressions, and text.

[0648] 7. Reanalysis and Adjustment

[0649] The server reanalyzes the received progress data and emotional information, regenerates meal and training menus as needed, and sends the improved plans to the device, providing a flexible health promotion plan that also takes the user's emotional state into account.

[0650] Introduction of specific examples

[0651] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The system analyzes the user's emotional state each day (e.g., if the stress level is high) and adjusts the menu accordingly. On days when stress is high, it will provide recipes using ingredients that have a relaxing effect and suggest light exercise menus.

[0652] In this way, the system can provide a personalized health promotion plan that takes into account not only the user's health status and goals, but also their emotional state.

[0653] The processing flow will be explained below.

[0654] Step 1:

[0655] Users turn on their smartphone or tablet and open a health management application. Within the application, they input their health status (weight, height, allergies, etc.) and goals (e.g., weight loss, muscle gain, etc.), as well as their favorite and least favorite foods.

[0656] Step 2:

[0657] Users can communicate their emotional state to the system by inputting voice and facial expressions using a microphone and camera, and can also input emotions using the text chat function.

[0658] Step 3:

[0659] The device receives data entered by the user (health status, goals, food preferences, allergy information, voice, facial expressions, text data), formats it appropriately, and sends it to the server.

[0660] Step 4:

[0661] The server receives user data sent from the device, analyzes it regarding health status and goals, and then uses generative AI to calculate appropriate nutrients.

[0662] Step 5:

[0663] The server then uses an emotion engine to analyze emotions from the user's voice, facial expressions, and text data. Based on the analysis results, the emotional information is used as auxiliary data for the generative AI.

[0664] Step 6:

[0665] The server accesses a nutrition database and selects appropriate ingredients based on the user's nutritional needs. Taking into account the user's emotional data, the generative AI creates a cooking recipe tailored to the user. The recipe includes ingredients, specific serving sizes, cooking instructions, and nutritional information.

[0666] Step 7:

[0667] The server analyzes data from vital sensors to evaluate athletic ability and muscle mass. Taking emotional data into account, the generative AI creates an appropriate training menu. The menu includes exercise instructions, load, number of repetitions, and links to reference videos.

[0668] Step 8:

[0669] The server sends the generated cooking recipes and training menus to the terminal.

[0670] Step 9:

[0671] The device receives the data sent from the server and displays it to the user, who can then use the application to check and implement the provided cooking recipes and training menus.

[0672] Step 10:

[0673] The user inputs the progress of their diet and exercise into the application. They also update their emotional information by inputting voice and facial expressions. This data is stored on the device.

[0674] Step 11:

[0675] The terminal transmits the progress data and updated emotion information to the server.

[0676] Step 12:

[0677] The server reanalyzes the received progress data and emotional information, regenerates meal and training menus as needed, and sends the improved plans to the device.

[0678] Step 13:

[0679] The terminal receives the regenerated plan and displays it to the user, who then proceeds with the next execution based on it.

[0680] The above are the specific processing steps for the user, the terminal, and the server. This system provides an optimal personalized health promotion plan that takes into account the user's health condition, goals, and daily emotional state.

[0681] Example 2

[0682] 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."

[0683] In modern society, it is important to create effective health promotion plans tailored to individual health conditions and goals. However, these plans require the consideration of a wide range of individual data, making it particularly difficult to reflect the user's emotional state. Furthermore, there is a need for real-time feedback on whether the menus and training plans provided are appropriate for the user's current condition, and adjustments should be made accordingly. This will enable more personalized health management.

[0684] 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.

[0685] In this invention, the server includes input means for inputting the user's health condition and goals, transmission means for transmitting the user data received from the input means, analysis means for analyzing the user data and calculating each individual's required nutrients, emotion analysis means for analyzing the user's voice, facial expression, and text data and evaluating their emotional state, generation means for generating appropriate meal and training menus based on the analysis results and their emotional state, output means for transmitting the generated menus to the terminal, display means for the terminal to display the menus to the user, and feedback means for inputting the user's progress in diet and exercise. This makes it possible to provide a personalized health promotion plan that takes into account the user's individual health condition and emotional state.

[0686] "Input means" refers to a device or function that allows a user to input their health status and goals.

[0687] "Transmission means" refers to a device or function for transmitting data received from a user to a server.

[0688] "Analysis means" refers to a device or function for analyzing received user data and calculating individual required nutrients.

[0689] "Emotion analysis means" refers to a device or function for analyzing a user's voice, facial expression, and text data to evaluate their emotional state.

[0690] "Generation means" refers to a device or function for generating appropriate meal menus and training menus based on the analysis results and emotional state.

[0691] The "output means" refers to a device or function for transmitting the menu generated by the generation means to the terminal.

[0692] "Display means" refers to a device or function for displaying the transmitted menu to the user.

[0693] "Feedback means" refers to a device or function that allows a user to input progress on diet and exercise.

[0694] "Vital data" refers to data containing the user's biological information, such as heart rate and calorie consumption.

[0695] "Synchronization means" refers to a device or function for synchronizing data from a user's vital sensors with a terminal.

[0696] This invention is a system that provides an optimal health promotion plan based on a user's health status, goals, and even emotional state. This system is built around a health management application that uses a smartphone or tablet device, and related server-side processing.

[0697] User Roles

[0698] First, the user launches the health management application on their smartphone or tablet. Within the app, they enter the following information:

[0699] Basic information (e.g. weight, height)

[0700] Health goals (e.g., lose 5 kg in one month)

[0701] Food preferences (e.g., mainly Japanese food, lots of vegetables)

[0702] Allergy information (e.g., peanut allergy)

[0703] Emotional information (e.g., voice input via microphone, facial expression recognition via camera, or text chat)

[0704] Device Role

[0705] The terminal takes this information entered by the user and formats the data as follows:

[0706] Basic information, health goals, food preferences, allergy information: formatted in JSON format

[0707] Emotional information: Analyzes audio and image data and converts them into numerical values ​​or text

[0708] The formatted data is sent to the server using the HTTPS protocol.

[0709] Server Roles

[0710] The server receives the data sent from the device and analyzes it through the following process:

[0711] Store basic information and health goals in a database

[0712] Uses generative AI models to calculate appropriate nutrients based on health goals

[0713] Analyze vital data (e.g., heart rate, calorie consumption) to evaluate athletic ability and muscle mass

[0714] Use an emotion engine to analyze emotions from voice, facial expressions, and text data and store them as numerical values.

[0715] Examples of prompts to input to a generative AI model include:

[0716] "A 30-year-old, 170cm tall, 70kg person wants to lose 5kg in one month. Their stress level is high. Please provide them with an appropriate diet and exercise plan."

[0717] Menu generation

[0718] The server generates the following information based on the analysis:

[0719] Cooking recipes: ingredients, specific amounts, cooking instructions, nutritional information

[0720] Training plan: Exercise procedure, load, number of repetitions, reference video link

[0721] The system takes emotional data into consideration and creates a plan that includes foods with a relaxing effect and light exercise. The generated information is sent to the device in JSON format.

[0722] Displaying data on the device

[0723] The device receives the meal and training menu sent from the server and displays it to the user as follows:

[0724] The recipe screen displays the name of the dish, ingredients, serving size, cooking instructions, and nutritional information in a list format.

[0725] The training screen displays the exercise name, load, number of repetitions, and a link to a reference video.

[0726] Based on this information, users can put the provided meal recipes and training menus into practice.

[0727] User Feedback

[0728] The user then re-enters their diet and exercise progress into the application using checkboxes, text, voice input, facial expression recognition, etc. This feedback data is also sent back to the server, which then re-analyzes it.

[0729] Reanalysis and adjustments

[0730] The server then analyzes the received feedback data and generates new diet and training plans as needed, providing an optimal health promotion plan tailored to the individual user's health and emotional state.

[0731] As described above, this system provides more personalized health management for users, and supports them effectively and flexibly.

[0732] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0733] Step 1:

[0734] The user launches the health management application using a smartphone or tablet device and enters the following information within the application:

[0735] Basic information (e.g. weight, height)

[0736] Health goals (e.g., lose 5 kg in one month)

[0737] Food preferences (e.g., mainly Japanese food, lots of vegetables)

[0738] Allergy information (e.g., peanut allergy)

[0739] Emotional information (e.g., voice input via microphone, facial expression recognition via camera, or text chat)

[0740] Input: User's individual data (basic information, health goals, food preferences, allergy information, emotional information)

[0741] Output: None (data entry complete)

[0742] Step 2:

[0743] The terminal takes the information entered by the user and formats the data as follows:

[0744] Basic information, health goals, food preferences, allergy information: formatted in JSON format

[0745] Emotional information: Analyzes audio and image data and converts them into numerical values ​​or text

[0746] Input: Individual data entered by the user

[0747] Output: Formatted data (JSON format)

[0748] Step 3:

[0749] The terminal sends the formatted data to the server using the HTTPS protocol.

[0750] Input: Formatted data (JSON format)

[0751] Output: Data sent to the server

[0752] Step 4:

[0753] The server receives the data sent from the device and performs the following processes:

[0754] Store basic information and health goals in a database

[0755] Uses generative AI models to calculate appropriate nutrients based on health goals

[0756] Analyze vital data (e.g., heart rate, calorie consumption) to evaluate athletic ability and muscle mass

[0757] Use an emotion engine to analyze emotions from voice, facial expressions, and text data and store them as numerical values.

[0758] Input: Data sent from the terminal

[0759] Output: Analysis results (nutrient calculation, vital data evaluation, emotion analysis results)

[0760] Step 5:

[0761] The server generates the following information based on the analysis:

[0762] Cooking recipes: ingredients, specific amounts, cooking instructions, nutritional information

[0763] Training plan: Exercise procedure, load, number of repetitions, reference video link

[0764] Input: Analysis results

[0765] Output: Generated menu information (cooking recipes, training plans)

[0766] Step 6:

[0767] The server sends the generated menu information to the terminal in JSON format.

[0768] Input: Generated menu information (cooking recipes, training plans)

[0769] Output: Menu information sent to the device

[0770] Step 7:

[0771] The terminal receives the menu information sent from the server and displays it to the user as follows:

[0772] The recipe screen displays the name of the dish, ingredients, serving size, cooking instructions, and nutritional information in a list format.

[0773] The training screen displays the exercise name, load, number of repetitions, and a link to a reference video.

[0774] Input: Menu information sent from the server

[0775] Output: Menu information displayed to the user

[0776] Step 8:

[0777] Users can enter their progress in diet and exercise into the application. For example, they can record their dietary achievements and changes in their physical condition using text or check boxes, and provide feedback using voice or facial expressions.

[0778] Input: User progress data (diet and exercise achievements and feedback)

[0779] Output: Feedback data input completed

[0780] Step 9:

[0781] The terminal transmits the feedback data input by the user to the server.

[0782] Input: Feedback data

[0783] Output: Feedback data sent to the server

[0784] Step 10:

[0785] The server then re-analyzes the received feedback data and generates new diet and training plans as needed, again involving generative AI models and sentiment analysis.

[0786] Input: Feedback data

[0787] Output: Revised Health Promotion Plan

[0788] As described above, the program of this system performs specific data processing and calculation for each step, and provides the user with an optimal health promotion plan.

[0789] (Application example 2)

[0790] 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."

[0791] Modern health management systems focus on providing dietary and training menus tailored to the user's health status and goals, but lack comprehensive support that takes into account the user's emotional state. In particular, it is difficult for physical stores to provide real-time product recommendations tailored to the user's current health and emotional state. This makes it difficult for users to easily find the products that best suit them, leading to ineffective health management.

[0792] The identification process by the identification 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 emotion analysis means that analyzes the emotional state of the user in real time and proposes product information according to the emotional state, and a presentation means that provides product information in the store. This allows the user to receive product proposals in real time in the physical store that are tailored to their health condition and emotional state.

[0793] Definitions of important words

[0794] A "user" is someone who inputs information such as health status, goals, and emotional state.

[0795] "Health status" refers to the user's current physical condition.

[0796] A "goal" is a health-related objective that a user wishes to achieve.

[0797] "Input means" refers to a method or device that allows a user to input information such as health status and goals.

[0798] The "transmission means" refers to a method or device for transmitting input user data to a server.

[0799] A "server" is a device or system that receives and analyzes user data and provides the necessary information.

[0800] The "analysis means" refers to a method or device by which the server analyzes user data and calculates the necessary information.

[0801] The "generation means" refers to a method or device that generates an appropriate menu based on the analysis results.

[0802] "Output means" refers to a method or device for transmitting the generated menu to a terminal.

