System

Abstract

To provide a system for supporting sustainable health management by providing an optimum diet plan to an individual user.SOLUTION: An input means for inputting a height, a weight, a body fat percentage, a target weight and a target body fat percentage of a user, a calculation means for creating a customized diet plan based on the input information using generative artificial intelligence, a generation means for generating a menu, a recipe and a list of required ingredients of a meal based on the diet plan, a display means for presenting the generated menu, recipe and list of ingredients to the user, an upload means for uploading a photograph of the meal consumed by the user, and an analysis means for analyzing the uploaded photograph; A system comprising: analysis means for estimating meal content and calories; modification means for generating a trajectory modified menu if total caloric intake exceeds a target; and assistance means for predicting a decrease in motivation and providing timely advice.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 today's busy lifestyles, it is extremely difficult to carry out effective dieting and health management tailored to individual health conditions and goals. Maintaining ongoing motivation, choosing the right food, and managing calories are particularly challenging for many people. This has led many people to abandon their dieting and health management efforts midway. A system is needed to solve this problem, provide optimal diet plans for individual users, and support sustainable health management. [Means for solving the problem]

[0005] The present invention provides a system including an input means for inputting a user's height, weight, body fat percentage, target weight, and target body fat percentage, and a calculation means for creating a customized diet plan based on the input information using artificial intelligence. The system further includes a generation means for generating a meal plan, recipes, and a list of ingredients based on the diet plan, a display means for presenting the generated meal plan, recipes, and ingredient list to the user, an upload means for uploading photos of the meals the user has eaten, an analysis means for analyzing the uploaded photos and estimating the meal contents and calories, a correction means for generating a menu that corrects the course if the total calorie intake exceeds the target, and a support means for predicting a decrease in motivation and providing timely advice. This system can efficiently support individual users' health management.

[0006] The "input means" is an interface for the user to input information such as his / her height, weight, body fat percentage, target weight, and target body fat percentage.

[0007] The "calculation means" is a function that uses generative artificial intelligence to create a customized diet plan based on information entered by the user.

[0008] The "generation means" is a function for generating a meal plan, recipes, and a list of necessary ingredients suitable for the user based on the diet plan created by the calculation means.

[0009] The "display means" is an interface for presenting the generated menu, recipes, and ingredient list to the user.

[0010] The "uploading means" is a function that allows a user to take a photo of the meal they have eaten and upload that photo to the system.

[0011] The "analysis means" is a function that analyzes uploaded photos of meals and estimates the contents and calories of the meal.

[0012] The "correction means" is a function for generating a menu that corrects the course when the total calorie intake exceeds the target.

[0013] "Support measures" are functions that predict declines in motivation and provide timely advice. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a health management system that inputs a user's height, weight, body fat percentage, target weight, and target body fat percentage and provides a customized diet plan based on the input. Specific embodiments for carrying out the present invention will be described below.

[0036] 1. System Configuration

[0037] This system consists of a terminal for users to input information, a server that performs calculations and analysis, and a terminal that outputs and displays data.

[0038] 2. Input Method

[0039] Users use a device such as a mobile phone or tablet to launch the application and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server.

[0040] 3. Means of calculation

[0041] Based on the information received, the server uses generative AI to generate a customized diet plan, applying algorithms that take into account the user's current health status and goals.

[0042] 4. Generation means

[0043] Based on the diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, allowing users to efficiently prepare and shop for ingredients.

[0044] 5. Display means

[0045] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, where the user can review and work on the actual meal plan.

[0046] 6. Upload Method

[0047] After eating a meal, users can use their device to take a photo of the meal and upload it to the system, allowing the system to record their calorie intake.

[0048] 7. Analysis method

[0049] The server analyzes the uploaded photos and estimates the contents and calories of the food depicted in them, which are then compared to the user's calorie goal and stored in a database.

[0050] 8. Remedies

[0051] The server detects when the user's total calorie intake exceeds their goal and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[0052] 9. Support methods

[0053] To prevent a decline in motivation, the server analyzes past data to learn patterns of declining motivation and then sends advice messages to users at appropriate times based on the results of that learning.

[0054] Specific examples

[0055] For example, suppose a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. When the user enters this information into the device and submits it, the server uses generative AI to create a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and a list of necessary ingredients is also automatically generated.

[0056] Users upload photos of the meals they eat each day, and the server analyzes them to estimate the calories. If the total calories consumed in a day exceeds the target, the server adjusts the menu for the next day to balance the calorie intake. In addition, when the server predicts that the user's motivation will decrease, it sends encouraging messages and specific action plans.

[0057] As described above, the system of the present invention provides users with consistent and personalized diet and health management support.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] A user starts the application using a mobile device or tablet. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage into an input form.

[0061] Step 2:

[0062] The terminal displays the entered information on the screen in real time and temporarily stores it after all the information has been entered.

[0063] Step 3:

[0064] The terminal transmits the temporarily stored user information data to the server.

[0065] Step 4:

[0066] The server stores the received user information data in a database.

[0067] Step 5:

[0068] The server retrieves the stored user information from the database and passes it to the generative AI engine.

[0069] Step 6:

[0070] The generative AI engine creates a customized diet plan based on the user's height, weight, body fat percentage, target weight and target body fat percentage.

[0071] Step 7:

[0072] The server generates a diet plan based on the user's preferences and lifestyle, along with meal plans, recipes, and a list of ingredients needed.

[0073] Step 8:

[0074] The server sends the generated menu, recipes, and ingredient list to the terminal.

[0075] Step 9:

[0076] The device displays meal plans, recipes, and ingredient lists to the user, who can then confirm the information and follow the meal plan.

[0077] Step 10:

[0078] After a user consumes a meal, they use their device to take a photo of the meal and upload it to the system.

[0079] Step 11:

[0080] The device sends photo data of the meal to the server.

[0081] Step 12:

[0082] The server analyzes the received photo data and estimates the contents and calories of the food depicted.

[0083] Step 13:

[0084] The server compares the estimated calories with the user's calorie restriction goal and stores the results in a database.

[0085] Step 14:

[0086] The server monitors the total calorie intake stored in the database, and if the target is exceeded, it instructs the generative AI engine to regenerate a revised menu.

[0087] Step 15:

[0088] The server sends the revised menu to the terminal and notifies the user.

[0089] Step 16:

[0090] The user can check the revised menu on the device and adjust their meal plan for the next day.

[0091] Step 17:

[0092] The server analyzes the user's past data and learns patterns of declining motivation.

[0093] Step 18:

[0094] The server predicts when motivation is likely to wane and generates encouraging messages and specific advice.

[0095] Step 19:

[0096] The server sends the generated encouraging message or advice to the terminal and notifies the user.

[0097] Step 20:

[0098] The user checks the message on their device and follows the advice provided.

[0099] The above steps will efficiently support the user's diet and health management.

[0100] Example 1

[0101] 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."

[0102] Conventional diet support systems have been unable to adequately support individual users' dietary management and calorie restriction, and have not provided specific methods for effectively achieving target weight or body fat percentage. Furthermore, they lacked the ability to predict and appropriately respond to users' declining motivation, making continuous health management difficult.

[0103] 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.

[0104] In this invention, the server includes input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage, transmission means for transmitting data to the server, calculation means for creating a customized diet plan based on the input information using artificial intelligence, generation means for generating a meal plan, recipes, and a list of necessary ingredients based on the diet plan, display means for presenting the generated meal plan, recipes, and ingredient list to the user, upload means for uploading photos of the meals the user has eaten, analysis means for analyzing the uploaded photos to estimate the meal contents and calories, correction means for generating a menu that corrects the user's calorie intake if the user's total calorie intake exceeds the target, and support means for predicting a decline in the user's motivation based on past data and providing advice. This allows the user to consistently follow the individually customized diet plan and effectively manage their health.

[0105] "Input means" refers to a device or function that allows a user to input information such as height, weight, body fat percentage, target weight, and target body fat percentage into the system.

[0106] "Transmission means" refers to a device or function that allows the terminal to transmit information entered by the user to the server.

[0107] "Generative artificial intelligence" refers to computational algorithms or models that create customized diet plans based on input information.

[0108] "Computational means" refers to the process or device that analyzes the received user information and generates a diet plan.

[0109] The "creation means" is a device or function for creating a meal plan, recipes, and a list of necessary ingredients based on the diet plan.

[0110] "Display means" refers to a device or function for visually presenting the generated menu, recipe, and ingredient list to the user.

[0111] "Uploading means" refers to a device or function that allows a user to send photos of the food they have eaten to the system.

[0112] "Analysis means" refers to a device or function that analyzes uploaded photos and estimates the contents and calories of meals.

[0113] The "correction means" is a device or function for generating a menu that corrects the course when the total calorie intake exceeds the target.

[0114] "Support tools" are devices or functions that predict a decline in a user's motivation based on past data and provide advice at the appropriate time.

[0115] The present invention is a system developed to support users' health management, which generates a customized diet plan based on information input by the user and supports continuous health management. The following describes in detail the embodiments of the present invention.

[0116] This system consists of a terminal for users to input information, a server that receives and analyzes the data, and a terminal for displaying the generated information.

[0117] Input Method

[0118] The user launches a dedicated application using a device such as a mobile phone or tablet. In the application, basic information such as height, weight, body fat percentage, target weight and target body fat percentage is entered, and this information is temporarily saved on the device. After entering the information, the user presses the "Send" button, and the entered information is sent to the server.

[0119] Transmission method

[0120] The device sends the stored user basic information to the server. This data is sent using a secure communication protocol (e.g. HTTPS).

[0121] means of calculation

[0122] The server then uses the received data to create a customized diet plan using a generative AI model that has previously learned various health data to generate the optimal plan based on the user's current health condition and goals.

[0123] generation means

[0124] The server generates daily or weekly meal plans, recipes, and ingredient lists based on the customized diet plan, a process that includes retrieving recipe and ingredient information from a database.

[0125] Display means

[0126] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, allowing the user to follow the actual meal plan.

[0127] Upload method

[0128] After consuming a meal, the user uses the device to take a photo of the meal and upload it to the system. The uploading operation is performed within the application, and the photo data is sent to the server.

[0129] Analysis means

[0130] The server analyzes the uploaded photos and estimates the food contents and calories of the food depicted in the photos using image recognition algorithms, and the results of the analysis are stored in a database.

[0131] Correction means

[0132] If the user's total calorie intake exceeds their goal, the server will use that information to regenerate a revised menu, which will then be sent back to the device and displayed.

[0133] Support means

[0134] The server analyzes past data to learn patterns of when users lose motivation, and then uses an AI model to generate advice messages to help users maintain their motivation at the right time and send them to their devices.