[0803] A "terminal" is a device used by a user, such as a smartphone or tablet.

[0804] "Display means" refers to a method or device by which the terminal displays a menu to the user.

[0805] "Feedback means" refers to a method or device for inputting the progress of the user's diet and exercise.

[0806] "Emotional state" refers to the user's mental and emotional state.

[0807] An "emotion analysis means" is a method or device for analyzing a user's emotional state in real time.

[0808] "Presentation means" refers to a method or device for providing product information within a store.

[0809] "Product information" is information about products that are suggested based on the user's health condition and emotional state.

[0810] The "synchronization means" is a method or device for matching data from vital sensors between the terminal and the server.

[0811] MODE FOR CARRYING OUT THE INVENTION

[0812] The system embodying the present invention provides a personalized health shopping assistant using smart glasses. The specific system construction method and operation will be described below.

[0813] System Configuration

[0814] 1. Hardware and Software

[0815] Smart Glasses: Use common smart glasses (e.g., Google Glass, Vuzix Blade).

[0816] Backend server: Uses cloud services (e.g., AWS EC2, Amazon RDS) and deploys generative AI models (e.g., TensorFlow).

[0817] Front-end app: Use a smartphone application (e.g., Android / iOS application, React Native).

[0818] Sensor devices: Use common vital sensors such as heart rate monitors and blood pressure monitors.

[0819] Data analysis tools: Use Python, Pandas, Scikit-learn, etc.

[0820] System Processing

[0821] 2. Data Entry

[0822] Through a smartphone application, users input their health status, health goals, food preferences, allergy information, etc. This data is then synced to the smart glasses via Bluetooth.

[0823] 3. Server Processing

[0824] The server receives user data and analyzes it using a generative AI model. The analysis includes the user's health status, vital data, and emotional state. The generative AI model calculates optimal nutrients for each individual and generates appropriate meal and training menus.

[0825] 4. Emotion analysis

[0826] The smart glasses use a built-in camera and microphone to capture the user's facial expressions and voice in real time, and then analyze them using an emotion analysis engine. The analysis results are sent to a server and used to adjust the health promotion plan.

[0827] 5. Product proposal

[0828] When a user approaches a particular product in a store, the smart glasses' display will display information about the product and its fit with health goals, such as, "This spinach is rich in vitamin C, which helps relieve stress."

[0829] Specific examples

[0830] If a user is looking to "relieve stress," the smart glasses will display a message while browsing vegetables in a store saying, "This banana is rich in potassium, which helps to relieve fatigue," allowing users to quickly find products that fit their health goals.

[0831] Prompt Sentence Examples

[0832] For example, you can use the following prompt sentences in your generative AI model:

[0833] User information: Age 35, Gender female, Weight 60kg, Height 165cm

[0834] Goal: Lose 3kg in 2 months and relieve stress

[0835] Food preferences: Vegetable-loving, gluten-free

[0836] Emotional state: High stress level (facial recognition), low audio tension

[0837] Recipe provided:

[0838] This user is recommended a banana and spinach smoothie, which is rich in potassium and vitamin C. Generate the following recipe:

[0839] (Banana and spinach smoothie)

[0840] material:

[0841] 1 banana

[0842] 1 cup spinach

[0843] 200ml almond milk

[0844] 1 teaspoon honey

[0845] Instructions:

[0846] 1. Cut the banana into small pieces.

[0847] 2. Add to blender along with spinach.

[0848] 3. Add the almond milk and blend until smooth.

[0849] 4. Add honey and blend again.

[0850] 5. Chill before drinking.

[0851] This invention allows users to receive product suggestions in real time within a physical store that are tailored to their health and emotional state, enabling them to manage their health more effectively.

[0852] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0853] Program processing steps

[0854] Step 1:

[0855] Data Entry

[0856] A user inputs data such as health status, health goals, food preferences, and allergy information through a smartphone application. This data includes, for example, weight, height, and goals (e.g., weight loss, muscle gain). This input data is synchronized to the smart glasses via Bluetooth. The input data is sent from the smartphone application to a server by a transmission means and stored on the server.

[0857] Step 2:

[0858] Data analysis on the server

[0859] The server analyzes the received user data. What is particularly important here is the use of a generative AI model. The generative AI model analyzes the user's basic data (e.g., weight, height, goals) and calculates the optimal nutrients for each individual. It receives the user's basic data as input and outputs data that is useful for nutrient balance and weight management. The analysis results are stored on the server for use in the next step.

[0860] Step 3:

[0861] Emotion analysis

[0862] The device (smart glasses) uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. This data is sent to a server via an emotion analysis engine, which analyzes the user's emotional state. It receives the user's facial and voice data as input and outputs their emotional state (e.g., stress level, elation, depression). The analysis results of the emotional state are used as auxiliary data for the generative AI model.

[0863] Step 4:

[0864] Meal and training menu generation

[0865] The server uses the generative AI model to generate appropriate meal and training menus based on the analysis results. Specifically, it references a nutrition database and automatically generates optimal cooking recipes and exercise plans for the user. It receives the nutrition database and the user's analysis results as input, and outputs meal and exercise menus. The generated menu information is stored on the server.

[0866] Step 5:

[0867] Sending and displaying menu information

[0868] The server transmits the generated menu information to a terminal (smartphone or smart glasses) via a transmission means. The terminal displays the received menu information to the user using a display means. This allows the user to refer to and carry out the suggested meal menu or training menu. The menu information is received as input from the server and displayed via a user interface.

[0869] Step 6:

[0870] Real-time product information display

[0871] The terminal (smart glasses) uses RFID tags and beacons to display product information in real time within the store. When a user approaches a particular product, the smart glasses display information about the product and its suitability for health goals. For example, the glasses may display information such as, "This spinach is rich in vitamin C, which helps relieve stress." The glasses receive data from beacons and RFID tags as input and display product information.

[0872] Step 7:

[0873] Feedback collection

[0874] The user inputs their diet and training progress as feedback via a smartphone application. The data input through the feedback means is sent back to the server and stored on the server. This data is used for the next adjustment of the health plan. The system receives the user's feedback data as input and sends the progress to the server.

[0875] Step 8:

[0876] Reanalysis and plan adjustments

[0877] The server re-analyzes the feedback data and emotional data and re-generates the meal and training menu as necessary. This makes it possible to provide an optimal plan that takes into account the user's health and emotional state. It receives the feedback data and emotional data as input and re-outputs the adjusted menu.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] [Third embodiment]

[0882] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0883] 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.

[0884] 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).

[0885] 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.

[0886] 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.

[0887] 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).

[0888] 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. 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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."

[0894] This invention is a system that provides an optimal health promotion plan based on the user's health condition and goals. Below, we will explain the program of this system and introduce its operation with specific examples.

[0895] Specific system construction

[0896] 1. User Roles

[0897] Users can launch the application on their smartphone or tablet and input their health status and goals, such as weight, height, allergy information, and goals such as weight loss or muscle gain. They can also input their favorite and least favorite foods.

[0898] 2. Role of the terminal

[0899] The terminal (smartphone or tablet device) receives data entered by the user and transmits it to the server, where it is properly formatted and sent via secure communication.

[0900] 3. Server Roles

[0901] The server analyzes the received user data and uses generative AI to calculate the nutritional needs of each individual, creating a personalized nutrition plan based on the user's health status and goals.

[0902] The server then accesses a nutrition database to select ingredients that meet the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. This recipe includes specific amounts of ingredients, cooking instructions, and the nutritional value of the dish.

[0903] Similarly, for training menus, the server analyzes data from vital sensors to evaluate the user's athletic ability and muscle mass. Based on the evaluation results, the generative AI creates an appropriate training menu. This menu includes specific exercise steps, loads, number of repetitions, and reference video links.

[0904] The generated menu information is transmitted from the server to the terminal.

[0905] 4. Terminal Processing

[0906] The device receives the data sent from the server and displays it to the user. The user can then check and follow the provided cooking recipes and training menus through the application. The device also periodically syncs data from vital sensors and records the user's progress.

[0907] 5. User Feedback

[0908] Users input their diet and exercise progress into the application. They can also automatically synchronize vital sensor values ​​with the application. The feedback data is sent from the device to a server and analyzed again. This cycle allows the device to adjust diet and training menus appropriately based on the user's progress.

[0909] Introduction of specific examples

[0910] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The server analyzes the user's current weight, height, allergies, food preferences, etc., and generates a low-calorie, nutritionally balanced meal menu. It also calculates basal metabolic rate and calories burned during exercise based on data from vital sensor data, and provides a menu of moderate-intensity aerobic exercise and muscle training that matches these.

[0911] In this way, the system of the present invention can flexibly respond to the health status and goals of each user and provide a personalized health promotion plan.

[0912] The processing flow will be explained below.

[0913] Step 1:

[0914] Users turn on their smartphone or tablet and open a health management application. Within the application, they enter their basic information and health goals (e.g., weight loss, muscle gain, etc.), as well as food preferences and allergy information.

[0915] Step 2:

[0916] The device receives data entered by the user, formats it appropriately, and sends it to the server, including weight, height, goals, food preferences, and allergy information.

[0917] Step 3:

[0918] The server receives user data sent from the device, analyzes this data, and uses generative AI to calculate the appropriate nutrients for each user.

[0919] Step 4:

[0920] The server accesses a nutrition database and selects appropriate ingredients based on the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. The recipe includes ingredients, specific amounts, cooking instructions, and nutritional information.

[0921] Step 5:

[0922] The server analyzes the user's data obtained from vital sensors, assesses the user's athletic ability and muscle mass, and uses generative AI to create an appropriate training menu, including exercise procedures, loads, number of repetitions, and reference video links.

[0923] Step 6:

[0924] The server sends the generated cooking recipes and training menus to the terminal.

[0925] Step 7:

[0926] The device receives the data sent from the server and displays it to the user, who then checks the provided cooking recipes and training menus through the application and follows the instructions.

[0927] Step 8:

[0928] Users input their diet and exercise progress into the application, and the input data is stored on the device as feedback.

[0929] Step 9:

[0930] The device transmits the progress data entered by the user and the data from the vital sensors to the server.

[0931] Step 10:

[0932] The server re-analyzes the received progress data and vital sensor data, regenerates meal and training menus as needed based on the analysis results, and sends the improved plan to the device.

[0933] Step 11:

[0934] The terminal receives the regenerated plan and displays it to the user, who then proceeds with further execution based on it.

[0935] The above are the specific processing steps for the user, the terminal, and the server. This system efficiently provides the user with a meal and exercise plan that is optimal for their individual health goals.

[0936] Example 1

[0937] 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."

[0938] Conventional health management systems have struggled to provide appropriate meal and training menus based on individual users' health status and goals. They also lack the ability to synchronize vital data in real time, and they do not guarantee that generated plans are consistently updated based on the user's progress. This has led to problems that prevent users from fully utilizing their personalized, optimal health plans.

[0939] 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.

[0940] In this invention, the server includes an input means for inputting the user's health status and goals, a transmission means for transmitting the user data received from the input means to the server, an analysis means for the server to analyze the user data and calculate individual nutrient needs, a generation means for using a generative AI model to generate appropriate meal and training menus based on the analysis results, an output means for transmitting the menus generated by the generation means to a terminal, a display means for the terminal to display the menus to the user, a feedback means for inputting and providing feedback on the progress of the user's diet and exercise, and a synchronization means for synchronizing data from vital sensors in real time. This allows the user to obtain an optimal, personalized health promotion plan and realize specific and feasible health management.

[0941] "Input means" refers to a device or application software that allows a user to input their health status and goals.

[0942] The "transmission means" is a function or device that transmits user data received from the input means to the server.

[0943] The "analysis means" is a program or device that analyzes user data on the server and calculates the nutrients required for each user.

[0944] The "generation means" is a program or device that uses a generative AI model to generate appropriate meal and training menus based on the results of the analysis means.

[0945] "Output means" refers to a function or device that transmits the generated menu to the terminal.

[0946] The "display means" refers to a device or program for visually presenting the menu sent from the server to the user.

[0947] The "feedback means" is a device or program that allows the user to input the progress of their diet and exercise and transmit that information to the server.

[0948] The "synchronization means" is a function or device for receiving data from the vital sensors in real time and synchronizing it with the server.

[0949] The system of the present invention provides an optimal health promotion plan based on the user's health condition and goals. The following provides an overview of this system with specific examples.

[0950] First, a user launches the application on a smartphone or tablet and inputs their health status and goals. This includes, for example, weight, height, allergy information, and goals such as weight loss or muscle gain. In addition, favorite and disliked foods can also be entered. For example, consider a user who weighs 70 kg, is 170 cm tall, has no allergies, and wants to lose 5 kg in one month.