[0135] Specific examples

[0136] For example, consider a user who is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. The user enters this information into the device and presses the "Send" button. Based on the received information, the server uses a generative AI model to create a diet plan that includes calorie restriction and appropriate exercise. The plan includes specific menus and recipes for breakfast, lunch, and dinner, and an automatically generated list of ingredients is also included. The user uploads photos of their daily meals, and the server analyzes the photos to estimate calories. If the total daily calorie intake exceeds the target, the server adjusts the menu for the next day. The server also sends encouraging messages and specific action plans at times when the user's motivation is predicted to wane.

[0137] Example prompts to input to the generative AI model

[0138] User's current height: 170 cm

[0139] User's current weight: 75 kg

[0140] User's current body fat percentage: 25%

[0141] User target weight: 65 kg

[0142] User's target body fat percentage: 15%

[0143] Use this information to generate a customized diet plan that includes daily and weekly meal plans, recipes, and ingredient lists.

[0144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0145] Step 1:

[0146] The user starts up the device and accesses the application. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage. The entered information is temporarily stored in the device's memory.

[0147] Input: User's height, weight, body fat percentage, target weight, target body fat percentage

[0148] Output: User information temporarily stored on the device

[0149] Specific behavior: The user enters the required information into the input form and presses the "Submit" button.

[0150] Step 2:

[0151] The terminal uses a secure protocol (e.g., HTTPS) to send the input data to the server. The terminal generates JSON-formatted data as a transmission method and sends it to the server using an HTTP POST request.

[0152] Input: User information temporarily stored on the device

[0153] Output: User information sent to the server

[0154] Specific operation: When the "Send" button is pressed, the device generates data in JSON format, issues an HTTP request, and sends it to the server.

[0155] Step 3:

[0156] The server stores the received user information in a database and uses a generative AI model to create a customized diet plan. Based on the received information, the server inputs prompts into the AI ​​model to generate the diet plan.

[0157] Input: User information received by the server (JSON format)

[0158] Output: A customized diet plan

[0159] What it does: The server stores the data in a database and provides prompts to the AI ​​model to generate a diet plan.

[0160] Step 4:

[0161] The server analyzes the generated diet plan and generates daily and weekly meal plans, recipes, and ingredient lists, which are then encoded in JSON format and sent to the device.

[0162] Enter: your customized diet plan.

[0163] Output: JSON format meal plans, recipes, and ingredient lists

[0164] Specific operation: The server generates the necessary information based on the diet plan and encodes it for transmission to the device.

[0165] Step 5:

[0166] The device analyzes the received data and displays it in a visually easy-to-understand format for the user, allowing them to check the generated menu, recipes, and ingredient lists.

[0167] Input: JSON format data sent from the server

[0168] Output: Menus, recipes, and ingredient lists displayed on the device screen

[0169] Specific behavior: The device parses the JSON formatted data and displays the information based on the user interface.

[0170] Step 6:

[0171] After consuming a meal, the user uses the device to take a photo of the meal and upload it to the system. The user then presses the "upload" button in the application to send the photo to the server.

[0172] Input: A photo of a meal taken by the user

[0173] Output: Food photos uploaded to the server

[0174] Specific actions: The user takes a photo with the device's camera and presses the "upload" button within the app.

[0175] Step 7:

[0176] The server analyzes the uploaded photos and uses image recognition algorithms to estimate the contents and calories of the meal, and the analysis results are stored in a database.

[0177] Input: User-uploaded food photos

[0178] Output: Estimated meal contents and calorie information

[0179] What it does: The server runs an image recognition algorithm to identify the type of food and its calories.

[0180] Step 8:

[0181] If the user's total calorie intake exceeds their goal, the server will use that information to revise the next day's meal plan, which will then be sent back to the device and displayed to the user.

[0182] Input: Estimated meal contents and calorie information

[0183] Output: Modified meal plan

[0184] Specific operation: The server analyzes the historical data, generates a new meal plan, and sends it to the device.

[0185] Step 9:

[0186] The server analyzes past data and learns patterns of when the user's motivation declines, then generates encouraging and advice messages at the appropriate time and sends them to the device.

[0187] Input: User's historical data

[0188] Output: Advice message

[0189] What happens: The server uses machine learning models to analyze the data, generate advice messages, and send them.

[0190] (Application example 1)

[0191] 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."

[0192] Health management requires not only providing users with personalized diet plans tailored to their individual needs, but also a wide range of support, such as purchasing the ingredients needed to actually follow the plan and maintaining motivation. However, current systems are not adequately able to suggest individual health foods to users, provide support for purchasing in physical stores, or maintain ongoing motivation. Therefore, there is a need to provide comprehensive support to help users achieve their goals.

[0193] 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.

[0194] In this invention, the server includes: an input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; a calculation means for creating a customized diet plan based on the input information using artificial intelligence; a generation means for generating a meal plan, recipes, and a list of necessary ingredients based on the diet plan; a display means for presenting the generated meal plan, recipes, and ingredient list to the user; an upload means for uploading photos of the meals the user has eaten; an analysis means for analyzing the uploaded photos and estimating the meal contents and calories; a correction means for generating a menu that corrects the user's calorie intake if the total calorie intake exceeds the target; a support means for predicting a decrease in motivation and providing timely advice; and a means for suggesting health foods sold in physical stores to the user and providing purchasing support based on the list of necessary ingredients. This allows users to receive comprehensive support for their individual health goals and effectively manage their health.

[0195] "User" refers to an individual who uses the health care system.

[0196] "Height" is the vertical distance from the bottom of the user's feet to the top of their head.

[0197] "Weight" is the total body mass of the user.

[0198] "Body fat percentage" is the percentage of fat in relation to the user's body weight.

[0199] "Goal weight" is the weight that a user wishes to achieve.

[0200] "Target body fat percentage" is the percentage of body fat that the user wishes to achieve.

[0201] "Input means" refers to a device or method that allows a user to input their physical information into the system.

[0202] "Generative AI" is an AI technology that generates customized diet plans based on user information.

[0203] A "computing means" is a device or method for carrying out the process of generating a diet plan based on user input information.

[0204] The "generation means" refers to a device or method for creating a specific meal plan, recipes, and a list of ingredients based on the diet plan generated by the calculation means.

[0205] "Display means" refers to a device or method for presenting the generated menu, recipe, and ingredient list to the user.

[0206] An "uploading means" is a device or method that allows a user to send a photo of the meal they have eaten to the system.

[0207] The "analysis means" refers to a device or method for analyzing uploaded photos and estimating the contents and calories of meals.

[0208] A "modifier" is a device or method for reformulating a diet plan if total calorie intake exceeds the target.

[0209] "Support measures" are devices and methods for predicting a decline in motivation and providing appropriate advice.

[0210] A "brick and mortar store" is a store that sells health foods in a physical location.

[0211] "Health foods" are foods marketed to support the health of users.

[0212] A "purchasing support tool" is a device or method that supports users in efficiently shopping at physical stores based on a list of necessary ingredients.

[0213] The system of the present invention is configured as follows to enhance individual health support for users.

[0214] 1. System Configuration

[0215] The system consists of a terminal for users to input information, a server that performs calculations and analysis, and a terminal that outputs and displays data. The main hardware used is a smartphone or tablet, and the software includes React Native (mobile application development), Python, MySQL, and TensorFlow.

[0216] 2. Input Method

[0217] Users launch the application using a device such as a smartphone or tablet and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server via HTTPS.

[0218] 3. Means of calculation

[0219] The server uses the received information to generate a customized diet plan using a generative AI model, applying algorithms that take into account the user's current health status and goals.

[0220] 4. Generation means

[0221] The server generates specific meal plans, recipes, and ingredient lists based on the generated diet plan, making it easier for the user to prepare and shop for specific ingredients.

[0222] 5. Display means

[0223] The generated menu, recipes, and ingredient list are sent from the server to the terminal and displayed on the user's terminal screen, allowing the user to actually carry out the plan.

[0224] 6. Upload Method

[0225] After eating, users use their device to take a photo of the meal and upload it to the system, which records their calorie intake.

[0226] 7. Analysis method

[0227] The server analyzes the uploaded photos and estimates the contents and calories of the food photographed, which are then compared to the user's calorie goal and stored in a database.

[0228] 8. Remedies

[0229] If the total calorie intake exceeds the target, the server detects this and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[0230] 9. Support methods

[0231] The server analyzes past data and learns patterns of declining motivation. Based on the results of this learning, it sends advice messages to users at appropriate times, thereby maintaining their motivation.

[0232] 10. Healthy food recommendations and purchasing support methods

[0233] In addition, the service suggests health foods sold in physical stores to users and provides purchasing support based on a list of necessary ingredients, allowing users to efficiently purchase the health foods they need in physical stores.

[0234] Specific examples

[0235] For example, if a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and target body fat percentage at 15%, the system operates as follows: When the user enters this information into their device and sends it to the server, the generative AI model creates a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and also generates a list of necessary ingredients. The user uploads photos of the meals they eat each day, and the server analyzes them to estimate calories. If the total calories consumed in a day exceed the target, the server adjusts the menu for the next day to balance calorie intake.

[0236] "Generate a diet plan for a health management system. The user is 170 cm tall, 75 kg, and has a body fat percentage of 25%. Their target weight is 65 kg and their target body fat percentage is 15%."

[0237] In this way, the system provides users with consistent and personalized diet and health management support.

[0238] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0239] Step 1:

[0240] Using a device, a user launches the application and enters basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server via HTTPS. The entered data is related to the user's health status and goals.

[0241] Step 2:

[0242] The server then takes the received user information and generates a customized diet plan using a generative AI model. Specifically, it applies an algorithm that takes into account the user's current health status and goals. It receives the user's health data as input and generates a diet plan that includes calorie restriction and exercise as output.

[0243] Step 3:

[0244] The server generates specific meal plans, recipes, and ingredient lists based on the generated diet plan, including specific menus that are easy for users to follow. It receives the generated diet plan as input and generates specific meal plans and ingredient lists as output.

[0245] Step 4:

[0246] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the user's device screen. The user checks this output and works on the actual meal plan. In this step, data transfer and UI updates are performed to display the data generated by the server on the user's device.

[0247] Step 5:

[0248] After consuming a meal, the user takes a photo of the meal using their device and uploads it to the system. This photo becomes input data for the next analysis step. The user's actions are to take a photo of the meal and upload it.

[0249] Step 6:

[0250] The server analyzes the uploaded photos and estimates the contents and calories of the food in them. Specifically, it uses TensorFlow and SSD models to analyze the images and recognize the food contents. It receives food images as input and generates food contents and calorie value data as output.

[0251] Step 7:

[0252] The calorie intake obtained as a result of the analysis is compared with the user's calorie restriction target and stored in a database. If the total calorie intake exceeds the target, the server detects this situation and regenerates a menu with corrected course. By adjusting the menu for the next day, the user can prevent over-intake. The system receives the analyzed calorie value data as input and generates a revised diet plan as output.