[0951] The terminal receives data entered by the user, formats it appropriately, and sends it to the server. Here, the input data is serialized in JSON format or similar, and a secure communication method (such as HTTPS) is used.

[0952] The server then uses the generative AI model to analyze the received user data. The server sends the generative AI model a prompt like this:

[0953] User Profile:

[0954] Weight: 70kg

[0955] Height: 170cm

[0956] Allergies: None

[0957] Food preferences: Likes vegetables, dislikes fatty foods

[0958] Goal: Lose 5kg in 1 month

[0959] Output format:

[0960] Please suggest a daily meal plan, recipes, and exercise menu.

[0961] The generative AI model generates the optimal health plan (meal recipes and exercise menu) for the user. For example, it suggests a green smoothie for breakfast, a chicken breast salad for lunch, and tofu steak for dinner. It also generates an exercise menu based on vital sensor data, such as 30 minutes of running, 3 sets of 15 push-ups, and 3 sets of 20 squats every day.

[0962] The generated plan is sent from the server to the device, which displays it to the user. The user can then review the provided meal and training menu and put it into practice. The device also synchronizes data from vital sensors in real time to record the user's progress.

[0963] The user inputs their diet and exercise progress into the application, and this feedback data is sent from the device to the server. The server then analyzes this feedback data and adjusts the diet and training menu accordingly based on the user's progress.

[0964] This allows users to receive a personalized health plan based on their health status and goals, supporting continuous health management. The system synchronizes data in real time and utilizes generative AI models to provide users with the optimal health promotion plan.

[0965] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0966] Step 1:

[0967] Users launch the application using a smartphone or tablet device and input their weight, height, allergy information, health goals such as weight loss or muscle gain, and favorite and disliked foods. This input data is later analyzed by the server, so accurate input is required.

[0968] Input: User's health status (weight, height, allergy information, etc.) and health goals

[0969] Output: Formatted user data

[0970] Step 2:

[0971] The terminal receives data entered by the user and converts it into an appropriate format, for example, serializing the input data into JSON format and preparing it for transmission to the server, while also checking the integrity of the data.

[0972] Input: Raw data entered by the user

[0973] Output: User data in JSON format

[0974] Step 3:

[0975] The terminal sends the formatted data to the server via a secure communication method (e.g., HTTPS), and receives a response from the server to confirm whether the communication was successful.

[0976] Input: User data in JSON format

[0977] Output: Data transmission result to server (success / failure)

[0978] Step 4:

[0979] The server analyzes the received user data and generates prompts for the generative AI model, which includes the user's health status and goals, and the generative AI model then generates an optimal health plan based on this.

[0980] Input: User data received by the server

[0981] Output: Prompt sentence to the generative AI model

[0982] Step 5:

[0983] The generative AI model generates optimal meal and training menus based on prompts, including specific ingredients, cooking instructions, nutritional information, exercise types, loads, repetitions, and reference video links.

[0984] Input: Prompt sentence for generative AI model

[0985] Output: A health plan (meal menu and training menu) that is optimal for the user

[0986] Step 6:

[0987] The server sends the generated health plan to the terminal in an appropriate format such as JSON, where the server checks the integrity of the data and ensures that the terminal receives it correctly.

[0988] Input: Health plan from a generative AI model

[0989] Output: Health plan transmission result to the device (success / failure)

[0990] Step 7:

[0991] The device analyzes the health plan received from the server and visually displays it to the user. The user can then check the provided meal and training menus on the application screen and put them into practice.

[0992] Input: Health plan received from the server

[0993] Output: Health plan user interface display

[0994] Step 8:

[0995] Users enter their diet and exercise progress into the application, which also syncs vital sensor data in real time to record their progress, including heart rate, steps taken, and calories burned.

[0996] Input: User's diet and exercise progress data, vital sensor data

[0997] Output: Formatted feedback data

[0998] Step 9:

[0999] The device sends the user's feedback data to a server, which then analyzes the data and adjusts the user's diet and training accordingly based on their progress, enabling the provision of a continuous, personalized health plan.

[1000] Input: User feedback data

[1001] Output: Coordinated Health Plan

[1002] (Application example 1)

[1003] 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."

[1004] While traditional health management systems suggest meal and training menus based on a user's individual health status and goals, they lack the ability to order specific meals. This requires users to purchase ingredients and cook the meals themselves to achieve the suggested menus, making them time-consuming and impractical. Furthermore, the lack of feedback and subsequent adjustments based on the user's individual progress makes effective health management in real time difficult.

[1005] 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.

[1006] In this invention, the server includes an input means for inputting the user's health condition and goals, a transmission means for transmitting the user data received from the input means to the server, an analysis means for the server to analyze the user data and calculate individual nutrient needs, and a generation means for generating appropriate meal menus and training menus based on the analysis results. This makes it possible to propose and directly order individualized meal menus according to the user's health condition and goals, thereby realizing efficient health management.

[1007] An "input means" is a device or method for a user to input information such as their health status and goals.

[1008] The "transmitting means" is a device or method for transmitting the user data received from the input means to the server.

[1009] The "analysis means" refers to a device or method by which the server analyzes user data and calculates the nutritional and exercise information required by each individual user.

[1010] The "generation means" is a device or method for generating appropriate meal menus and training menus based on the analysis results.

[1011] The "output means" is a device or method for transmitting the menu generated by the generating means to a terminal.

[1012] The "display means" is a device or method by which the terminal displays the menu to the user.

[1013] A "feedback means" is a device or method for inputting the progress of the diet and exercise a user has undertaken.

[1014] The "ordering means" refers to a device or method that allows a user to place an order with a service that provides the proposed meal menu as is.

[1015] This invention is a system that provides a personalized health promotion plan based on a user's health status and goals. The system transmits data entered by the user to a server, which analyzes the data to generate an optimal nutrition plan and training menu, which are then provided to the user via a terminal. A specific embodiment of this system is described below.

[1016] System configuration

[1017] 1. User Roles

[1018] Users can launch the application on their smartphone or tablet and input their health status and goals, such as weight, height, allergy information, and goals such as weight loss or muscle gain. They can also input their favorite and least favorite foods.

[1019] 2. Role of the terminal

[1020] The terminal is responsible for receiving data entered by the user and transmitting it to the server, where it is properly formatted and sent to the server via secure communications.

[1021] 3. Server Roles

[1022] The server analyzes the received user data and uses the generative AI to calculate the nutritional needs of each individual. This creates a personalized nutrition plan based on the user's health status and goals. The server also accesses a nutrition database to select ingredients that are appropriate for the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. This recipe includes specific amounts of ingredients, cooking instructions, and the nutritional value of the dish.

[1023] Similarly, when it comes to training menus, the server analyzes vital sensor data to evaluate the user's athletic ability and muscle mass. Based on the evaluation results, the generation AI creates an appropriate training menu. This menu includes specific exercise steps, load, number of repetitions, and reference video links. This generated menu information is sent from the server to the device.

[1024] 4. Terminal Processing

[1025] The device receives the data sent from the server and displays it to the user. The user can then check and follow the cooking recipes and training menus provided through the application. The device also periodically syncs data from vital sensors and records the user's progress.

[1026] 5. User Feedback

[1027] Users input their diet and exercise progress into the application. They can then place an order from the service, which provides the suggested meal menu as is. It is also possible to automatically synchronize vital sensor values ​​with the application. The feedback data is sent from the device to the server and analyzed again. This cycle allows the diet and training menu to be adjusted appropriately according to the user's progress.

[1028] Specific examples

[1029] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The server analyzes the user's current weight, height, allergies, food preferences, etc., and generates a low-calorie, nutritionally balanced meal menu. It also calculates basal metabolic rate and calories burned during exercise based on data from vital sensor data, and provides a menu of moderate-intensity aerobic exercise and muscle training that matches these.

[1030] Prompt Sentence Examples

[1031] Create a daily meal plan based on your health and goals.

[1032] Current weight: 70kg

[1033] Height: 175cm

[1034] Goal: Lose 5kg in 1 month

[1035] Allergies: None

[1036] Favorite foods: Chicken, broccoli, yogurt

[1037] Disliked ingredients: None

[1038] In this way, the system of the present invention can flexibly respond to the health status and goals of each user and provide a personalized health promotion plan, which is useful for supporting the user's life in a healthier and more efficient manner.

[1039] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1040] Step 1:

[1041] A user launches the application using a smartphone or tablet and inputs their health status and goals, including weight, height, allergies, food preferences, and goals such as weight loss or muscle gain. This generates user input data.

[1042] Step 2:

[1043] The terminal receives the user's input data and sends it to the server in an appropriate format. The input data is formatted in JSON format or similar and sent to the server via a secure communication method (e.g., HTTPS). This transfers the data to the server.

[1044] Step 3:

[1045] The server analyzes the received user data and uses a generative AI model to calculate individual nutritional needs. In this step, the user data is converted into a prompt sentence and input into a generative AI model (e.g., GPT-3). The generative AI model analyzes the prompt sentence and outputs information on required nutrients and exercise.

[1046] Step 4:

[1047] The server generates appropriate meal and training menus based on the analysis results. The server accesses a nutritional database and selects ingredients that are appropriate for the user's nutritional needs. It also analyzes data from vital sensors to evaluate the user's athletic ability and muscle mass. This allows it to generate specific menus and training plans.

[1048] Step 5:

[1049] The server sends the generated menu to the terminal, where it is properly formatted and sent to the terminal via secure communication means, thereby transferring the menu information to the terminal.

[1050] Step 6:

[1051] The device displays the received data to the user. The provided cooking recipes and training menus can be viewed on the application screen. The user can then refer to the specific steps for carrying out these menus.

[1052] Step 7:

[1053] The user orders the suggested meal menu from the food delivery service. The user selects the suggested meal menu from the terminal and completes the order procedure with the food delivery service. The necessary ingredients and food are then delivered to the user's home.

[1054] Step 8:

[1055] The user inputs the progress of their diet and exercise, and the device sends it to the server. The user inputs the details of their exercise into the application, and the device sends it to the server. This transfers the progress data to the server.

[1056] Step 9:

[1057] The server analyzes the progress data and generates new menus and training plans as appropriate. The server then analyzes the vital sensor data again and generates new menus and plans based on the user's progress. This allows the user's health promotion plan to be updated as appropriate.

[1058] Through these steps, a personalized health promotion plan is provided based on the user's health condition and goals, enabling the user to effectively manage their health.

[1059] 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.

[1060] This invention is a system that provides an optimal health promotion plan based on the user's health condition, goals, and even emotional state. Below, we will explain the program of this system and introduce its operation with specific examples.

[1061] Specific system construction

[1062] 1. User Roles

[1063] A user opens a health management application on a smartphone or tablet. Within the application, the user enters their basic information, health goals (e.g., weight loss, muscle gain, etc.), food preferences, allergy information, etc. The user can also communicate their emotions by inputting voice and facial expressions using a microphone and camera. Emotions can also be input using the text chat function.

[1064] 2. Role of the terminal

[1065] The device receives basic data and health goals entered by the user, as well as voice, facial expressions, and text data, and then appropriately formats and transmits the data to the server. This data includes weight, height, goals, food preferences, allergy information, and the user's emotional state.

[1066] 3. Server Roles

[1067] The server receives user data sent from the device. The server first analyzes basic data and health goals, and uses generative AI to calculate the appropriate nutrients for each user. Next, the server's evaluation means analyzes vital data and evaluates athletic ability and muscle mass.

[1068] Furthermore, the server uses an emotion engine to analyze emotions from the user's voice, facial expressions, and text data, and uses this emotional information as auxiliary data for the generative AI to calculate nutritional intake and evaluate athletic ability.

[1069] 4. Create a meal and training menu

[1070] The server selects appropriate ingredients based on the analysis results, and the generative AI creates a cooking recipe tailored to the user based on the nutrition database and emotional data. The recipe includes ingredients, specific amounts, cooking instructions, and nutritional information.

[1071] Similarly, training menus are generated based on vital sensor data and emotional data, and include exercise instructions, load, number of repetitions, and reference video links.

[1072] The generated menu information is transmitted from the server to the terminal.

[1073] 5. Terminal Processing

[1074] The device receives the data sent from the server and displays it to the user. The user can then use the application to check the provided cooking recipes and training menus and follow the instructions. The device also periodically syncs data from vital sensors to record the user's progress.

[1075] 6. User Feedback

[1076] Users input their diet and exercise progress into the application. The input data is saved on the device as feedback and sent to the server. Users can also input emotional information using voice, facial expressions, and text.

[1077] 7. Reanalysis and Adjustment

[1078] The server reanalyzes the received progress data and emotional information, regenerates meal and training menus as needed, and sends the improved plans to the device, providing a flexible health promotion plan that also takes the user's emotional state into account.

[1079] Introduction of specific examples

[1080] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The system analyzes the user's emotional state each day (e.g., if the stress level is high) and adjusts the menu accordingly. On days when stress is high, it will provide recipes using ingredients that have a relaxing effect and suggest light exercise menus.

[1081] In this way, the system can provide a personalized health promotion plan that takes into account not only the user's health status and goals, but also their emotional state.

[1082] The processing flow will be explained below.

[1083] Step 1:

[1084] Users turn on their smartphone or tablet and open a health management application. Within the application, they input their health status (weight, height, allergies, etc.) and goals (e.g., weight loss, muscle gain, etc.), as well as their favorite and least favorite foods.

[1085] Step 2:

[1086] Users can communicate their emotional state to the system by inputting voice and facial expressions using a microphone and camera, and can also input emotions using the text chat function.

[1087] Step 3:

[1088] The device receives data entered by the user (health status, goals, food preferences, allergy information, voice, facial expressions, text data), formats it appropriately, and sends it to the server.

[1089] Step 4:

[1090] The server receives user data sent from the device, analyzes it regarding health status and goals, and then uses generative AI to calculate appropriate nutrients.

[1091] Step 5:

[1092] The server then uses an emotion engine to analyze emotions from the user's voice, facial expressions, and text data. Based on the analysis results, the emotional information is used as auxiliary data for the generative AI.

[1093] Step 6:

[1094] The server accesses a nutrition database and selects appropriate ingredients based on the user's nutritional needs. Taking into account the user's emotional data, the generative AI creates a cooking recipe tailored to the user. The recipe includes ingredients, specific serving sizes, cooking instructions, and nutritional information.

[1095] Step 7:

[1096] The server analyzes data from vital sensors to evaluate athletic ability and muscle mass. Taking emotional data into account, the generative AI creates an appropriate training menu. The menu includes exercise instructions, load, number of repetitions, and links to reference videos.

[1097] Step 8:

[1098] The server sends the generated cooking recipes and training menus to the terminal.

[1099] Step 9:

[1100] The device receives the data sent from the server and displays it to the user, who can then use the application to check and implement the provided cooking recipes and training menus.

[1101] Step 10:

[1102] The user inputs the progress of their diet and exercise into the application. They also update their emotional information by inputting voice and facial expressions. This data is stored on the device.

[1103] Step 11:

[1104] The terminal transmits the progress data and updated emotion information to the server.

[1105] Step 12:

[1106] The server reanalyzes the received progress data and emotional information, regenerates meal and training menus as needed, and sends the improved plans to the device.

[1107] Step 13:

[1108] The terminal receives the regenerated plan and displays it to the user, who then proceeds with the next execution based on it.

[1109] The above are the specific processing steps for the user, the terminal, and the server. This system provides an optimal personalized health promotion plan that takes into account the user's health condition, goals, and daily emotional state.

[1110] Example 2

[1111] 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."

[1112] In modern society, it is important to create effective health promotion plans tailored to individual health conditions and goals. However, these plans require the consideration of a wide range of individual data, making it particularly difficult to reflect the user's emotional state. Furthermore, there is a need for real-time feedback on whether the menus and training plans provided are appropriate for the user's current condition, and adjustments should be made accordingly. This will enable more personalized health management.

[1113] 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.

[1114] In this invention, the server includes input means for inputting the user's health condition and goals, transmission means for transmitting the user data received from the input means, analysis means for analyzing the user data and calculating each individual's required nutrients, emotion analysis means for analyzing the user's voice, facial expression, and text data and evaluating their emotional state, generation means for generating appropriate meal and training menus based on the analysis results and their emotional state, output means for transmitting the generated menus to the terminal, display means for the terminal to display the menus to the user, and feedback means for inputting the user's progress in diet and exercise. This makes it possible to provide a personalized health promotion plan that takes into account the user's individual health condition and emotional state.

[1115] "Input means" refers to a device or function that allows a user to input their health status and goals.

[1116] "Transmission means" refers to a device or function for transmitting data received from a user to a server.

[1117] "Analysis means" refers to a device or function for analyzing received user data and calculating individual required nutrients.

[1118] "Emotion analysis means" refers to a device or function for analyzing a user's voice, facial expression, and text data to evaluate their emotional state.

[1119] "Generation means" refers to a device or function for generating appropriate meal menus and training menus based on the analysis results and emotional state.

[1120] The "output means" refers to a device or function for transmitting the menu generated by the generation means to the terminal.

[1121] "Display means" refers to a device or function for displaying the transmitted menu to the user.

[1122] "Feedback means" refers to a device or function that allows a user to input progress on diet and exercise.

[1123] "Vital data" refers to data containing the user's biological information, such as heart rate and calorie consumption.

[1124] "Synchronization means" refers to a device or function for synchronizing data from a user's vital sensors with a terminal.

[1125] This invention is a system that provides an optimal health promotion plan based on a user's health status, goals, and even emotional state. This system is built around a health management application that uses a smartphone or tablet device, and related server-side processing.

[1126] User Roles

[1127] First, the user launches the health management application on their smartphone or tablet. Within the app, they enter the following information:

[1128] Basic information (e.g. weight, height)

[1129] Health goals (e.g., lose 5 kg in one month)

[1130] Food preferences (e.g., mainly Japanese food, lots of vegetables)

[1131] Allergy information (e.g., peanut allergy)

[1132] Emotional information (e.g., voice input via microphone, facial expression recognition via camera, or text chat)

[1133] Device Role

[1134] The terminal takes this information entered by the user and formats the data as follows:

[1135] Basic information, health goals, food preferences, allergy information: formatted in JSON format

[1136] Emotional information: Analyzes audio and image data and converts them into numerical values ​​or text

[1137] The formatted data is sent to the server using the HTTPS protocol.

[1138] Server Roles

[1139] The server receives the data sent from the device and analyzes it through the following process:

[1140] Store basic information and health goals in a database

[1141] Uses generative AI models to calculate appropriate nutrients based on health goals

[1142] Analyze vital data (e.g., heart rate, calorie consumption) to evaluate athletic ability and muscle mass

[1143] Use an emotion engine to analyze emotions from voice, facial expressions, and text data and store them as numerical values.

[1144] Examples of prompts to input to a generative AI model include:

[1145] "A 30-year-old, 170cm tall, 70kg person wants to lose 5kg in one month. Their stress level is high. Please provide them with an appropriate diet and exercise plan."

[1146] Menu generation

[1147] The server generates the following information based on the analysis:

[1148] Cooking recipes: ingredients, specific amounts, cooking instructions, nutritional information

[1149] Training plan: Exercise procedure, load, number of repetitions, reference video link

[1150] The system takes emotional data into consideration and creates a plan that includes foods with a relaxing effect and light exercise. The generated information is sent to the device in JSON format.

[1151] Displaying data on the device

[1152] The device receives the meal and training menu sent from the server and displays it to the user as follows:

[1153] The recipe screen displays the name of the dish, ingredients, serving size, cooking instructions, and nutritional information in a list format.

[1154] The training screen displays the exercise name, load, number of repetitions, and a link to a reference video.

[1155] Based on this information, users can put the provided meal recipes and training menus into practice.

[1156] User Feedback

[1157] The user then re-enters their diet and exercise progress into the application using checkboxes, text, voice input, facial expression recognition, etc. This feedback data is also sent back to the server, which then re-analyzes it.

[1158] Reanalysis and adjustments

[1159] The server then analyzes the received feedback data and generates new diet and training plans as needed, providing an optimal health promotion plan tailored to the individual user's health and emotional state.

[1160] As described above, this system provides more personalized health management for users, and supports them effectively and flexibly.

[1161] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1162] Step 1:

[1163] The user launches the health management application using a smartphone or tablet device and enters the following information within the application:

[1164] Basic information (e.g. weight, height)

[1165] Health goals (e.g., lose 5 kg in one month)

[1166] Food preferences (e.g., mainly Japanese food, lots of vegetables)

[1167] Allergy information (e.g., peanut allergy)

[1168] Emotional information (e.g., voice input via microphone, facial expression recognition via camera, or text chat)

[1169] Input: User's individual data (basic information, health goals, food preferences, allergy information, emotional information)

[1170] Output: None (data entry complete)

[1171] Step 2:

[1172] The terminal takes the information entered by the user and formats the data as follows:

[1173] Basic information, health goals, food preferences, allergy information: formatted in JSON format

[1174] Emotional information: Analyzes audio and image data and converts them into numerical values ​​or text

[1175] Input: Individual data entered by the user

[1176] Output: Formatted data (JSON format)

[1177] Step 3:

[1178] The terminal sends the formatted data to the server using the HTTPS protocol.

[1179] Input: Formatted data (JSON format)

[1180] Output: Data sent to the server

[1181] Step 4:

[1182] The server receives the data sent from the device and performs the following processes:

[1183] Store basic information and health goals in a database

[1184] Uses generative AI models to calculate appropriate nutrients based on health goals

[1185] Analyze vital data (e.g., heart rate, calorie consumption) to evaluate athletic ability and muscle mass

[1186] Use an emotion engine to analyze emotions from voice, facial expressions, and text data and store them as numerical values.

[1187] Input: Data sent from the terminal

[1188] Output: Analysis results (nutrient calculation, vital data evaluation, emotion analysis results)

[1189] Step 5:

[1190] The server generates the following information based on the analysis:

[1191] Cooking recipes: ingredients, specific amounts, cooking instructions, nutritional information

[1192] Training plan: Exercise procedure, load, number of repetitions, reference video link

[1193] Input: Analysis results

[1194] Output: Generated menu information (cooking recipes, training plans)

[1195] Step 6:

[1196] The server sends the generated menu information to the terminal in JSON format.

[1197] Input: Generated menu information (cooking recipes, training plans)

[1198] Output: Menu information sent to the device

[1199] Step 7:

[1200] The terminal receives the menu information sent from the server and displays it to the user as follows:

[1201] The recipe screen displays the name of the dish, ingredients, serving size, cooking instructions, and nutritional information in a list format.

[1202] The training screen displays the exercise name, load, number of repetitions, and a link to a reference video.

[1203] Input: Menu information sent from the server

[1204] Output: Menu information displayed to the user

[1205] Step 8:

[1206] Users can enter their progress in diet and exercise into the application. For example, they can record their dietary achievements and changes in their physical condition using text or check boxes, and provide feedback using voice or facial expressions.

[1207] Input: User progress data (diet and exercise achievements and feedback)

[1208] Output: Feedback data input completed

[1209] Step 9:

[1210] The terminal transmits the feedback data input by the user to the server.

[1211] Input: Feedback data

[1212] Output: Feedback data sent to the server

[1213] Step 10:

[1214] The server then re-analyzes the received feedback data and generates new diet and training plans as needed, again involving generative AI models and sentiment analysis.

[1215] Input: Feedback data

[1216] Output: Revised Health Promotion Plan

[1217] As described above, the program of this system performs specific data processing and calculation for each step, and provides the user with an optimal health promotion plan.

[1218] (Application example 2)

[1219] 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."

[1220] Modern health management systems focus on providing dietary and training menus tailored to the user's health status and goals, but lack comprehensive support that takes into account the user's emotional state. In particular, it is difficult for physical stores to provide real-time product recommendations tailored to the user's current health and emotional state. This makes it difficult for users to easily find the products that best suit them, leading to ineffective health management.

[1221] The identification process by the identification 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 emotion analysis means that analyzes the emotional state of the user in real time and proposes product information according to the emotional state, and a presentation means that provides product information in the store. This allows the user to receive product proposals in real time in the physical store that are tailored to their health condition and emotional state.

[1222] Definitions of important words

[1223] A "user" is someone who inputs information such as health status, goals, and emotional state.

[1224] "Health status" refers to the user's current physical condition.

[1225] A "goal" is a health-related objective that a user wishes to achieve.

[1226] "Input means" refers to a method or device that allows a user to input information such as health status and goals.

[1227] The "transmission means" refers to a method or device for transmitting input user data to a server.

[1228] A "server" is a device or system that receives and analyzes user data and provides the necessary information.

[1229] The "analysis means" refers to a method or device by which the server analyzes user data and calculates the necessary information.

[1230] The "generation means" refers to a method or device that generates an appropriate menu based on the analysis results.

[1231] "Output means" refers to a method or device for transmitting the generated menu to a terminal.