[0253] Step 8:

[0254] The server analyzes past data and learns patterns of declining motivation. Based on this learning, it sends advice messages to users at appropriate times. This helps maintain the user's motivation and support continued health management. It receives the user's behavioral data as input and generates advice messages as output.

[0255] Step 9:

[0256] The system suggests health foods sold in physical stores to users and provides purchasing support based on the required ingredient list. Users can use this information to shop efficiently in physical stores. The server provides optimal store information and product suggestions based on the user's ingredient list. It receives the ingredient list as input and generates purchasing support information for the store as output.

[0257] Through the above processing steps, the present invention becomes a system that provides users with consistently personalized diet and health management support.

[0258] 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.

[0259] The present invention is a health management system that allows a user to input their height, weight, body fat percentage, target weight, and target body fat percentage and provides a customized diet plan based on the input. Furthermore, by combining the present invention with an emotion engine that recognizes the user's emotions, the present invention more effectively supports maintaining motivation. Specific embodiments for implementing the present invention are described below.

[0260] 1. System Configuration

[0261] This system consists of a terminal for users to input information, a server that performs calculations and analysis, an emotion engine, and a terminal that outputs and displays data.

[0262] 2. Input Method

[0263] Users use a device such as a mobile phone or tablet to launch the application and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server.

[0264] 3. Means of calculation

[0265] Based on the information received, the server uses generative AI to generate a customized diet plan, applying algorithms that take into account the user's current health status and goals.

[0266] 4. Generation means

[0267] Based on the diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, allowing users to efficiently prepare and shop for ingredients.

[0268] 5. Display means

[0269] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, where the user can review and work on the actual meal plan.

[0270] 6. Upload Method

[0271] After eating a meal, users can use their device to take a photo of the meal and upload it to the system, allowing the system to record their calorie intake.

[0272] 7. Analysis method

[0273] The server analyzes the uploaded photos and estimates the contents and calories of the food depicted in them, which are then compared to the user's calorie goal and stored in a database.

[0274] 8. Remedies

[0275] The server detects when the user's total calorie intake exceeds their goal and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[0276] 9. Emotion Engine

[0277] The emotion engine has the ability to analyze the user's facial expressions and voice to estimate their emotional state. The emotion engine is installed on the device and collects data from the user's daily actions and speech.

[0278] Examples:

[0279] The user captures their facial expression through the device's camera and inputs voice information into the device.

[0280] The emotion engine analyzes the collected data and estimates the user's emotional state, which is then sent to the server in real time.

[0281] 10. Support methods

[0282] The server receives data from the emotion engine and predicts a decline in motivation based on the user's emotional state. Based on the predicted information, the generative AI generates timely advice messages and sends them to the device.

[0283] Examples:

[0284] The server predicts when a user shows signs of stress or fatigue and sends them messages of encouragement and tips on how to relax.

[0285] For example, a message might be sent saying, "You've worked hard today. Let's take a break and refresh yourself."

[0286] Specific examples

[0287] Let's say a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. When the user enters this information into the device and submits it, the server uses generative AI to create a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and a list of necessary ingredients is also automatically generated.

[0288] Users upload photos of the meals they eat each day, and the server analyzes them to estimate the calories. If the total calories consumed in a day exceeds the target, the server adjusts the menu for the next day to balance the calorie intake. In addition, when the server predicts that the user's motivation will decrease, it sends encouraging messages and specific action plans.

[0289] The emotion engine analyzes the user's facial expressions and voice and transmits their emotional state in real time to the server, which uses this information to provide appropriate advice when the user is feeling stressed or tired.

[0290] As described above, the system of the present invention provides users with consistent and personalized diet and health management support.

[0291] The processing flow will be explained below.

[0292] Step 1:

[0293] A user starts the application using a mobile device or tablet. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage into an input form.

[0294] Step 2:

[0295] The terminal displays the entered information on the screen in real time and temporarily stores it after all the information has been entered.

[0296] Step 3:

[0297] The terminal transmits the temporarily stored user information data to the server.

[0298] Step 4:

[0299] The server stores the received user information data in a database.

[0300] Step 5:

[0301] The server retrieves the stored user information from the database and passes it to the generative AI engine.

[0302] Step 6:

[0303] The generative AI engine creates a customized diet plan based on the user's height, weight, body fat percentage, target weight and target body fat percentage.

[0304] Step 7:

[0305] The server generates a diet plan based on the user's preferences and lifestyle, along with meal plans, recipes, and a list of ingredients needed.

[0306] Step 8:

[0307] The server sends the generated menu, recipes, and ingredient list to the terminal.

[0308] Step 9:

[0309] The device displays meal plans, recipes, and ingredient lists to the user, who can then confirm the information and follow the meal plan.

[0310] Step 10:

[0311] After a user consumes a meal, they use their device to take a photo of the meal and upload it to the system.

[0312] Step 11:

[0313] The device sends photo data of the meal to the server.

[0314] Step 12:

[0315] The server analyzes the received photo data and estimates the contents and calories of the food depicted.

[0316] Step 13:

[0317] The server compares the estimated calories with the user's calorie restriction goal and stores the results in a database.

[0318] Step 14:

[0319] The server monitors the total calorie intake stored in the database, and if the target is exceeded, it instructs the generative AI engine to regenerate a revised menu.

[0320] Step 15:

[0321] The server sends the revised menu to the terminal and notifies the user.

[0322] Step 16:

[0323] The user can check the revised menu on the device and adjust their meal plan for the next day.

[0324] Step 17:

[0325] The emotion engine captures the user's facial expressions and voice through the device, either by speaking into the sensor or by taking a photo.

[0326] Step 18:

[0327] Based on the data captured by the emotion engine, the user's emotional state is analyzed and the emotional information is sent to the server.

[0328] Step 19:

[0329] The server receives the emotion information sent from the emotion engine and predicts a decrease in the user's motivation.

[0330] Step 20:

[0331] Based on the predicted decline in motivation, the server instructs the generative AI engine to create timely advice messages.

[0332] Step 21:

[0333] The server sends the generated advice message to the terminal to notify the user.

[0334] Step 22:

[0335] The user checks the advice message on the device and follows the advice provided.

[0336] These steps help users manage their diet and health in an efficient and personalized way, and by taking into account the user's emotional state, the system can more effectively maintain motivation.

[0337] Example 2

[0338] 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."

[0339] Conventional health management systems have issues in that they do not adequately provide users with personalized diet plans, and do not respond appropriately to users' emotional state or a decline in motivation. Furthermore, they lack a mechanism for accurately analyzing the calories of the food a user consumes and providing appropriate meal plans in real time based on that information. Furthermore, there is a need for a system that can provide appropriate advice when motivation declines, allowing users to continue effective health management over the long term.

[0340] 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.

[0341] In this invention, the server includes: input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; calculation means for creating a customized management plan based on the input information using artificial intelligence; generation means for generating a meal plan, recipes, and ingredient list based on the management plan; display means for presenting the generated meal plan, recipes, and ingredient list to the user; transfer means for uploading images of the meals the user has eaten; analysis means for analyzing the uploaded images and estimating the meal contents and calories; adjustment means for generating a revised meal plan based on the estimated calories if the total calorie intake exceeds the target; and support means for estimating the user's emotional state from facial expressions and voice, predicting a decrease in motivation based on the estimated calories, and providing timely advice. This enables the user to receive consistent, personalized diet and health management support, as well as timely advice based on the user's emotional state, to support long-term health management.

[0342] The "input means" is a means for a user to input data such as height, weight, body fat percentage, target weight, and target body fat percentage through a terminal.

[0343] "Calculation means" refers to a means for creating a customized management plan using generative artificial intelligence based on data input by a user.

[0344] The "generation means" is a means for generating a specific meal menu, recipes, and a list of necessary ingredients based on the management plan created by the calculation means.

[0345] The "display means" is a means for visually presenting to the user the menu, recipe, and ingredient list generated by the generation means.

[0346] The "transfer means" is a means for a user to upload images of the food they have eaten to the system via their terminal.

[0347] The "analysis means" is a means for analyzing the image of the meal uploaded via the transfer means and estimating the contents and calories of the meal.

[0348] The "adjustment means" is a means for generating a revised menu based on the calories estimated by the analysis means when the total calorie intake exceeds the target.

[0349] "Support measures" are means for estimating the user's emotional state from their facial expressions and voice, predicting a decline in motivation based on that information, and providing timely advice.

[0350] The present invention is a health management system that provides a customized diet plan based on the user's height, weight, body fat percentage, target weight, and target body fat percentage. Furthermore, the system has a function to recognize the user's emotions and help maintain motivation.

[0351] This system consists of eight main components: input means, calculation means, generation means, display means, transfer means, analysis means, adjustment means, and support means.

[0352] First, the user launches a dedicated app on their mobile phone or tablet. Then, the user enters information such as their height, weight, body fat percentage, target weight, and target body fat percentage. This information is temporarily stored on the device and then sent to a server via the Internet.

[0353] The server uses the received information to generate a customized diet plan for the user using a generative AI model (e.g., GPT-3). The following prompt is input to the generative AI model:

[0354] "Create a diet plan based on my height of 170 cm, weight of 75 kg, and body fat percentage of 25%, and my target weight of 65 kg and target body fat percentage of 15%."

[0355] The generated diet plan includes a daily or weekly meal plan, recipes, and a list of ingredients. The server sends this data to the device, which displays it to the user. The user can then refer to the displayed menu and recipes to prepare the actual meal.

[0356] After consuming a meal, the user takes a photo of the meal using their device and uploads it to the system. This data is sent to a server, which uses image analysis techniques (e.g., OpenCV or TensorFlow) to estimate the meal contents and calories. The estimated calorie information is compared with the user's calorie restriction goal and stored in a database.

[0357] If the total calorie intake for the day exceeds the target, the server will regenerate a menu with corrected calories and adjust the diet plan for the next day. This process helps users avoid overeating and enables effective health management.

[0358] The system is also equipped with an emotion engine that analyzes the user's facial expressions and voice to estimate their emotional state. The user inputs their facial expressions and voice using the device's camera and microphone, and the data is sent to the server in real time. The server predicts a decline in the user's motivation based on the emotional data, and uses a generative AI model to create an appropriate advice message and send it to the device. For example, a timely message such as, "You seem a little tired today. I recommend taking a short walk as a way to relax" is provided.

[0359] As described above, the system of the present invention can provide users with consistent and personalized diet and health management support, as well as timely advice based on the user's emotional state, to support long-term health management.

[0360] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0361] Step 1:

[0362] The user launches the dedicated app and inputs their height, weight, body fat percentage, target weight, and target body fat percentage. The input information is temporarily stored on the device.

[0363] Input: User's height, weight, body fat percentage, goal weight, and goal body fat percentage

[0364] Output: Temporarily stored user information data

[0365] Step 2:

[0366] The device sends the saved user information to the server using an HTTP request.