[1232] A "terminal" is a device used by a user, such as a smartphone or tablet.

[1233] "Display means" refers to a method or device by which the terminal displays a menu to the user.

[1234] "Feedback means" refers to a method or device for inputting the progress of the user's diet and exercise.

[1235] "Emotional state" refers to the user's mental and emotional state.

[1236] An "emotion analysis means" is a method or device for analyzing a user's emotional state in real time.

[1237] "Presentation means" refers to a method or device for providing product information within a store.

[1238] "Product information" is information about products that are suggested based on the user's health condition and emotional state.

[1239] The "synchronization means" is a method or device for matching data from vital sensors between the terminal and the server.

[1240] MODE FOR CARRYING OUT THE INVENTION

[1241] The system embodying the present invention provides a personalized health shopping assistant using smart glasses. The specific system construction method and operation will be described below.

[1242] System Configuration

[1243] 1. Hardware and Software

[1244] Smart Glasses: Use common smart glasses (e.g., Google Glass, Vuzix Blade).

[1245] Backend server: Uses cloud services (e.g., AWS EC2, Amazon RDS) and deploys generative AI models (e.g., TensorFlow).

[1246] Front-end app: Use a smartphone application (e.g., Android / iOS application, React Native).

[1247] Sensor devices: Use common vital sensors such as heart rate monitors and blood pressure monitors.

[1248] Data analysis tools: Use Python, Pandas, Scikit-learn, etc.

[1249] System Processing

[1250] 2. Data Entry

[1251] Through a smartphone application, users input their health status, health goals, food preferences, allergy information, etc. This data is then synced to the smart glasses via Bluetooth.

[1252] 3. Server Processing

[1253] The server receives user data and analyzes it using a generative AI model. The analysis includes the user's health status, vital data, and emotional state. The generative AI model calculates optimal nutrients for each individual and generates appropriate meal and training menus.

[1254] 4. Emotion analysis

[1255] The smart glasses use a built-in camera and microphone to capture the user's facial expressions and voice in real time, and then analyze them using an emotion analysis engine. The analysis results are sent to a server and used to adjust the health promotion plan.

[1256] 5. Product proposal

[1257] When a user approaches a particular product in a store, the smart glasses' display will display information about the product and its fit with health goals, such as, "This spinach is rich in vitamin C, which helps relieve stress."

[1258] Specific examples

[1259] If a user is looking to "relieve stress," the smart glasses will display a message while browsing vegetables in a store saying, "This banana is rich in potassium, which helps to relieve fatigue," allowing users to quickly find products that fit their health goals.

[1260] Prompt Sentence Examples

[1261] For example, you can use the following prompt sentences in your generative AI model:

[1262] User information: Age 35, Gender female, Weight 60kg, Height 165cm

[1263] Goal: Lose 3kg in 2 months and relieve stress

[1264] Food preferences: Vegetable-loving, gluten-free

[1265] Emotional state: High stress level (facial recognition), low audio tension

[1266] Recipe provided:

[1267] This user is recommended a banana and spinach smoothie, which is rich in potassium and vitamin C. Generate the following recipe:

[1268] (Banana and spinach smoothie)

[1269] material:

[1270] 1 banana

[1271] 1 cup spinach

[1272] 200ml almond milk

[1273] 1 teaspoon honey

[1274] Instructions:

[1275] 1. Cut the banana into small pieces.

[1276] 2. Add to blender along with spinach.

[1277] 3. Add the almond milk and blend until smooth.

[1278] 4. Add honey and blend again.

[1279] 5. Chill before drinking.

[1280] This invention allows users to receive product suggestions in real time within a physical store that are tailored to their health and emotional state, enabling them to manage their health more effectively.

[1281] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1282] Program processing steps

[1283] Step 1:

[1284] Data Entry

[1285] A user inputs data such as health status, health goals, food preferences, and allergy information through a smartphone application. This data includes, for example, weight, height, and goals (e.g., weight loss, muscle gain). This input data is synchronized to the smart glasses via Bluetooth. The input data is sent from the smartphone application to a server by a transmission means and stored on the server.

[1286] Step 2:

[1287] Data analysis on the server

[1288] The server analyzes the received user data. What is particularly important here is the use of a generative AI model. The generative AI model analyzes the user's basic data (e.g., weight, height, goals) and calculates the optimal nutrients for each individual. It receives the user's basic data as input and outputs data that is useful for nutrient balance and weight management. The analysis results are stored on the server for use in the next step.

[1289] Step 3:

[1290] Emotion analysis

[1291] The device (smart glasses) uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. This data is sent to a server via an emotion analysis engine, which analyzes the user's emotional state. It receives the user's facial and voice data as input and outputs their emotional state (e.g., stress level, elation, depression). The analysis results of the emotional state are used as auxiliary data for the generative AI model.

[1292] Step 4:

[1293] Meal and training menu generation

[1294] The server uses the generative AI model to generate appropriate meal and training menus based on the analysis results. Specifically, it references a nutrition database and automatically generates optimal cooking recipes and exercise plans for the user. It receives the nutrition database and the user's analysis results as input, and outputs meal and exercise menus. The generated menu information is stored on the server.

[1295] Step 5:

[1296] Sending and displaying menu information

[1297] The server transmits the generated menu information to a terminal (smartphone or smart glasses) via a transmission means. The terminal displays the received menu information to the user using a display means. This allows the user to refer to and carry out the suggested meal menu or training menu. The menu information is received as input from the server and displayed via a user interface.

[1298] Step 6:

[1299] Real-time product information display

[1300] The terminal (smart glasses) uses RFID tags and beacons to display product information in real time within the store. When a user approaches a particular product, the smart glasses display information about the product and its suitability for health goals. For example, the glasses may display information such as, "This spinach is rich in vitamin C, which helps relieve stress." The glasses receive data from beacons and RFID tags as input and display product information.

[1301] Step 7:

[1302] Feedback collection

[1303] The user inputs their diet and training progress as feedback via a smartphone application. The data input through the feedback means is sent back to the server and stored on the server. This data is used for the next adjustment of the health plan. The system receives the user's feedback data as input and sends the progress to the server.

[1304] Step 8:

[1305] Reanalysis and plan adjustments

[1306] The server re-analyzes the feedback data and emotional data and re-generates the meal and training menu as necessary. This makes it possible to provide an optimal plan that takes into account the user's health and emotional state. It receives the feedback data and emotional data as input and re-outputs the adjusted menu.

[1307] 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.

[1308] 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.

[1309] 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.

[1310] [Fourth embodiment]

[1311] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1312] 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.

[1313] 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).

[1314] 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.

[1315] 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.

[1316] 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).

[1317] 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. 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.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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."

[1324] This invention is a system that provides an optimal health promotion plan based on the user's health condition and goals. Below, we will explain the program of this system and introduce its operation with specific examples.

[1325] Specific system construction

[1326] 1. User Roles

[1327] Users can launch the application on their smartphone or tablet and input their health status and goals, such as weight, height, allergy information, and goals such as weight loss or muscle gain. They can also input their favorite and least favorite foods.

[1328] 2. Role of the terminal

[1329] The terminal (smartphone or tablet device) receives data entered by the user and transmits it to the server, where it is properly formatted and sent via secure communication.

[1330] 3. Server Roles

[1331] The server analyzes the received user data and uses generative AI to calculate the nutritional needs of each individual, creating a personalized nutrition plan based on the user's health status and goals.

[1332] The server then accesses a nutrition database to select ingredients that meet the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. This recipe includes specific amounts of ingredients, cooking instructions, and the nutritional value of the dish.

[1333] Similarly, for training menus, the server analyzes data from vital sensors to evaluate the user's athletic ability and muscle mass. Based on the evaluation results, the generative AI creates an appropriate training menu. This menu includes specific exercise steps, loads, number of repetitions, and reference video links.

[1334] The generated menu information is transmitted from the server to the terminal.

[1335] 4. Terminal Processing

[1336] The device receives the data sent from the server and displays it to the user. The user can then check and follow the provided cooking recipes and training menus through the application. The device also periodically syncs data from vital sensors and records the user's progress.

[1337] 5. User Feedback

[1338] Users input their diet and exercise progress into the application. They can also automatically synchronize vital sensor values ​​with the application. The feedback data is sent from the device to a server and analyzed again. This cycle allows the device to adjust diet and training menus appropriately based on the user's progress.

[1339] Introduction of specific examples

[1340] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The server analyzes the user's current weight, height, allergies, food preferences, etc., and generates a low-calorie, nutritionally balanced meal menu. It also calculates basal metabolic rate and calories burned during exercise based on data from vital sensor data, and provides a menu of moderate-intensity aerobic exercise and muscle training that matches these.

[1341] In this way, the system of the present invention can flexibly respond to the health status and goals of each user and provide a personalized health promotion plan.

[1342] The processing flow will be explained below.

[1343] Step 1:

[1344] Users turn on their smartphone or tablet and open a health management application. Within the application, they enter their basic information and health goals (e.g., weight loss, muscle gain, etc.), as well as food preferences and allergy information.

[1345] Step 2:

[1346] The device receives data entered by the user, formats it appropriately, and sends it to the server, including weight, height, goals, food preferences, and allergy information.

[1347] Step 3:

[1348] The server receives user data sent from the device, analyzes this data, and uses generative AI to calculate the appropriate nutrients for each user.

[1349] Step 4:

[1350] The server accesses a nutrition database and selects appropriate ingredients based on the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. The recipe includes ingredients, specific amounts, cooking instructions, and nutritional information.

[1351] Step 5:

[1352] The server analyzes the user's data obtained from vital sensors, assesses the user's athletic ability and muscle mass, and uses generative AI to create an appropriate training menu, including exercise procedures, loads, number of repetitions, and reference video links.

[1353] Step 6:

[1354] The server sends the generated cooking recipes and training menus to the terminal.

[1355] Step 7:

[1356] The device receives the data sent from the server and displays it to the user, who then checks the provided cooking recipes and training menus through the application and follows the instructions.

[1357] Step 8:

[1358] Users input their diet and exercise progress into the application, and the input data is stored on the device as feedback.

[1359] Step 9:

[1360] The device transmits the progress data entered by the user and the data from the vital sensors to the server.

[1361] Step 10:

[1362] The server re-analyzes the received progress data and vital sensor data, regenerates meal and training menus as needed based on the analysis results, and sends the improved plan to the device.

[1363] Step 11:

[1364] The terminal receives the regenerated plan and displays it to the user, who then proceeds with further execution based on it.

[1365] The above are the specific processing steps for the user, the terminal, and the server. This system efficiently provides the user with a meal and exercise plan that is optimal for their individual health goals.

[1366] Example 1

[1367] 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."

[1368] Conventional health management systems have struggled to provide appropriate meal and training menus based on individual users' health status and goals. They also lack the ability to synchronize vital data in real time, and they do not guarantee that generated plans are consistently updated based on the user's progress. This has led to problems that prevent users from fully utilizing their personalized, optimal health plans.

[1369] 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.

[1370] In this invention, the server includes an input means for inputting the user's health status and goals, a transmission means for transmitting the user data received from the input means to the server, an analysis means for the server to analyze the user data and calculate individual nutrient needs, a generation means for using a generative AI model to generate appropriate meal and training menus based on the analysis results, an output means for transmitting the menus generated by the generation means to a terminal, a display means for the terminal to display the menus to the user, a feedback means for inputting and providing feedback on the progress of the user's diet and exercise, and a synchronization means for synchronizing data from vital sensors in real time. This allows the user to obtain an optimal, personalized health promotion plan and realize specific and feasible health management.

[1371] "Input means" refers to a device or application software that allows a user to input their health status and goals.

[1372] The "transmission means" is a function or device that transmits user data received from the input means to the server.

[1373] The "analysis means" is a program or device that analyzes user data on the server and calculates the nutrients required for each user.

[1374] The "generation means" is a program or device that uses a generative AI model to generate appropriate meal and training menus based on the results of the analysis means.

[1375] "Output means" refers to a function or device that transmits the generated menu to the terminal.

[1376] The "display means" refers to a device or program for visually presenting the menu sent from the server to the user.

[1377] The "feedback means" is a device or program that allows the user to input the progress of their diet and exercise and transmit that information to the server.

[1378] The "synchronization means" is a function or device for receiving data from the vital sensors in real time and synchronizing it with the server.

[1379] The system of the present invention provides an optimal health promotion plan based on the user's health condition and goals. The following provides an overview of this system with specific examples.

[1380] First, a user launches the application on a smartphone or tablet and inputs their health status and goals. This includes, for example, weight, height, allergy information, and goals such as weight loss or muscle gain. In addition, favorite and disliked foods can also be entered. For example, consider a user who weighs 70 kg, is 170 cm tall, has no allergies, and wants to lose 5 kg in one month.