[0367] Input: Temporarily stored user information data

[0368] Output: User information data sent to the server

[0369] Step 3:

[0370] The server creates and inputs a prompt sentence into the generative AI model based on the received user information.

[0371] Input: Prompt and user information: "Create a diet plan based on a height of 170 cm, weight of 75 kg, body fat percentage of 25%, target weight of 65 kg, and target body fat percentage of 15%."

[0372] Output: A customized diet plan from the generative AI

[0373] Step 4:

[0374] Based on the generated diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, taking into account the user's calorie restrictions and nutritional balance.

[0375] Input: Generated diet plan

[0376] Output: Specific meal plans, recipes, and ingredient lists

[0377] Step 5:

[0378] The server transmits the generated menu, recipes, and ingredient list to the terminal.

[0379] Input: Specific meal plans, recipes, and ingredient lists

[0380] Output: Data sent to the terminal

[0381] Step 6:

[0382] The device displays the received data on a user interface, allowing the user to confirm the displayed information and follow the actual meal plan.

[0383] Input: Data sent to the terminal

[0384] Output: Meal plan displayed in the user interface

[0385] Step 7:

[0386] After eating a meal, the user takes a photo of the meal using the device's camera and uploads it to the system.

[0387] Input: Food photo

[0388] Output: Food image data sent to the server

[0389] Step 8:

[0390] The server uses image analysis software, such as OpenCV and TensorFlow, to analyze the uploaded food image data and recognize and estimate the food contents and calories.

[0391] Input: Food image data

[0392] Output: Estimated meal contents and calorie information

[0393] Step 9:

[0394] The server compares the estimated calorie information with the user's calorie goal, and if the total calorie intake exceeds the goal, the server regenerates a modified menu.

[0395] Input: Estimated meal content and calorie information, user's calorie restriction goal

[0396] Output: Modified menu

[0397] Step 10:

[0398] Users use the device's camera and microphone to input their facial expressions and voice, and the emotion engine analyzes this data to estimate the user's emotional state.

[0399] Input: User's facial expressions and voice data

[0400] Output: Estimated emotional state

[0401] Step 11:

[0402] The server predicts a decline in the user's motivation based on the emotional data received from the emotion engine, and uses a generative AI model to create an appropriate advice message and send it to the device.

[0403] Input: Estimated emotional state

[0404] Output: Advice message

[0405] Through these steps, the system can provide users with consistent, personalized health management and motivation.

[0406] (Application example 2)

[0407] 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."

[0408] While conventional health management systems can provide customized diet plans tailored to individual users' health goals, they lack the support to appropriately respond to users' emotional changes and maintain their motivation. Furthermore, they have not implemented health management using virtual reality technology, nor have they suggested appropriate health foods and nutritional supplements in virtual stores to improve the user experience. As a result, there has been a lack of support for users to continue managing their health.

[0409] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0410] In this invention, the server includes: an input device for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; a calculation device using artificial intelligence to create a customized diet plan based on the input information; a generation device for generating a meal plan, recipes, and a list of ingredients based on the diet plan; a display device for presenting the generated meal plan, recipes, and ingredient list to the user; an upload device for uploading photos of the meals the user has eaten; an analysis device for analyzing the uploaded photos and estimating the meal contents and calories; a correction device for generating a menu that corrects the user's calorie intake if the total calorie intake exceeds the target; a support device for predicting a decrease in motivation and providing timely advice; a presentation device for suggesting health foods and nutritional supplements within the virtual store; an emotion analysis device for analyzing the user's emotional state using an emotion engine and providing messages to maintain motivation based on the analysis results; and a virtual interface device for supporting the user's activities within the virtual reality environment of the virtual store. This not only provides personalized support that responds to the user's emotional changes, but also enhances the shopping experience within the virtual store and enables continuous health management support.

[0411] The "input means" refers to a means for a user to input his / her height, weight, body fat percentage, target weight, and target body fat percentage through a terminal.

[0412] The "calculation means" is a means for creating a customized diet plan using artificial intelligence based on input information.

[0413] The "generation means" is a means for generating a meal plan, recipes, and a list of ingredients required based on a customized diet plan.

[0414] The "display means" is a means for presenting the generated menu, recipe, and ingredient list to the user.

[0415] The "uploading means" is a means for a user to upload a photo of the meal they have eaten to the system.

[0416] The "analysis means" is a means for analyzing uploaded photos and estimating the contents and calories of the food photographed.

[0417] The "correction means" is a means for generating a menu that corrects the user's calorie intake if the user's total calorie intake exceeds the target.

[0418] "Support measures" are means for predicting declines in motivation and providing timely advice.

[0419] The "presentation means" refers to a means for suggesting health foods and nutritional supplements to users within the virtual store.

[0420] The "emotion analysis means" is a means for analyzing the user's emotional state using an emotion engine and providing a message to maintain motivation based on the analysis results.

[0421] "Virtual interface means" refers to means for supporting user actions within the virtual reality environment of the virtual store.

[0422] This invention aims to develop a system for providing customized diet plans using a virtual store, with the aim of helping users manage their health. The system analyzes the user's health information and emotional state, and provides personalized meal plans, suggested products, and advice to maintain motivation.

[0423] 1. Input Method

[0424] First, the user inputs their height, weight, body fat percentage, target weight, and target body fat percentage using a device such as a smartphone, tablet, or head-mounted display (HMD). This information is temporarily stored on the device.

[0425] 2. Means of calculation

[0426] The information entered by the user is sent to a cloud server, which uses a generative AI model (e.g., OpenAI's GPT-4) to generate a customized diet plan based on the information entered. This AI model is pre-trained on a large health management dataset and has highly accurate algorithms to create the optimal plan for each user.

[0427] 3. Generation means

[0428] Based on the generated diet plan, specific daily meal plans, recipes, and ingredient lists are created, allowing users to efficiently prepare meals and shop daily.

[0429] 4. Display means

[0430] The generated menu, recipes, and ingredient list are sent from the cloud server to the user's device and displayed on the device screen, allowing the user to check and act in accordance with the meal plan.

[0431] 5. Uploading Method

[0432] Users take photos of the food they have eaten using their device camera and upload them to the system, where the data is then sent to a cloud server and stored in a database.

[0433] 6. Analysis method

[0434] The cloud server analyzes the uploaded photos and estimates the contents and calories of the meal. The technology used is an image analysis algorithm (e.g., Amazon Rekognition or Google Cloud Vision). This allows for a highly accurate understanding of the user's calorie intake.

[0435] 7. Remedies

[0436] If the total calorie intake exceeds the target, the cloud server will adjust the menu for the next day to maintain a balanced diet, helping users to comfortably approach their target weight.

[0437] 8. Presentation means

[0438] Health foods and nutritional supplements that are useful for health management are presented in a virtual store. The virtual store is constructed using, for example, a head-mounted display (HMD). Users can select and purchase products in the virtual space.

[0439] 9. Emotion analysis method

[0440] To analyze the user's emotional state, facial expression and voice data are collected using the device's camera and microphone. An emotion engine (e.g., Microsoft Azure Cognitive Services) analyzes this data and estimates the user's emotional state. The analysis results are sent to a cloud server and stored in a database.

[0441] 10. Support methods

[0442] The cloud server uses emotion analysis data to provide motivational advice. For example, if a user feels fatigued or stressed, the AI ​​will generate a message such as "Take a short break to refresh yourself" and display it on the device.

[0443] Specific examples

[0444] Specific user scenarios:

[0445] The user inputs information such as height 170 cm, weight 75 kg, body fat percentage 25%, target weight 65 kg, and target body fat percentage 15%. Based on this information, the generative AI model generates a diet plan including appropriate calorie restrictions and exercise plans. Specific menus and recipes for breakfast, lunch, and dinner are presented, and a list of ingredients is automatically generated.

[0446] Example prompt sentence:

[0447] After entering basic user information, the generative AI model receives prompts like this:

[0448] User Basic Information:

[0449] Height: 170 cm

[0450] Weight: 75 kg

[0451] Body fat percentage: 25%

[0452] Target weight: 65 kg

[0453] Target body fat percentage: 15%

[0454] Based on this data, your application should generate a customized diet plan for the user, recommend suitable health foods and supplements in a virtual store, and analyze the user's facial expressions and voice to estimate their emotional state and display advice messages to keep them motivated as needed.

[0455] This example demonstrates how the invention can be put into practice, allowing users to receive consistent, personalized diet and health management support.

[0456] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0457] Step 1:

[0458] The user uses the device to input their height, weight, body fat percentage, target weight, and target body fat percentage. This input information is temporarily stored on the device and later sent to the cloud server. The server receives this information and stores it as data for the next process.

[0459] input:

[0460] User's health information (height, weight, body fat percentage, target weight, target body fat percentage)

[0461] output:

[0462] User health information data stored on a cloud server

[0463] Specific behavior:

[0464] The user enters the necessary information using a smartphone, tablet, or HMD and presses the send button.

[0465] Step 2:

[0466] The cloud server generates a customized diet plan using a generative AI model based on the input health information. The server inputs prompts into the generative AI model to generate the diet plan.

[0467] input:

[0468] Stored user health information data

[0469] output:

[0470] Customized diet plans

[0471] Specific behavior:

[0472] The cloud server inputs data into a generative AI model (e.g., GPT-4) to generate a diet plan, which is then stored on the server.

[0473] Step 3:

[0474] Based on the generated diet plan, the cloud server creates a specific meal plan, recipes, and a list of ingredients, which are then sent to the user's device.

[0475] input:

[0476] Customized diet plans

[0477] output:

[0478] Meal plans, recipes, and ingredient lists

[0479] Specific behavior:

[0480] The cloud server references the recipe database and ingredient database to generate a specific meal plan, which is then sent to the device and displayed for the user to review.

[0481] Step 4:

[0482] After consuming a meal, users use their device to take a photo of the meal and upload it to the system, which then sends the photo to a cloud server for further processing.

[0483] input:

[0484] Food photos taken by users

[0485] output:

[0486] Meal photos uploaded to a cloud server

[0487] Specific behavior:

[0488] Users take a photo of their meal with their device's camera and press the upload button to send it to the server, where it is stored.

[0489] Step 5:

[0490] The cloud server analyzes the uploaded photos and estimates the contents and calories of the meal using image analysis algorithms.

[0491] input:

[0492] Uploaded food photos

[0493] output:

[0494] Analysis data of meal contents and estimated calories

[0495] Specific behavior:

[0496] The cloud server runs an image analysis algorithm (e.g., Amazon Rekognition) to analyze the type and portion size of food from the photo and calculate calories.

[0497] Step 6:

[0498] The server records the analyzed calorie data as the total calories. If the total calorie intake exceeds the target, the menu for the next day is revised. This revised data is then sent back to the user's device.

[0499] input:

[0500] Analyzed food content data and estimated calories

[0501] output:

[0502] Revised meal plans, recipes, and ingredient lists

[0503] Specific behavior:

[0504] The cloud server compares the user's calorie intake history with their goals and adjusts the meal plan for the next day if necessary, then sends the revised plan to the user's device and displays it again on the device screen.

[0505] Step 7:

[0506] Health foods and nutritional supplements are suggested to users in the virtual store, and they can purchase these products using their devices.

[0507] input:

[0508] Customized diet plans and virtual store data

[0509] output:

[0510] Suggested health foods and nutritional supplements

[0511] Specific behavior:

[0512] Users wear a head-mounted display and access a virtual store, select food or supplements using the virtual interface, and press the purchase button.

[0513] Step 8:

[0514] The user's facial expressions and voice are collected through the device's camera and microphone and sent to a cloud server, where an emotion engine analyzes them to estimate the user's emotional state.

[0515] input:

[0516] Your facial and voice data

[0517] output:

[0518] Inferred emotional state

[0519] Specific behavior:

[0520] The device collects data using a camera and microphone, and the cloud server analyzes the data using an emotion engine (e.g., Microsoft Azure Cognitive Services) to estimate emotions.

[0521] Step 9:

[0522] The cloud server uses a generative AI model to generate advice messages to maintain motivation based on the user's emotional state and sends them to the user's device.

[0523] input:

[0524] Inferred emotional state

[0525] output:

[0526] Advice Message

[0527] Specific behavior:

[0528] The cloud server uses a generative AI model to create a message based on the emotion analysis data, and the generated message is sent to the user's device and displayed on the screen.

[0529] Above is a detailed flow of each processing step, which allows users to consistently receive personalized diet plans, healthy food recommendations, and advice based on their emotional state.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] [Second embodiment]

[0534] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0535] 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.

[0536] 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).

[0537] 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.

[0538] 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.

[0539] 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).

[0540] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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.

[0545] 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."

[0546] The present invention is a health management system that inputs a user's height, weight, body fat percentage, target weight, and target body fat percentage and provides a customized diet plan based on the input. Specific embodiments for carrying out the present invention will be described below.

[0547] 1. System Configuration

[0548] This system consists of a terminal for users to input information, a server that performs calculations and analysis, and a terminal that outputs and displays data.

[0549] 2. Input Method

[0550] Users use a device such as a mobile phone or tablet to launch the application and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server.

[0551] 3. Means of calculation

[0552] Based on the information received, the server uses generative AI to generate a customized diet plan, applying algorithms that take into account the user's current health status and goals.

[0553] 4. Generation means

[0554] Based on the diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, allowing users to efficiently prepare and shop for ingredients.

[0555] 5. Display means

[0556] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, where the user can review and work on the actual meal plan.

[0557] 6. Upload Method

[0558] After eating a meal, users can use their device to take a photo of the meal and upload it to the system, allowing the system to record their calorie intake.

[0559] 7. Analysis method

[0560] The server analyzes the uploaded photos and estimates the contents and calories of the food depicted in them, which are then compared to the user's calorie goal and stored in a database.

[0561] 8. Remedies

[0562] The server detects when the user's total calorie intake exceeds their goal and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[0563] 9. Support methods

[0564] To prevent a decline in motivation, the server analyzes past data to learn patterns of declining motivation and then sends advice messages to users at appropriate times based on the results of that learning.

[0565] Specific examples

[0566] For example, suppose a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. When the user enters this information into the device and submits it, the server uses generative AI to create a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and a list of necessary ingredients is also automatically generated.

[0567] Users upload photos of the meals they eat each day, and the server analyzes them to estimate the calories. If the total calories consumed in a day exceeds the target, the server adjusts the menu for the next day to balance the calorie intake. In addition, when the server predicts that the user's motivation will decrease, it sends encouraging messages and specific action plans.

[0568] As described above, the system of the present invention provides users with consistent and personalized diet and health management support.

[0569] The processing flow will be explained below.

[0570] Step 1:

[0571] A user starts the application using a mobile device or tablet. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage into an input form.

[0572] Step 2:

[0573] The terminal displays the entered information on the screen in real time and temporarily stores it after all the information has been entered.

[0574] Step 3:

[0575] The terminal transmits the temporarily stored user information data to the server.

[0576] Step 4:

[0577] The server stores the received user information data in a database.

[0578] Step 5:

[0579] The server retrieves the stored user information from the database and passes it to the generative AI engine.

[0580] Step 6:

[0581] The generative AI engine creates a customized diet plan based on the user's height, weight, body fat percentage, target weight and target body fat percentage.

[0582] Step 7:

[0583] The server generates a diet plan based on the user's preferences and lifestyle, along with meal plans, recipes, and a list of ingredients needed.

[0584] Step 8:

[0585] The server sends the generated menu, recipes, and ingredient list to the terminal.

[0586] Step 9:

[0587] The device displays meal plans, recipes, and ingredient lists to the user, who can then confirm the information and follow the meal plan.

[0588] Step 10:

[0589] After a user consumes a meal, they use their device to take a photo of the meal and upload it to the system.

[0590] Step 11:

[0591] The device sends photo data of the meal to the server.

[0592] Step 12:

[0593] The server analyzes the received photo data and estimates the contents and calories of the food depicted.

[0594] Step 13:

[0595] The server compares the estimated calories with the user's calorie restriction goal and stores the results in a database.

[0596] Step 14:

[0597] The server monitors the total calorie intake stored in the database, and if the target is exceeded, it instructs the generative AI engine to regenerate a revised menu.

[0598] Step 15:

[0599] The server sends the revised menu to the terminal and notifies the user.

[0600] Step 16:

[0601] The user can check the revised menu on the device and adjust their meal plan for the next day.

[0602] Step 17:

[0603] The server analyzes the user's past data and learns patterns of declining motivation.

[0604] Step 18:

[0605] The server predicts when motivation is likely to wane and generates encouraging messages and specific advice.

[0606] Step 19:

[0607] The server sends the generated encouraging message or advice to the terminal and notifies the user.

[0608] Step 20:

[0609] The user checks the message on their device and follows the advice provided.

[0610] The above steps will efficiently support the user's diet and health management.

[0611] Example 1

[0612] 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."

[0613] Conventional diet support systems have been unable to adequately support individual users' dietary management and calorie restriction, and have not provided specific methods for effectively achieving target weight or body fat percentage. Furthermore, they lacked the ability to predict and appropriately respond to users' declining motivation, making continuous health management difficult.

[0614] 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.

[0615] In this invention, the server includes input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage, transmission means for transmitting data to the server, calculation means for creating a customized diet plan based on the input information using artificial intelligence, generation means for generating a meal plan, recipes, and a list of necessary ingredients based on the diet plan, display means for presenting the generated meal plan, recipes, and ingredient list to the user, upload means for uploading photos of the meals the user has eaten, analysis means for analyzing the uploaded photos to estimate the meal contents and calories, correction means for generating a menu that corrects the user's calorie intake if the user's total calorie intake exceeds the target, and support means for predicting a decline in the user's motivation based on past data and providing advice. This allows the user to consistently follow the individually customized diet plan and effectively manage their health.

[0616] "Input means" refers to a device or function that allows a user to input information such as height, weight, body fat percentage, target weight, and target body fat percentage into the system.

[0617] "Transmission means" refers to a device or function that allows the terminal to transmit information entered by the user to the server.

[0618] "Generative artificial intelligence" refers to computational algorithms or models that create customized diet plans based on input information.

[0619] "Computational means" refers to the process or device that analyzes the received user information and generates a diet plan.

[0620] The "creation means" is a device or function for creating a meal plan, recipes, and a list of necessary ingredients based on the diet plan.

[0621] "Display means" refers to a device or function for visually presenting the generated menu, recipe, and ingredient list to the user.

[0622] "Uploading means" refers to a device or function that allows a user to send photos of the food they have eaten to the system.

[0623] "Analysis means" refers to a device or function that analyzes uploaded photos and estimates the contents and calories of meals.

[0624] The "correction means" is a device or function for generating a menu that corrects the course when the total calorie intake exceeds the target.

[0625] "Support tools" are devices or functions that predict a decline in a user's motivation based on past data and provide advice at the appropriate time.

[0626] The present invention is a system developed to support users' health management, which generates a customized diet plan based on information input by the user and supports continuous health management. The following describes in detail the embodiments of the present invention.

[0627] This system consists of a terminal for users to input information, a server that receives and analyzes the data, and a terminal for displaying the generated information.

[0628] Input Method

[0629] The user launches a dedicated application using a device such as a mobile phone or tablet. In the application, basic information such as height, weight, body fat percentage, target weight and target body fat percentage is entered, and this information is temporarily saved on the device. After entering the information, the user presses the "Send" button, and the entered information is sent to the server.

[0630] Transmission method

[0631] The device sends the stored user basic information to the server. This data is sent using a secure communication protocol (e.g. HTTPS).

[0632] means of calculation

[0633] The server then uses the received data to create a customized diet plan using a generative AI model that has previously learned various health data to generate the optimal plan based on the user's current health condition and goals.

[0634] generation means

[0635] The server generates daily or weekly meal plans, recipes, and ingredient lists based on the customized diet plan, a process that includes retrieving recipe and ingredient information from a database.

[0636] Display means

[0637] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, allowing the user to follow the actual meal plan.

[0638] Upload method

[0639] After consuming a meal, the user uses the device to take a photo of the meal and upload it to the system. The uploading operation is performed within the application, and the photo data is sent to the server.

[0640] Analysis means

[0641] The server analyzes the uploaded photos and estimates the food contents and calories of the food depicted in the photos using image recognition algorithms, and the results of the analysis are stored in a database.

[0642] Correction means

[0643] If the user's total calorie intake exceeds their goal, the server will use that information to regenerate a revised menu, which will then be sent back to the device and displayed.

[0644] Support means

[0645] The server analyzes past data to learn patterns of when users lose motivation, and then uses an AI model to generate advice messages to help users maintain their motivation at the right time and send them to their devices.

[0646] Specific examples

[0647] For example, consider a user who is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. The user enters this information into the device and presses the "Send" button. Based on the received information, the server uses a generative AI model to create a diet plan that includes calorie restriction and appropriate exercise. The plan includes specific menus and recipes for breakfast, lunch, and dinner, and an automatically generated list of ingredients is also included. The user uploads photos of their daily meals, and the server analyzes the photos to estimate calories. If the total daily calorie intake exceeds the target, the server adjusts the menu for the next day. The server also sends encouraging messages and specific action plans at times when the user's motivation is predicted to wane.

[0648] Example prompts to input to the generative AI model

[0649] User's current height: 170 cm

[0650] User's current weight: 75 kg

[0651] User's current body fat percentage: 25%

[0652] User target weight: 65 kg

[0653] User's target body fat percentage: 15%

[0654] Use this information to generate a customized diet plan that includes daily and weekly meal plans, recipes, and ingredient lists.