[1381] The terminal receives data entered by the user, formats it appropriately, and sends it to the server. Here, the input data is serialized in JSON format or similar, and a secure communication method (such as HTTPS) is used.

[1382] The server then uses the generative AI model to analyze the received user data. The server sends the generative AI model a prompt like this:

[1383] User Profile:

[1384] Weight: 70kg

[1385] Height: 170cm

[1386] Allergies: None

[1387] Food preferences: Likes vegetables, dislikes fatty foods

[1388] Goal: Lose 5kg in 1 month

[1389] Output format:

[1390] Please suggest a daily meal plan, recipes, and exercise menu.

[1391] The generative AI model generates the optimal health plan (meal recipes and exercise menu) for the user. For example, it suggests a green smoothie for breakfast, a chicken breast salad for lunch, and tofu steak for dinner. It also generates an exercise menu based on vital sensor data, such as 30 minutes of running, 3 sets of 15 push-ups, and 3 sets of 20 squats every day.

[1392] The generated plan is sent from the server to the device, which displays it to the user. The user can then review the provided meal and training menu and put it into practice. The device also synchronizes data from vital sensors in real time to record the user's progress.

[1393] The user inputs their diet and exercise progress into the application, and this feedback data is sent from the device to the server. The server then analyzes this feedback data and adjusts the diet and training menu accordingly based on the user's progress.

[1394] This allows users to receive a personalized health plan based on their health status and goals, supporting continuous health management. The system synchronizes data in real time and utilizes generative AI models to provide users with the optimal health promotion plan.

[1395] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1396] Step 1:

[1397] Users launch the application using a smartphone or tablet device and input their weight, height, allergy information, health goals such as weight loss or muscle gain, and favorite and disliked foods. This input data is later analyzed by the server, so accurate input is required.

[1398] Input: User's health status (weight, height, allergy information, etc.) and health goals

[1399] Output: Formatted user data

[1400] Step 2:

[1401] The terminal receives data entered by the user and converts it into an appropriate format, for example, serializing the input data into JSON format and preparing it for transmission to the server, while also checking the integrity of the data.

[1402] Input: Raw data entered by the user

[1403] Output: User data in JSON format

[1404] Step 3:

[1405] The terminal sends the formatted data to the server via a secure communication method (e.g., HTTPS), and receives a response from the server to confirm whether the communication was successful.

[1406] Input: User data in JSON format

[1407] Output: Data transmission result to server (success / failure)

[1408] Step 4:

[1409] The server analyzes the received user data and generates prompts for the generative AI model, which includes the user's health status and goals, and the generative AI model then generates an optimal health plan based on this.

[1410] Input: User data received by the server

[1411] Output: Prompt sentence to the generative AI model

[1412] Step 5:

[1413] The generative AI model generates optimal meal and training menus based on prompts, including specific ingredients, cooking instructions, nutritional information, exercise types, loads, repetitions, and reference video links.

[1414] Input: Prompt sentence for generative AI model

[1415] Output: A health plan (meal menu and training menu) that is optimal for the user

[1416] Step 6:

[1417] The server sends the generated health plan to the terminal in an appropriate format such as JSON, where the server checks the integrity of the data and ensures that the terminal receives it correctly.

[1418] Input: Health plan from a generative AI model

[1419] Output: Health plan transmission result to the device (success / failure)

[1420] Step 7:

[1421] The device analyzes the health plan received from the server and visually displays it to the user. The user can then check the provided meal and training menus on the application screen and put them into practice.

[1422] Input: Health plan received from the server

[1423] Output: Health plan user interface display

[1424] Step 8:

[1425] Users enter their diet and exercise progress into the application, which also syncs vital sensor data in real time to record their progress, including heart rate, steps taken, and calories burned.

[1426] Input: User's diet and exercise progress data, vital sensor data

[1427] Output: Formatted feedback data

[1428] Step 9:

[1429] The device sends the user's feedback data to a server, which then analyzes the data and adjusts the user's diet and training accordingly based on their progress, enabling the provision of a continuous, personalized health plan.

[1430] Input: User feedback data

[1431] Output: Coordinated Health Plan

[1432] (Application example 1)

[1433] 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."

[1434] While traditional health management systems suggest meal and training menus based on a user's individual health status and goals, they lack the ability to order specific meals. This requires users to purchase ingredients and cook the meals themselves to achieve the suggested menus, making them time-consuming and impractical. Furthermore, the lack of feedback and subsequent adjustments based on the user's individual progress makes effective health management in real time difficult.

[1435] 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.

[1436] In this invention, the server includes an input means for inputting the user's health condition and goals, a transmission means for transmitting the user data received from the input means to the server, an analysis means for the server to analyze the user data and calculate individual nutrient needs, and a generation means for generating appropriate meal menus and training menus based on the analysis results. This makes it possible to propose and directly order individualized meal menus according to the user's health condition and goals, thereby realizing efficient health management.

[1437] An "input means" is a device or method for a user to input information such as their health status and goals.

[1438] The "transmitting means" is a device or method for transmitting the user data received from the input means to the server.

[1439] The "analysis means" refers to a device or method by which the server analyzes user data and calculates the nutritional and exercise information required by each individual user.

[1440] The "generation means" is a device or method for generating appropriate meal menus and training menus based on the analysis results.

[1441] The "output means" is a device or method for transmitting the menu generated by the generating means to a terminal.

[1442] The "display means" is a device or method by which the terminal displays the menu to the user.

[1443] A "feedback means" is a device or method for inputting the progress of the diet and exercise a user has undertaken.

[1444] The "ordering means" refers to a device or method that allows a user to place an order with a service that provides the proposed meal menu as is.

[1445] This invention is a system that provides a personalized health promotion plan based on a user's health status and goals. The system transmits data entered by the user to a server, which analyzes the data to generate an optimal nutrition plan and training menu, which are then provided to the user via a terminal. A specific embodiment of this system is described below.

[1446] System configuration

[1447] 1. User Roles

[1448] Users can launch the application on their smartphone or tablet and input their health status and goals, such as weight, height, allergy information, and goals such as weight loss or muscle gain. They can also input their favorite and least favorite foods.

[1449] 2. Role of the terminal

[1450] The terminal is responsible for receiving data entered by the user and transmitting it to the server, where it is properly formatted and sent to the server via secure communications.

[1451] 3. Server Roles

[1452] The server analyzes the received user data and uses the generative AI to calculate the nutritional needs of each individual. This creates a personalized nutrition plan based on the user's health status and goals. The server also accesses a nutrition database to select ingredients that are appropriate for the user's nutritional needs. Based on the selected ingredients, the generative AI creates a cooking recipe tailored to the user. This recipe includes specific amounts of ingredients, cooking instructions, and the nutritional value of the dish.

[1453] Similarly, when it comes to training menus, the server analyzes vital sensor data to evaluate the user's athletic ability and muscle mass. Based on the evaluation results, the generation AI creates an appropriate training menu. This menu includes specific exercise steps, load, number of repetitions, and reference video links. This generated menu information is sent from the server to the device.

[1454] 4. Terminal Processing

[1455] The device receives the data sent from the server and displays it to the user. The user can then check and follow the cooking recipes and training menus provided through the application. The device also periodically syncs data from vital sensors and records the user's progress.

[1456] 5. User Feedback

[1457] Users input their diet and exercise progress into the application. They can then place an order from the service, which provides the suggested meal menu as is. It is also possible to automatically synchronize vital sensor values ​​with the application. The feedback data is sent from the device to the server and analyzed again. This cycle allows the diet and training menu to be adjusted appropriately according to the user's progress.

[1458] Specific examples

[1459] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The server analyzes the user's current weight, height, allergies, food preferences, etc., and generates a low-calorie, nutritionally balanced meal menu. It also calculates basal metabolic rate and calories burned during exercise based on data from vital sensor data, and provides a menu of moderate-intensity aerobic exercise and muscle training that matches these.

[1460] Prompt Sentence Examples

[1461] Create a daily meal plan based on your health and goals.

[1462] Current weight: 70kg

[1463] Height: 175cm

[1464] Goal: Lose 5kg in 1 month

[1465] Allergies: None

[1466] Favorite foods: Chicken, broccoli, yogurt

[1467] Disliked ingredients: None

[1468] In this way, the system of the present invention can flexibly respond to the health status and goals of each user and provide a personalized health promotion plan, which is useful for supporting the user's life in a healthier and more efficient manner.

[1469] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1470] Step 1:

[1471] A user launches the application using a smartphone or tablet and inputs their health status and goals, including weight, height, allergies, food preferences, and goals such as weight loss or muscle gain. This generates user input data.

[1472] Step 2:

[1473] The terminal receives the user's input data and sends it to the server in an appropriate format. The input data is formatted in JSON format or similar and sent to the server via a secure communication method (e.g., HTTPS). This transfers the data to the server.

[1474] Step 3:

[1475] The server analyzes the received user data and uses a generative AI model to calculate individual nutritional needs. In this step, the user data is converted into a prompt sentence and input into a generative AI model (e.g., GPT-3). The generative AI model analyzes the prompt sentence and outputs information on required nutrients and exercise.

[1476] Step 4:

[1477] The server generates appropriate meal and training menus based on the analysis results. The server accesses a nutritional database and selects ingredients that are appropriate for the user's nutritional needs. It also analyzes data from vital sensors to evaluate the user's athletic ability and muscle mass. This allows it to generate specific menus and training plans.

[1478] Step 5:

[1479] The server sends the generated menu to the terminal, where it is properly formatted and sent to the terminal via secure communication means, thereby transferring the menu information to the terminal.

[1480] Step 6:

[1481] The device displays the received data to the user. The provided cooking recipes and training menus can be viewed on the application screen. The user can then refer to the specific steps for carrying out these menus.

[1482] Step 7:

[1483] The user orders the suggested meal menu from the food delivery service. The user selects the suggested meal menu from the terminal and completes the order procedure with the food delivery service. The necessary ingredients and food are then delivered to the user's home.

[1484] Step 8:

[1485] The user inputs the progress of their diet and exercise, and the device sends it to the server. The user inputs the details of their exercise into the application, and the device sends it to the server. This transfers the progress data to the server.

[1486] Step 9:

[1487] The server analyzes the progress data and generates new menus and training plans as appropriate. The server then analyzes the vital sensor data again and generates new menus and plans based on the user's progress. This allows the user's health promotion plan to be updated as appropriate.

[1488] Through these steps, a personalized health promotion plan is provided based on the user's health condition and goals, enabling the user to effectively manage their health.

[1489] 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.

[1490] This invention is a system that provides an optimal health promotion plan based on the user's health condition, goals, and even emotional state. Below, we will explain the program of this system and introduce its operation with specific examples.

[1491] Specific system construction

[1492] 1. User Roles

[1493] A user opens a health management application on a smartphone or tablet. Within the application, the user enters their basic information, health goals (e.g., weight loss, muscle gain, etc.), food preferences, allergy information, etc. The user can also communicate their emotions by inputting voice and facial expressions using a microphone and camera. Emotions can also be input using the text chat function.

[1494] 2. Role of the terminal

[1495] The device receives basic data and health goals entered by the user, as well as voice, facial expressions, and text data, and then appropriately formats and transmits the data to the server. This data includes weight, height, goals, food preferences, allergy information, and the user's emotional state.

[1496] 3. Server Roles

[1497] The server receives user data sent from the device. The server first analyzes basic data and health goals, and uses generative AI to calculate the appropriate nutrients for each user. Next, the server's evaluation means analyzes vital data and evaluates athletic ability and muscle mass.

[1498] Furthermore, the server uses an emotion engine to analyze emotions from the user's voice, facial expressions, and text data, and uses this emotional information as auxiliary data for the generative AI to calculate nutritional intake and evaluate athletic ability.

[1499] 4. Create a meal and training menu

[1500] The server selects appropriate ingredients based on the analysis results, and the generative AI creates a cooking recipe tailored to the user based on the nutrition database and emotional data. The recipe includes ingredients, specific amounts, cooking instructions, and nutritional information.

[1501] Similarly, training menus are generated based on vital sensor data and emotional data, and include exercise instructions, load, number of repetitions, and reference video links.

[1502] The generated menu information is transmitted from the server to the terminal.

[1503] 5. Terminal Processing

[1504] The device receives the data sent from the server and displays it to the user. The user can then use the application to check the provided cooking recipes and training menus and follow the instructions. The device also periodically syncs data from vital sensors to record the user's progress.