[0655] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0656] Step 1:

[0657] The user starts up the device and accesses the application. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage. The entered information is temporarily stored in the device's memory.

[0658] Input: User's height, weight, body fat percentage, target weight, target body fat percentage

[0659] Output: User information temporarily stored on the device

[0660] Specific behavior: The user enters the required information into the input form and presses the "Submit" button.

[0661] Step 2:

[0662] The terminal uses a secure protocol (e.g., HTTPS) to send the input data to the server. The terminal generates JSON-formatted data as a transmission method and sends it to the server using an HTTP POST request.

[0663] Input: User information temporarily stored on the device

[0664] Output: User information sent to the server

[0665] Specific operation: When the "Send" button is pressed, the device generates data in JSON format, issues an HTTP request, and sends it to the server.

[0666] Step 3:

[0667] The server stores the received user information in a database and uses a generative AI model to create a customized diet plan. Based on the received information, the server inputs prompts into the AI ​​model to generate the diet plan.

[0668] Input: User information received by the server (JSON format)

[0669] Output: A customized diet plan

[0670] What it does: The server stores the data in a database and provides prompts to the AI ​​model to generate a diet plan.

[0671] Step 4:

[0672] The server analyzes the generated diet plan and generates daily and weekly meal plans, recipes, and ingredient lists, which are then encoded in JSON format and sent to the device.

[0673] Enter: your customized diet plan.

[0674] Output: JSON format meal plans, recipes, and ingredient lists

[0675] Specific operation: The server generates the necessary information based on the diet plan and encodes it for transmission to the device.

[0676] Step 5:

[0677] The device analyzes the received data and displays it in a visually easy-to-understand format for the user, allowing them to check the generated menu, recipes, and ingredient lists.

[0678] Input: JSON format data sent from the server

[0679] Output: Menus, recipes, and ingredient lists displayed on the device screen

[0680] Specific behavior: The device parses the JSON formatted data and displays the information based on the user interface.

[0681] Step 6:

[0682] After consuming a meal, the user uses the device to take a photo of the meal and upload it to the system. The user then presses the "upload" button in the application to send the photo to the server.

[0683] Input: A photo of a meal taken by the user

[0684] Output: Food photos uploaded to the server

[0685] Specific actions: The user takes a photo with the device's camera and presses the "upload" button within the app.

[0686] Step 7:

[0687] The server analyzes the uploaded photos and uses image recognition algorithms to estimate the contents and calories of the meal, and the analysis results are stored in a database.

[0688] Input: User-uploaded food photos

[0689] Output: Estimated meal contents and calorie information

[0690] What it does: The server runs an image recognition algorithm to identify the type of food and its calories.

[0691] Step 8:

[0692] If the user's total calorie intake exceeds their goal, the server will use that information to revise the next day's meal plan, which will then be sent back to the device and displayed to the user.

[0693] Input: Estimated meal contents and calorie information

[0694] Output: Modified meal plan

[0695] Specific operation: The server analyzes the historical data, generates a new meal plan, and sends it to the device.

[0696] Step 9:

[0697] The server analyzes past data and learns patterns of when the user's motivation declines, then generates encouraging and advice messages at the appropriate time and sends them to the device.

[0698] Input: User's historical data

[0699] Output: Advice message

[0700] What happens: The server uses machine learning models to analyze the data, generate advice messages, and send them.

[0701] (Application example 1)

[0702] 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."

[0703] Health management requires not only providing users with personalized diet plans tailored to their individual needs, but also a wide range of support, such as purchasing the ingredients needed to actually follow the plan and maintaining motivation. However, current systems are not adequately able to suggest individual health foods to users, provide support for purchasing in physical stores, or maintain ongoing motivation. Therefore, there is a need to provide comprehensive support to help users achieve their goals.

[0704] 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.

[0705] In this invention, the server includes: an input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; a calculation means for creating a customized diet plan based on the input information using artificial intelligence; a generation means for generating a meal plan, recipes, and a list of necessary ingredients based on the diet plan; a display means for presenting the generated meal plan, recipes, and ingredient list to the user; an upload means for uploading photos of the meals the user has eaten; an analysis means for analyzing the uploaded photos and estimating the meal contents and calories; a correction means for generating a menu that corrects the user's calorie intake if the total calorie intake exceeds the target; a support means for predicting a decrease in motivation and providing timely advice; and a means for suggesting health foods sold in physical stores to the user and providing purchasing support based on the list of necessary ingredients. This allows users to receive comprehensive support for their individual health goals and effectively manage their health.

[0706] "User" refers to an individual who uses the health care system.

[0707] "Height" is the vertical distance from the bottom of the user's feet to the top of their head.

[0708] "Weight" is the total body mass of the user.

[0709] "Body fat percentage" is the percentage of fat in relation to the user's body weight.

[0710] "Goal weight" is the weight that a user wishes to achieve.

[0711] "Target body fat percentage" is the percentage of body fat that the user wishes to achieve.

[0712] "Input means" refers to a device or method that allows a user to input their physical information into the system.

[0713] "Generative AI" is an AI technology that generates customized diet plans based on user information.

[0714] A "computing means" is a device or method for carrying out the process of generating a diet plan based on user input information.

[0715] The "generation means" refers to a device or method for creating a specific meal plan, recipes, and a list of ingredients based on the diet plan generated by the calculation means.

[0716] "Display means" refers to a device or method for presenting the generated menu, recipe, and ingredient list to the user.

[0717] An "uploading means" is a device or method that allows a user to send a photo of the meal they have eaten to the system.

[0718] The "analysis means" refers to a device or method for analyzing uploaded photos and estimating the contents and calories of meals.

[0719] A "modifier" is a device or method for reformulating a diet plan if total calorie intake exceeds the target.

[0720] "Support measures" are devices and methods for predicting a decline in motivation and providing appropriate advice.

[0721] A "brick and mortar store" is a store that sells health foods in a physical location.

[0722] "Health foods" are foods marketed to support the health of users.

[0723] A "purchasing support tool" is a device or method that supports users in efficiently shopping at physical stores based on a list of necessary ingredients.

[0724] The system of the present invention is configured as follows to enhance individual health support for users.

[0725] 1. System Configuration

[0726] The system consists of a terminal for users to input information, a server that performs calculations and analysis, and a terminal that outputs and displays data. The main hardware used is a smartphone or tablet, and the software includes React Native (mobile application development), Python, MySQL, and TensorFlow.

[0727] 2. Input Method

[0728] Users launch the application using a device such as a smartphone or tablet and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server via HTTPS.

[0729] 3. Means of calculation

[0730] The server uses the received information to generate a customized diet plan using a generative AI model, applying algorithms that take into account the user's current health status and goals.

[0731] 4. Generation means

[0732] The server generates specific meal plans, recipes, and ingredient lists based on the generated diet plan, making it easier for the user to prepare and shop for specific ingredients.

[0733] 5. Display means

[0734] The generated menu, recipes, and ingredient list are sent from the server to the terminal and displayed on the user's terminal screen, allowing the user to actually carry out the plan.

[0735] 6. Upload Method

[0736] After eating, users use their device to take a photo of the meal and upload it to the system, which records their calorie intake.

[0737] 7. Analysis method

[0738] The server analyzes the uploaded photos and estimates the contents and calories of the food photographed, which are then compared to the user's calorie goal and stored in a database.

[0739] 8. Remedies

[0740] If the total calorie intake exceeds the target, the server detects this and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[0741] 9. Support methods

[0742] The server analyzes past data and learns patterns of declining motivation. Based on the results of this learning, it sends advice messages to users at appropriate times, thereby maintaining their motivation.

[0743] 10. Healthy food recommendations and purchasing support methods

[0744] In addition, the service suggests health foods sold in physical stores to users and provides purchasing support based on a list of necessary ingredients, allowing users to efficiently purchase the health foods they need in physical stores.

[0745] Specific examples

[0746] For example, if a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and target body fat percentage at 15%, the system operates as follows: When the user enters this information into their device and sends it to the server, the generative AI model creates a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and also generates a list of necessary ingredients. The user uploads photos of the meals they eat each day, and the server analyzes them to estimate calories. If the total calories consumed in a day exceed the target, the server adjusts the menu for the next day to balance calorie intake.

[0747] "Generate a diet plan for a health management system. The user is 170 cm tall, 75 kg, and has a body fat percentage of 25%. Their target weight is 65 kg and their target body fat percentage is 15%."

[0748] In this way, the system provides users with consistent and personalized diet and health management support.

[0749] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0750] Step 1:

[0751] Using a device, a user launches the application and enters basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server via HTTPS. The entered data is related to the user's health status and goals.

[0752] Step 2:

[0753] The server then takes the received user information and generates a customized diet plan using a generative AI model. Specifically, it applies an algorithm that takes into account the user's current health status and goals. It receives the user's health data as input and generates a diet plan that includes calorie restriction and exercise as output.

[0754] Step 3:

[0755] The server generates specific meal plans, recipes, and ingredient lists based on the generated diet plan, including specific menus that are easy for users to follow. It receives the generated diet plan as input and generates specific meal plans and ingredient lists as output.

[0756] Step 4:

[0757] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the user's device screen. The user checks this output and works on the actual meal plan. In this step, data transfer and UI updates are performed to display the data generated by the server on the user's device.

[0758] Step 5:

[0759] After consuming a meal, the user takes a photo of the meal using their device and uploads it to the system. This photo becomes input data for the next analysis step. The user's actions are to take a photo of the meal and upload it.

[0760] Step 6:

[0761] The server analyzes the uploaded photos and estimates the contents and calories of the food in them. Specifically, it uses TensorFlow and SSD models to analyze the images and recognize the food contents. It receives food images as input and generates food contents and calorie value data as output.

[0762] Step 7:

[0763] The calorie intake obtained as a result of the analysis is compared with the user's calorie restriction target and stored in a database. If the total calorie intake exceeds the target, the server detects this situation and regenerates a menu with corrected course. By adjusting the menu for the next day, the user can prevent over-intake. The system receives the analyzed calorie value data as input and generates a revised diet plan as output.

[0764] Step 8:

[0765] The server analyzes past data and learns patterns of declining motivation. Based on this learning, it sends advice messages to users at appropriate times. This helps maintain the user's motivation and support continued health management. It receives the user's behavioral data as input and generates advice messages as output.

[0766] Step 9:

[0767] The system suggests health foods sold in physical stores to users and provides purchasing support based on the required ingredient list. Users can use this information to shop efficiently in physical stores. The server provides optimal store information and product suggestions based on the user's ingredient list. It receives the ingredient list as input and generates purchasing support information for the store as output.

[0768] Through the above processing steps, the present invention becomes a system that provides users with consistently personalized diet and health management support.

[0769] 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.

[0770] The present invention is a health management system that allows a user to input their height, weight, body fat percentage, target weight, and target body fat percentage and provides a customized diet plan based on the input. Furthermore, by combining the present invention with an emotion engine that recognizes the user's emotions, the present invention more effectively supports maintaining motivation. Specific embodiments for implementing the present invention are described below.

[0771] 1. System Configuration

[0772] This system consists of a terminal for users to input information, a server that performs calculations and analysis, an emotion engine, and a terminal that outputs and displays data.

[0773] 2. Input Method

[0774] Users use a device such as a mobile phone or tablet to launch the application and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server.

[0775] 3. Means of calculation

[0776] Based on the information received, the server uses generative AI to generate a customized diet plan, applying algorithms that take into account the user's current health status and goals.

[0777] 4. Generation means

[0778] Based on the diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, allowing users to efficiently prepare and shop for ingredients.

[0779] 5. Display means

[0780] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, where the user can review and work on the actual meal plan.

[0781] 6. Upload Method

[0782] After eating a meal, users can use their device to take a photo of the meal and upload it to the system, allowing the system to record their calorie intake.

[0783] 7. Analysis method

[0784] The server analyzes the uploaded photos and estimates the contents and calories of the food depicted in them, which are then compared to the user's calorie goal and stored in a database.

[0785] 8. Remedies

[0786] The server detects when the user's total calorie intake exceeds their goal and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[0787] 9. Emotion Engine

[0788] The emotion engine has the ability to analyze the user's facial expressions and voice to estimate their emotional state. The emotion engine is installed on the device and collects data from the user's daily actions and speech.

[0789] Examples:

[0790] The user captures their facial expression through the device's camera and inputs voice information into the device.

[0791] The emotion engine analyzes the collected data and estimates the user's emotional state, which is then sent to the server in real time.

[0792] 10. Support methods

[0793] The server receives data from the emotion engine and predicts a decline in motivation based on the user's emotional state. Based on the predicted information, the generative AI generates timely advice messages and sends them to the device.

[0794] Examples:

[0795] The server predicts when a user shows signs of stress or fatigue and sends them messages of encouragement and tips on how to relax.

[0796] For example, a message might be sent saying, "You've worked hard today. Let's take a break and refresh yourself."

[0797] Specific examples

[0798] Let's say a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. When the user enters this information into the device and submits it, the server uses generative AI to create a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and a list of necessary ingredients is also automatically generated.

[0799] Users upload photos of the meals they eat each day, and the server analyzes them to estimate the calories. If the total calories consumed in a day exceeds the target, the server adjusts the menu for the next day to balance the calorie intake. In addition, when the server predicts that the user's motivation will decrease, it sends encouraging messages and specific action plans.

[0800] The emotion engine analyzes the user's facial expressions and voice and transmits their emotional state in real time to the server, which uses this information to provide appropriate advice when the user is feeling stressed or tired.

[0801] As described above, the system of the present invention provides users with consistent and personalized diet and health management support.

[0802] The processing flow will be explained below.

[0803] Step 1:

[0804] A user starts the application using a mobile device or tablet. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage into an input form.

[0805] Step 2:

[0806] The terminal displays the entered information on the screen in real time and temporarily stores it after all the information has been entered.

[0807] Step 3:

[0808] The terminal transmits the temporarily stored user information data to the server.

[0809] Step 4:

[0810] The server stores the received user information data in a database.

[0811] Step 5:

[0812] The server retrieves the stored user information from the database and passes it to the generative AI engine.

[0813] Step 6:

[0814] The generative AI engine creates a customized diet plan based on the user's height, weight, body fat percentage, target weight and target body fat percentage.

[0815] Step 7:

[0816] The server generates a diet plan based on the user's preferences and lifestyle, along with meal plans, recipes, and a list of ingredients needed.

[0817] Step 8:

[0818] The server sends the generated menu, recipes, and ingredient list to the terminal.

[0819] Step 9:

[0820] The device displays meal plans, recipes, and ingredient lists to the user, who can then confirm the information and follow the meal plan.

[0821] Step 10:

[0822] After a user consumes a meal, they use their device to take a photo of the meal and upload it to the system.

[0823] Step 11:

[0824] The device sends photo data of the meal to the server.

[0825] Step 12:

[0826] The server analyzes the received photo data and estimates the contents and calories of the food depicted.

[0827] Step 13:

[0828] The server compares the estimated calories with the user's calorie restriction goal and stores the results in a database.

[0829] Step 14:

[0830] The server monitors the total calorie intake stored in the database, and if the target is exceeded, it instructs the generative AI engine to regenerate a revised menu.

[0831] Step 15:

[0832] The server sends the revised menu to the terminal and notifies the user.

[0833] Step 16:

[0834] The user can check the revised menu on the device and adjust their meal plan for the next day.

[0835] Step 17:

[0836] The emotion engine captures the user's facial expressions and voice through the device, either by speaking into the sensor or by taking a photo.

[0837] Step 18:

[0838] Based on the data captured by the emotion engine, the user's emotional state is analyzed and the emotional information is sent to the server.

[0839] Step 19:

[0840] The server receives the emotion information sent from the emotion engine and predicts a decrease in the user's motivation.

[0841] Step 20:

[0842] Based on the predicted decline in motivation, the server instructs the generative AI engine to create timely advice messages.

[0843] Step 21:

[0844] The server sends the generated advice message to the terminal to notify the user.

[0845] Step 22:

[0846] The user checks the advice message on the device and follows the advice provided.

[0847] These steps help users manage their diet and health in an efficient and personalized way, and by taking into account the user's emotional state, the system can more effectively maintain motivation.

[0848] Example 2

[0849] 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."

[0850] Conventional health management systems have issues in that they do not adequately provide users with personalized diet plans, and do not respond appropriately to users' emotional state or a decline in motivation. Furthermore, they lack a mechanism for accurately analyzing the calories of the food a user consumes and providing appropriate meal plans in real time based on that information. Furthermore, there is a need for a system that can provide appropriate advice when motivation declines, allowing users to continue effective health management over the long term.

[0851] 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.

[0852] In this invention, the server includes: input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; calculation means for creating a customized management plan based on the input information using artificial intelligence; generation means for generating a meal plan, recipes, and ingredient list based on the management plan; display means for presenting the generated meal plan, recipes, and ingredient list to the user; transfer means for uploading images of the meals the user has eaten; analysis means for analyzing the uploaded images and estimating the meal contents and calories; adjustment means for generating a revised meal plan based on the estimated calories if the total calorie intake exceeds the target; and support means for estimating the user's emotional state from facial expressions and voice, predicting a decrease in motivation based on the estimated calories, and providing timely advice. This enables the user to receive consistent, personalized diet and health management support, as well as timely advice based on the user's emotional state, to support long-term health management.

[0853] The "input means" is a means for a user to input data such as height, weight, body fat percentage, target weight, and target body fat percentage through a terminal.

[0854] "Calculation means" refers to a means for creating a customized management plan using generative artificial intelligence based on data input by a user.

[0855] The "generation means" is a means for generating a specific meal menu, recipes, and a list of necessary ingredients based on the management plan created by the calculation means.

[0856] The "display means" is a means for visually presenting to the user the menu, recipe, and ingredient list generated by the generation means.

[0857] The "transfer means" is a means for a user to upload images of the food they have eaten to the system via their terminal.

[0858] The "analysis means" is a means for analyzing the image of the meal uploaded via the transfer means and estimating the contents and calories of the meal.

[0859] The "adjustment means" is a means for generating a revised menu based on the calories estimated by the analysis means when the total calorie intake exceeds the target.

[0860] "Support measures" are means for estimating the user's emotional state from their facial expressions and voice, predicting a decline in motivation based on that information, and providing timely advice.

[0861] The present invention is a health management system that provides a customized diet plan based on the user's height, weight, body fat percentage, target weight, and target body fat percentage. Furthermore, the system has a function to recognize the user's emotions and help maintain motivation.

[0862] This system consists of eight main components: input means, calculation means, generation means, display means, transfer means, analysis means, adjustment means, and support means.

[0863] First, the user launches a dedicated app on their mobile phone or tablet. Then, the user enters information such as their height, weight, body fat percentage, target weight, and target body fat percentage. This information is temporarily stored on the device and then sent to a server via the Internet.

[0864] The server uses the received information to generate a customized diet plan for the user using a generative AI model (e.g., GPT-3). The following prompt is input to the generative AI model:

[0865] "Create a diet plan based on my height of 170 cm, weight of 75 kg, and body fat percentage of 25%, and my target weight of 65 kg and target body fat percentage of 15%."

[0866] The generated diet plan includes a daily or weekly meal plan, recipes, and a list of ingredients. The server sends this data to the device, which displays it to the user. The user can then refer to the displayed menu and recipes to prepare the actual meal.

[0867] After consuming a meal, the user takes a photo of the meal using their device and uploads it to the system. This data is sent to a server, which uses image analysis techniques (e.g., OpenCV or TensorFlow) to estimate the meal contents and calories. The estimated calorie information is compared with the user's calorie restriction goal and stored in a database.

[0868] If the total calorie intake for the day exceeds the target, the server will regenerate a menu with corrected calories and adjust the diet plan for the next day. This process helps users avoid overeating and enables effective health management.

[0869] The system is also equipped with an emotion engine that analyzes the user's facial expressions and voice to estimate their emotional state. The user inputs their facial expressions and voice using the device's camera and microphone, and the data is sent to the server in real time. The server predicts a decline in the user's motivation based on the emotional data, and uses a generative AI model to create an appropriate advice message and send it to the device. For example, a timely message such as, "You seem a little tired today. I recommend taking a short walk as a way to relax" is provided.

[0870] As described above, the system of the present invention can provide users with consistent and personalized diet and health management support, as well as timely advice based on the user's emotional state, to support long-term health management.

[0871] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0872] Step 1:

[0873] The user launches the dedicated app and inputs their height, weight, body fat percentage, target weight, and target body fat percentage. The input information is temporarily stored on the device.

[0874] Input: User's height, weight, body fat percentage, goal weight, and goal body fat percentage

[0875] Output: Temporarily stored user information data

[0876] Step 2:

[0877] The device sends the saved user information to the server using an HTTP request.

[0878] Input: Temporarily stored user information data

[0879] Output: User information data sent to the server

[0880] Step 3:

[0881] The server creates and inputs a prompt sentence into the generative AI model based on the received user information.

[0882] Input: Prompt and user information: "Create a diet plan based on a height of 170 cm, weight of 75 kg, body fat percentage of 25%, target weight of 65 kg, and target body fat percentage of 15%."

[0883] Output: A customized diet plan from the generative AI

[0884] Step 4:

[0885] Based on the generated diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, taking into account the user's calorie restrictions and nutritional balance.

[0886] Input: Generated diet plan

[0887] Output: Specific meal plans, recipes, and ingredient lists

[0888] Step 5:

[0889] The server transmits the generated menu, recipes, and ingredient list to the terminal.