[1505] 6. User Feedback

[1506] Users input their diet and exercise progress into the application. The input data is saved on the device as feedback and sent to the server. Users can also input emotional information using voice, facial expressions, and text.

[1507] 7. Reanalysis and Adjustment

[1508] The server reanalyzes the received progress data and emotional information, regenerates meal and training menus as needed, and sends the improved plans to the device, providing a flexible health promotion plan that also takes the user's emotional state into account.

[1509] Introduction of specific examples

[1510] For example, consider a case where a user inputs a goal of "I want to lose 5 kg in one month." The system analyzes the user's emotional state each day (e.g., if the stress level is high) and adjusts the menu accordingly. On days when stress is high, it will provide recipes using ingredients that have a relaxing effect and suggest light exercise menus.

[1511] In this way, the system can provide a personalized health promotion plan that takes into account not only the user's health status and goals, but also their emotional state.

[1512] The processing flow will be explained below.

[1513] Step 1:

[1514] Users turn on their smartphone or tablet and open a health management application. Within the application, they input their health status (weight, height, allergies, etc.) and goals (e.g., weight loss, muscle gain, etc.), as well as their favorite and least favorite foods.

[1515] Step 2:

[1516] Users can communicate their emotional state to the system by inputting voice and facial expressions using a microphone and camera, and can also input emotions using the text chat function.

[1517] Step 3:

[1518] The device receives data entered by the user (health status, goals, food preferences, allergy information, voice, facial expressions, text data), formats it appropriately, and sends it to the server.

[1519] Step 4:

[1520] The server receives user data sent from the device, analyzes it regarding health status and goals, and then uses generative AI to calculate appropriate nutrients.

[1521] Step 5:

[1522] The server then uses an emotion engine to analyze emotions from the user's voice, facial expressions, and text data. Based on the analysis results, the emotional information is used as auxiliary data for the generative AI.

[1523] Step 6:

[1524] The server accesses a nutrition database and selects appropriate ingredients based on the user's nutritional needs. Taking into account the user's emotional data, the generative AI creates a cooking recipe tailored to the user. The recipe includes ingredients, specific serving sizes, cooking instructions, and nutritional information.

[1525] Step 7:

[1526] The server analyzes data from vital sensors to evaluate athletic ability and muscle mass. Taking emotional data into account, the generative AI creates an appropriate training menu. The menu includes exercise instructions, load, number of repetitions, and links to reference videos.

[1527] Step 8:

[1528] The server sends the generated cooking recipes and training menus to the terminal.

[1529] Step 9:

[1530] The device receives the data sent from the server and displays it to the user, who can then use the application to check and implement the provided cooking recipes and training menus.

[1531] Step 10:

[1532] The user inputs the progress of their diet and exercise into the application. They also update their emotional information by inputting voice and facial expressions. This data is stored on the device.

[1533] Step 11:

[1534] The terminal transmits the progress data and updated emotion information to the server.

[1535] Step 12:

[1536] The server reanalyzes the received progress data and emotional information, regenerates meal and training menus as needed, and sends the improved plans to the device.

[1537] Step 13:

[1538] The terminal receives the regenerated plan and displays it to the user, who then proceeds with the next execution based on it.

[1539] The above are the specific processing steps for the user, the terminal, and the server. This system provides an optimal personalized health promotion plan that takes into account the user's health condition, goals, and daily emotional state.

[1540] Example 2

[1541] 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."

[1542] In modern society, it is important to create effective health promotion plans tailored to individual health conditions and goals. However, these plans require the consideration of a wide range of individual data, making it particularly difficult to reflect the user's emotional state. Furthermore, there is a need for real-time feedback on whether the menus and training plans provided are appropriate for the user's current condition, and adjustments should be made accordingly. This will enable more personalized health management.

[1543] 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.

[1544] In this invention, the server includes input means for inputting the user's health condition and goals, transmission means for transmitting the user data received from the input means, analysis means for analyzing the user data and calculating each individual's required nutrients, emotion analysis means for analyzing the user's voice, facial expression, and text data and evaluating their emotional state, generation means for generating appropriate meal and training menus based on the analysis results and their emotional state, output means for transmitting the generated menus to the terminal, display means for the terminal to display the menus to the user, and feedback means for inputting the user's progress in diet and exercise. This makes it possible to provide a personalized health promotion plan that takes into account the user's individual health condition and emotional state.

[1545] "Input means" refers to a device or function that allows a user to input their health status and goals.

[1546] "Transmission means" refers to a device or function for transmitting data received from a user to a server.

[1547] "Analysis means" refers to a device or function for analyzing received user data and calculating individual required nutrients.

[1548] "Emotion analysis means" refers to a device or function for analyzing a user's voice, facial expression, and text data to evaluate their emotional state.

[1549] "Generation means" refers to a device or function for generating appropriate meal menus and training menus based on the analysis results and emotional state.

[1550] The "output means" refers to a device or function for transmitting the menu generated by the generation means to the terminal.

[1551] "Display means" refers to a device or function for displaying the transmitted menu to the user.

[1552] "Feedback means" refers to a device or function that allows a user to input progress on diet and exercise.

[1553] "Vital data" refers to data containing the user's biological information, such as heart rate and calorie consumption.

[1554] "Synchronization means" refers to a device or function for synchronizing data from a user's vital sensors with a terminal.

[1555] This invention is a system that provides an optimal health promotion plan based on a user's health status, goals, and even emotional state. This system is built around a health management application that uses a smartphone or tablet device, and related server-side processing.

[1556] User Roles

[1557] First, the user launches the health management application on their smartphone or tablet. Within the app, they enter the following information:

[1558] Basic information (e.g. weight, height)

[1559] Health goals (e.g., lose 5 kg in one month)

[1560] Food preferences (e.g., mainly Japanese food, lots of vegetables)

[1561] Allergy information (e.g., peanut allergy)

[1562] Emotional information (e.g., voice input via microphone, facial expression recognition via camera, or text chat)

[1563] Device Role

[1564] The terminal takes this information entered by the user and formats the data as follows:

[1565] Basic information, health goals, food preferences, allergy information: formatted in JSON format

[1566] Emotional information: Analyzes audio and image data and converts them into numerical values ​​or text

[1567] The formatted data is sent to the server using the HTTPS protocol.

[1568] Server Roles

[1569] The server receives the data sent from the device and analyzes it through the following process:

[1570] Store basic information and health goals in a database

[1571] Uses generative AI models to calculate appropriate nutrients based on health goals

[1572] Analyze vital data (e.g., heart rate, calorie consumption) to evaluate athletic ability and muscle mass

[1573] Use an emotion engine to analyze emotions from voice, facial expressions, and text data and store them as numerical values.

[1574] Examples of prompts to input to a generative AI model include:

[1575] "A 30-year-old, 170cm tall, 70kg person wants to lose 5kg in one month. Their stress level is high. Please provide them with an appropriate diet and exercise plan."

[1576] Menu generation

[1577] The server generates the following information based on the analysis:

[1578] Cooking recipes: ingredients, specific amounts, cooking instructions, nutritional information

[1579] Training plan: Exercise procedure, load, number of repetitions, reference video link

[1580] The system takes emotional data into consideration and creates a plan that includes foods with a relaxing effect and light exercise. The generated information is sent to the device in JSON format.

[1581] Displaying data on the device

[1582] The device receives the meal and training menu sent from the server and displays it to the user as follows:

[1583] The recipe screen displays the name of the dish, ingredients, serving size, cooking instructions, and nutritional information in a list format.

[1584] The training screen displays the exercise name, load, number of repetitions, and a link to a reference video.

[1585] Based on this information, users can put the provided meal recipes and training menus into practice.

[1586] User Feedback

[1587] The user then re-enters their diet and exercise progress into the application using checkboxes, text, voice input, facial expression recognition, etc. This feedback data is also sent back to the server, which then re-analyzes it.

[1588] Reanalysis and adjustments

[1589] The server then analyzes the received feedback data and generates new diet and training plans as needed, providing an optimal health promotion plan tailored to the individual user's health and emotional state.

[1590] As described above, this system provides more personalized health management for users, and supports them effectively and flexibly.

[1591] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1592] Step 1:

[1593] The user launches the health management application using a smartphone or tablet device and enters the following information within the application:

[1594] Basic information (e.g. weight, height)

[1595] Health goals (e.g., lose 5 kg in one month)

[1596] Food preferences (e.g., mainly Japanese food, lots of vegetables)

[1597] Allergy information (e.g., peanut allergy)

[1598] Emotional information (e.g., voice input via microphone, facial expression recognition via camera, or text chat)

[1599] Input: User's individual data (basic information, health goals, food preferences, allergy information, emotional information)

[1600] Output: None (data entry complete)

[1601] Step 2:

[1602] The terminal takes the information entered by the user and formats the data as follows:

[1603] Basic information, health goals, food preferences, allergy information: formatted in JSON format

[1604] Emotional information: Analyzes audio and image data and converts them into numerical values ​​or text

[1605] Input: Individual data entered by the user

[1606] Output: Formatted data (JSON format)

[1607] Step 3:

[1608] The terminal sends the formatted data to the server using the HTTPS protocol.

[1609] Input: Formatted data (JSON format)

[1610] Output: Data sent to the server

[1611] Step 4:

[1612] The server receives the data sent from the device and performs the following processes:

[1613] Store basic information and health goals in a database

[1614] Uses generative AI models to calculate appropriate nutrients based on health goals

[1615] Analyze vital data (e.g., heart rate, calorie consumption) to evaluate athletic ability and muscle mass

[1616] Use an emotion engine to analyze emotions from voice, facial expressions, and text data and store them as numerical values.

[1617] Input: Data sent from the terminal

[1618] Output: Analysis results (nutrient calculation, vital data evaluation, emotion analysis results)

[1619] Step 5:

[1620] The server generates the following information based on the analysis:

[1621] Cooking recipes: ingredients, specific amounts, cooking instructions, nutritional information

[1622] Training plan: Exercise procedure, load, number of repetitions, reference video link

[1623] Input: Analysis results

[1624] Output: Generated menu information (cooking recipes, training plans)

[1625] Step 6:

[1626] The server sends the generated menu information to the terminal in JSON format.

[1627] Input: Generated menu information (cooking recipes, training plans)

[1628] Output: Menu information sent to the device

[1629] Step 7:

[1630] The terminal receives the menu information sent from the server and displays it to the user as follows:

[1631] The recipe screen displays the name of the dish, ingredients, serving size, cooking instructions, and nutritional information in a list format.

[1632] The training screen displays the exercise name, load, number of repetitions, and a link to a reference video.

[1633] Input: Menu information sent from the server

[1634] Output: Menu information displayed to the user

[1635] Step 8:

[1636] Users can enter their progress in diet and exercise into the application. For example, they can record their dietary achievements and changes in their physical condition using text or check boxes, and provide feedback using voice or facial expressions.

[1637] Input: User progress data (diet and exercise achievements and feedback)

[1638] Output: Feedback data input completed

[1639] Step 9:

[1640] The terminal transmits the feedback data input by the user to the server.

[1641] Input: Feedback data

[1642] Output: Feedback data sent to the server

[1643] Step 10:

[1644] The server then re-analyzes the received feedback data and generates new diet and training plans as needed, again involving generative AI models and sentiment analysis.

[1645] Input: Feedback data

[1646] Output: Revised Health Promotion Plan

[1647] As described above, the program of this system performs specific data processing and calculation for each step, and provides the user with an optimal health promotion plan.

[1648] (Application example 2)

[1649] 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."

[1650] Modern health management systems focus on providing dietary and training menus tailored to the user's health status and goals, but lack comprehensive support that takes into account the user's emotional state. In particular, it is difficult for physical stores to provide real-time product recommendations tailored to the user's current health and emotional state. This makes it difficult for users to easily find the products that best suit them, leading to ineffective health management.

[1651] The identification process by the identification 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 emotion analysis means that analyzes the emotional state of the user in real time and proposes product information according to the emotional state, and a presentation means that provides product information in the store. This allows the user to receive product proposals in real time in the physical store that are tailored to their health condition and emotional state.

[1652] Definitions of important words

[1653] A "user" is someone who inputs information such as health status, goals, and emotional state.

[1654] "Health status" refers to the user's current physical condition.

[1655] A "goal" is a health-related objective that a user wishes to achieve.

[1656] "Input means" refers to a method or device that allows a user to input information such as health status and goals.

[1657] The "transmission means" refers to a method or device for transmitting input user data to a server.

[1658] A "server" is a device or system that receives and analyzes user data and provides the necessary information.

[1659] The "analysis means" refers to a method or device by which the server analyzes user data and calculates the necessary information.

[1660] The "generation means" refers to a method or device that generates an appropriate menu based on the analysis results.

[1661] "Output means" refers to a method or device for transmitting the generated menu to a terminal.

[1662] A "terminal" is a device used by a user, such as a smartphone or tablet.

[1663] "Display means" refers to a method or device by which the terminal displays a menu to the user.

[1664] "Feedback means" refers to a method or device for inputting the progress of the user's diet and exercise.

[1665] "Emotional state" refers to the user's mental and emotional state.

[1666] An "emotion analysis means" is a method or device for analyzing a user's emotional state in real time.

[1667] "Presentation means" refers to a method or device for providing product information within a store.

[1668] "Product information" is information about products that are suggested based on the user's health condition and emotional state.

[1669] The "synchronization means" is a method or device for matching data from vital sensors between the terminal and the server.

[1670] MODE FOR CARRYING OUT THE INVENTION

[1671] The system embodying the present invention provides a personalized health shopping assistant using smart glasses. The specific system construction method and operation will be described below.

[1672] System Configuration

[1673] 1. Hardware and Software

[1674] Smart Glasses: Use common smart glasses (e.g., Google Glass, Vuzix Blade).

[1675] Backend server: Uses cloud services (e.g., AWS EC2, Amazon RDS) and deploys generative AI models (e.g., TensorFlow).

[1676] Front-end app: Use a smartphone application (e.g., Android / iOS application, React Native).

[1677] Sensor devices: Use common vital sensors such as heart rate monitors and blood pressure monitors.

[1678] Data analysis tools: Use Python, Pandas, Scikit-learn, etc.

[1679] System Processing

[1680] 2. Data Entry

[1681] Through a smartphone application, users input their health status, health goals, food preferences, allergy information, etc. This data is then synced to the smart glasses via Bluetooth.

[1682] 3. Server Processing

[1683] The server receives user data and analyzes it using a generative AI model. The analysis includes the user's health status, vital data, and emotional state. The generative AI model calculates optimal nutrients for each individual and generates appropriate meal and training menus.

[1684] 4. Emotion analysis

[1685] The smart glasses use a built-in camera and microphone to capture the user's facial expressions and voice in real time, and then analyze them using an emotion analysis engine. The analysis results are sent to a server and used to adjust the health promotion plan.

[1686] 5. Product proposal

[1687] When a user approaches a particular product in a store, the smart glasses' display will display information about the product and its fit with health goals, such as, "This spinach is rich in vitamin C, which helps relieve stress."

[1688] Specific examples

[1689] If a user is looking to "relieve stress," the smart glasses will display a message while browsing vegetables in a store saying, "This banana is rich in potassium, which helps to relieve fatigue," allowing users to quickly find products that fit their health goals.

[1690] Prompt Sentence Examples

[1691] For example, you can use the following prompt sentences in your generative AI model:

[1692] User information: Age 35, Gender female, Weight 60kg, Height 165cm

[1693] Goal: Lose 3kg in 2 months and relieve stress

[1694] Food preferences: Vegetable-loving, gluten-free

[1695] Emotional state: High stress level (facial recognition), low audio tension

[1696] Recipe provided:

[1697] This user is recommended a banana and spinach smoothie, which is rich in potassium and vitamin C. Generate the following recipe:

[1698] (Banana and spinach smoothie)

[1699] material:

[1700] 1 banana

[1701] 1 cup spinach

[1702] 200ml almond milk

[1703] 1 teaspoon honey

[1704] Instructions:

[1705] 1. Cut the banana into small pieces.

[1706] 2. Add to blender along with spinach.

[1707] 3. Add the almond milk and blend until smooth.

[1708] 4. Add honey and blend again.

[1709] 5. Chill before drinking.

[1710] This invention allows users to receive product suggestions in real time within a physical store that are tailored to their health and emotional state, enabling them to manage their health more effectively.

[1711] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1712] Program processing steps

[1713] Step 1:

[1714] Data Entry

[1715] A user inputs data such as health status, health goals, food preferences, and allergy information through a smartphone application. This data includes, for example, weight, height, and goals (e.g., weight loss, muscle gain). This input data is synchronized to the smart glasses via Bluetooth. The input data is sent from the smartphone application to a server by a transmission means and stored on the server.

[1716] Step 2:

[1717] Data analysis on the server

[1718] The server analyzes the received user data. What is particularly important here is the use of a generative AI model. The generative AI model analyzes the user's basic data (e.g., weight, height, goals) and calculates the optimal nutrients for each individual. It receives the user's basic data as input and outputs data that is useful for nutrient balance and weight management. The analysis results are stored on the server for use in the next step.

[1719] Step 3:

[1720] Emotion analysis

[1721] The device (smart glasses) uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. This data is sent to a server via an emotion analysis engine, which analyzes the user's emotional state. It receives the user's facial and voice data as input and outputs their emotional state (e.g., stress level, elation, depression). The analysis results of the emotional state are used as auxiliary data for the generative AI model.

[1722] Step 4:

[1723] Meal and training menu generation

[1724] The server uses the generative AI model to generate appropriate meal and training menus based on the analysis results. Specifically, it references a nutrition database and automatically generates optimal cooking recipes and exercise plans for the user. It receives the nutrition database and the user's analysis results as input, and outputs meal and exercise menus. The generated menu information is stored on the server.

[1725] Step 5:

[1726] Sending and displaying menu information

[1727] The server transmits the generated menu information to a terminal (smartphone or smart glasses) via a transmission means. The terminal displays the received menu information to the user using a display means. This allows the user to refer to and carry out the suggested meal menu or training menu. The menu information is received as input from the server and displayed via a user interface.

[1728] Step 6:

[1729] Real-time product information display

[1730] The terminal (smart glasses) uses RFID tags and beacons to display product information in real time within the store. When a user approaches a particular product, the smart glasses display information about the product and its suitability for health goals. For example, the glasses may display information such as, "This spinach is rich in vitamin C, which helps relieve stress." The glasses receive data from beacons and RFID tags as input and display product information.

[1731] Step 7:

[1732] Feedback collection

[1733] The user inputs their diet and training progress as feedback via a smartphone application. The data input through the feedback means is sent back to the server and stored on the server. This data is used for the next adjustment of the health plan. The system receives the user's feedback data as input and sends the progress to the server.

[1734] Step 8:

[1735] Reanalysis and plan adjustments

[1736] The server re-analyzes the feedback data and emotional data and re-generates the meal and training menu as necessary. This makes it possible to provide an optimal plan that takes into account the user's health and emotional state. It receives the feedback data and emotional data as input and re-outputs the adjusted menu.

[1737] 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.

[1738] 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.

[1739] 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.

[1740] 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.

[1741] 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.

[1742] 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.

[1743] 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).

[1744] 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.

[1745] 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."

[1746] 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.

[1747] 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).

[1748] 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.

[1749] 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.

[1750] 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.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] 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 various processors as hardware resources.

[1755] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1756] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1757] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1758] The following is further disclosed regarding the above embodiment.

[1759] (Claim 1)

[1760] an input means for inputting a user's health status and goals;

[1761] a transmitting means for transmitting the user data received from the input means to a server;

[1762] analysis means for the server to analyze the user data and calculate individual nutrient requirements;

[1763] A generating means for generating an appropriate meal menu and training menu based on the analysis result;

[1764] an output means for transmitting the menu generated by the generation means to a terminal;

[1765] a display means for displaying the menu to a user in the terminal;

[1766] a feedback means for inputting the progress of the diet and exercise carried out by the user;

[1767] A system including:

[1768] (Claim 2)

[1769] The system according to claim 1, wherein the server further comprises an evaluation means for analyzing the user's vital data and evaluating athletic ability and muscle mass.

[1770] (Claim 3)

[1771] The system of claim 1 , wherein the terminal further comprises synchronization means for synchronizing data from the user's vital sensors.

[1772] "Example 1"

[1773] (Claim 1)

[1774] an input means for inputting a user's health status and goals;

[1775] a transmitting means for transmitting the user data received from the input means to a server;

[1776] analysis means for the server to analyze the user data and calculate individual nutrient requirements;

[1777] A generating means for using the generative AI model to generate an appropriate meal menu and training menu based on the analysis results;

[1778] an output means for transmitting the menu generated by the generation means to a terminal;

[1779] a display means for displaying the menu to a user in the terminal;

[1780] a feedback means for inputting and providing feedback on the progress of the diet and exercise carried out by the user;

[1781] a synchronization means for synchronizing data from the vital sensors in real time;

[1782] A system including:

[1783] (Claim 2)

[1784] The system according to claim 1, wherein the server further comprises an evaluation means for analyzing the user's vital data and evaluating athletic ability and muscle mass.

[1785] (Claim 3)

[1786] The system of claim 1 , wherein the terminal further comprises a synchronization means for synchronizing data from vital sensors in real time.

[1787] "Application Example 1"

[1788] (Claim 1)

[1789] an input means for inputting a user's health status and goals;

[1790] a transmitting means for transmitting the user data received from the input means to a server;

[1791] analysis means for the server to analyze the user data and calculate individual nutrient requirements;

[1792] A generating means for generating an appropriate meal menu and training menu based on the analysis result;

[1793] an output means for transmitting the menu generated by the generation means to a terminal;

[1794] a display means for displaying the menu to a user in the terminal;

[1795] a feedback means for inputting the progress of the diet and exercise carried out by the user;

[1796] ordering means for placing an order for the service that proposes and provides the meal menu;

[1797] A system including:

[1798] (Claim 2)

[1799] The system according to claim 1, wherein the server further comprises an evaluation means for analyzing the user's vital data and evaluating athletic ability and muscle mass.

[1800] (Claim 3)

[1801] The system of claim 1 , wherein the terminal further comprises synchronization means for synchronizing data from the user's vital sensors.

[1802] "Example 2: Combining Emotion Engines"

[1803] (Claim 1)

[1804] an input means for inputting a user's health status and goals;

[1805] a transmitting means for transmitting the user data received from the input means to a server;

[1806] analysis means for the server to analyze the user data and calculate individual nutrient requirements;

[1807] emotion analysis means for analyzing the user's voice, facial expression, and text data to evaluate the user's emotional state;

[1808] A generating means for generating an appropriate meal menu and training menu based on the analysis result and the emotional state;

[1809] an output means for transmitting the menu generated by the generation means to a terminal;

[1810] a display means for displaying the menu to a user in the terminal;

[1811] a feedback means for inputting the progress of the diet and exercise carried out by the user;

[1812] A system including:

[1813] (Claim 2)

[1814] The system according to claim 1, wherein the server further comprises an evaluation means for analyzing the user's vital data and evaluating athletic ability and muscle mass.

[1815] (Claim 3)

[1816] The system of claim 1 , wherein the terminal further comprises synchronization means for synchronizing data from the user's vital sensors.

[1817] "Application example 2 when combining emotion engines"

[1818] (Claim 1)

[1819] an input means for inputting a user's health status and goals;

[1820] a transmitting means for transmitting the user data received from the input means to a server;

[1821] analysis means for the server to analyze the user data and calculate individual nutrient requirements;

[1822] A generating means for generating an appropriate meal menu and training menu based on the analysis result;

[1823] an output means for transmitting the menu generated by the generation means to a terminal;

[1824] a display means for displaying the menu to a user in the terminal;

[1825] a feedback means for inputting the progress of the diet and exercise carried out by the user;

[1826] emotion analysis means for analyzing the emotional state of the user in real time and suggesting product information according to the emotional state;

[1827] a presentation means for providing product information in a store;

[1828] A system including:

[1829] (Claim 2)

[1830] The system according to claim 1, wherein the server further comprises an evaluation means for analyzing the user's vital data and evaluating athletic ability and muscle mass.

[1831] (Claim 3)

[1832] The system of claim 1 , wherein the terminal further comprises synchronization means for synchronizing data from the user's vital sensors. [Explanation of symbols]

[1833] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting a user's health status and goals; a transmitting means for transmitting the user data received from the input means to a server; analysis means for the server to analyze the user data and calculate individual nutrient requirements; A generating means for generating an appropriate meal menu and training menu based on the analysis result; an output means for transmitting the menu generated by the generation means to a terminal; a display means for displaying the menu to a user in the terminal; a feedback means for inputting the progress of the diet and exercise carried out by the user; A system including:

2. The system according to claim 1 , wherein the server further comprises an evaluation means for analyzing the user's vital data and evaluating the user's athletic ability and muscle mass.

3. The system of claim 1 , wherein the terminal further comprises synchronization means for synchronizing data from the user's vital sensors.

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