[0890] Input: Specific meal plans, recipes, and ingredient lists

[0891] Output: Data sent to the terminal

[0892] Step 6:

[0893] The device displays the received data on a user interface, allowing the user to confirm the displayed information and follow the actual meal plan.

[0894] Input: Data sent to the terminal

[0895] Output: Meal plan displayed in the user interface

[0896] Step 7:

[0897] After eating a meal, the user takes a photo of the meal using the device's camera and uploads it to the system.

[0898] Input: Food photo

[0899] Output: Food image data sent to the server

[0900] Step 8:

[0901] The server uses image analysis software, such as OpenCV and TensorFlow, to analyze the uploaded food image data and recognize and estimate the food contents and calories.

[0902] Input: Food image data

[0903] Output: Estimated meal contents and calorie information

[0904] Step 9:

[0905] The server compares the estimated calorie information with the user's calorie goal, and if the total calorie intake exceeds the goal, the server regenerates a modified menu.

[0906] Input: Estimated meal content and calorie information, user's calorie restriction goal

[0907] Output: Modified menu

[0908] Step 10:

[0909] Users use the device's camera and microphone to input their facial expressions and voice, and the emotion engine analyzes this data to estimate the user's emotional state.

[0910] Input: User's facial expressions and voice data

[0911] Output: Estimated emotional state

[0912] Step 11:

[0913] The server predicts a decline in the user's motivation based on the emotional data received from the emotion engine, and uses a generative AI model to create an appropriate advice message and send it to the device.

[0914] Input: Estimated emotional state

[0915] Output: Advice message

[0916] Through these steps, the system can provide users with consistent, personalized health management and motivation.

[0917] (Application example 2)

[0918] 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."

[0919] While conventional health management systems can provide customized diet plans tailored to individual users' health goals, they lack the support to appropriately respond to users' emotional changes and maintain their motivation. Furthermore, they have not implemented health management using virtual reality technology, nor have they suggested appropriate health foods and nutritional supplements in virtual stores to improve the user experience. As a result, there has been a lack of support for users to continue managing their health.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0921] In this invention, the server includes: an input device for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; a calculation device using artificial intelligence to create a customized diet plan based on the input information; a generation device for generating a meal plan, recipes, and a list of ingredients based on the diet plan; a display device for presenting the generated meal plan, recipes, and ingredient list to the user; an upload device for uploading photos of the meals the user has eaten; an analysis device for analyzing the uploaded photos and estimating the meal contents and calories; a correction device for generating a menu that corrects the user's calorie intake if the total calorie intake exceeds the target; a support device for predicting a decrease in motivation and providing timely advice; a presentation device for suggesting health foods and nutritional supplements within the virtual store; an emotion analysis device for analyzing the user's emotional state using an emotion engine and providing messages to maintain motivation based on the analysis results; and a virtual interface device for supporting the user's activities within the virtual reality environment of the virtual store. This not only provides personalized support that responds to the user's emotional changes, but also enhances the shopping experience within the virtual store and enables continuous health management support.

[0922] The "input means" refers to a means for a user to input his / her height, weight, body fat percentage, target weight, and target body fat percentage through a terminal.

[0923] The "calculation means" is a means for creating a customized diet plan using artificial intelligence based on input information.

[0924] The "generation means" is a means for generating a meal plan, recipes, and a list of ingredients required based on a customized diet plan.

[0925] The "display means" is a means for presenting the generated menu, recipe, and ingredient list to the user.

[0926] The "uploading means" is a means for a user to upload a photo of the meal they have eaten to the system.

[0927] The "analysis means" is a means for analyzing uploaded photos and estimating the contents and calories of the food photographed.

[0928] The "correction means" is a means for generating a menu that corrects the user's calorie intake if the user's total calorie intake exceeds the target.

[0929] "Support measures" are means for predicting declines in motivation and providing timely advice.

[0930] The "presentation means" refers to a means for suggesting health foods and nutritional supplements to users within the virtual store.

[0931] The "emotion analysis means" is a means for analyzing the user's emotional state using an emotion engine and providing a message to maintain motivation based on the analysis results.

[0932] "Virtual interface means" refers to means for supporting user actions within the virtual reality environment of the virtual store.

[0933] This invention aims to develop a system for providing customized diet plans using a virtual store, with the aim of helping users manage their health. The system analyzes the user's health information and emotional state, and provides personalized meal plans, suggested products, and advice to maintain motivation.

[0934] 1. Input Method

[0935] First, the user inputs their height, weight, body fat percentage, target weight, and target body fat percentage using a device such as a smartphone, tablet, or head-mounted display (HMD). This information is temporarily stored on the device.

[0936] 2. Means of calculation

[0937] The information entered by the user is sent to a cloud server, which uses a generative AI model (e.g., OpenAI's GPT-4) to generate a customized diet plan based on the information entered. This AI model is pre-trained on a large health management dataset and has highly accurate algorithms to create the optimal plan for each user.

[0938] 3. Generation means

[0939] Based on the generated diet plan, specific daily meal plans, recipes, and ingredient lists are created, allowing users to efficiently prepare meals and shop daily.

[0940] 4. Display means

[0941] The generated menu, recipes, and ingredient list are sent from the cloud server to the user's device and displayed on the device screen, allowing the user to check and act in accordance with the meal plan.

[0942] 5. Uploading Method

[0943] Users take photos of the food they have eaten using their device camera and upload them to the system, where the data is then sent to a cloud server and stored in a database.

[0944] 6. Analysis method

[0945] The cloud server analyzes the uploaded photos and estimates the contents and calories of the meal. The technology used is an image analysis algorithm (e.g., Amazon Rekognition or Google Cloud Vision). This allows for a highly accurate understanding of the user's calorie intake.

[0946] 7. Remedies

[0947] If the total calorie intake exceeds the target, the cloud server will adjust the menu for the next day to maintain a balanced diet, helping users to comfortably approach their target weight.

[0948] 8. Presentation means

[0949] Health foods and nutritional supplements that are useful for health management are presented in a virtual store. The virtual store is constructed using, for example, a head-mounted display (HMD). Users can select and purchase products in the virtual space.

[0950] 9. Emotion analysis method

[0951] To analyze the user's emotional state, facial expression and voice data are collected using the device's camera and microphone. An emotion engine (e.g., Microsoft Azure Cognitive Services) analyzes this data and estimates the user's emotional state. The analysis results are sent to a cloud server and stored in a database.

[0952] 10. Support methods

[0953] The cloud server uses emotion analysis data to provide motivational advice. For example, if a user feels fatigued or stressed, the AI ​​will generate a message such as "Take a short break to refresh yourself" and display it on the device.

[0954] Specific examples

[0955] Specific user scenarios:

[0956] The user inputs information such as height 170 cm, weight 75 kg, body fat percentage 25%, target weight 65 kg, and target body fat percentage 15%. Based on this information, the generative AI model generates a diet plan including appropriate calorie restrictions and exercise plans. Specific menus and recipes for breakfast, lunch, and dinner are presented, and a list of ingredients is automatically generated.

[0957] Example prompt sentence:

[0958] After entering basic user information, the generative AI model receives prompts like this:

[0959] User Basic Information:

[0960] Height: 170 cm

[0961] Weight: 75 kg

[0962] Body fat percentage: 25%

[0963] Target weight: 65 kg

[0964] Target body fat percentage: 15%

[0965] Based on this data, your application should generate a customized diet plan for the user, recommend suitable health foods and supplements in a virtual store, and analyze the user's facial expressions and voice to estimate their emotional state and display advice messages to keep them motivated as needed.

[0966] This example demonstrates how the invention can be put into practice, allowing users to receive consistent, personalized diet and health management support.

[0967] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0968] Step 1:

[0969] The user uses the device to input their height, weight, body fat percentage, target weight, and target body fat percentage. This input information is temporarily stored on the device and later sent to the cloud server. The server receives this information and stores it as data for the next process.

[0970] input:

[0971] User's health information (height, weight, body fat percentage, target weight, target body fat percentage)

[0972] output:

[0973] User health information data stored on a cloud server

[0974] Specific behavior:

[0975] The user enters the necessary information using a smartphone, tablet, or HMD and presses the send button.

[0976] Step 2:

[0977] The cloud server generates a customized diet plan using a generative AI model based on the input health information. The server inputs prompts into the generative AI model to generate the diet plan.

[0978] input:

[0979] Stored user health information data

[0980] output:

[0981] Customized diet plans

[0982] Specific behavior:

[0983] The cloud server inputs data into a generative AI model (e.g., GPT-4) to generate a diet plan, which is then stored on the server.

[0984] Step 3:

[0985] Based on the generated diet plan, the cloud server creates a specific meal plan, recipes, and a list of ingredients, which are then sent to the user's device.

[0986] input:

[0987] Customized diet plans

[0988] output:

[0989] Meal plans, recipes, and ingredient lists

[0990] Specific behavior:

[0991] The cloud server references the recipe database and ingredient database to generate a specific meal plan, which is then sent to the device and displayed for the user to review.

[0992] Step 4:

[0993] After consuming a meal, users use their device to take a photo of the meal and upload it to the system, which then sends the photo to a cloud server for further processing.

[0994] input:

[0995] Food photos taken by users

[0996] output:

[0997] Meal photos uploaded to a cloud server

[0998] Specific behavior:

[0999] Users take a photo of their meal with their device's camera and press the upload button to send it to the server, where it is stored.

[1000] Step 5:

[1001] The cloud server analyzes the uploaded photos and estimates the contents and calories of the meal using image analysis algorithms.

[1002] input:

[1003] Uploaded food photos

[1004] output:

[1005] Analysis data of meal contents and estimated calories

[1006] Specific behavior:

[1007] The cloud server runs an image analysis algorithm (e.g., Amazon Rekognition) to analyze the type and portion size of food from the photo and calculate calories.

[1008] Step 6:

[1009] The server records the analyzed calorie data as the total calories. If the total calorie intake exceeds the target, the menu for the next day is revised. This revised data is then sent back to the user's device.

[1010] input:

[1011] Analyzed food content data and estimated calories

[1012] output:

[1013] Revised meal plans, recipes, and ingredient lists

[1014] Specific behavior:

[1015] The cloud server compares the user's calorie intake history with their goals and adjusts the meal plan for the next day if necessary, then sends the revised plan to the user's device and displays it again on the device screen.

[1016] Step 7:

[1017] Health foods and nutritional supplements are suggested to users in the virtual store, and they can purchase these products using their devices.

[1018] input:

[1019] Customized diet plans and virtual store data

[1020] output:

[1021] Suggested health foods and nutritional supplements

[1022] Specific behavior:

[1023] Users wear a head-mounted display and access a virtual store, select food or supplements using the virtual interface, and press the purchase button.

[1024] Step 8:

[1025] The user's facial expressions and voice are collected through the device's camera and microphone and sent to a cloud server, where an emotion engine analyzes them to estimate the user's emotional state.

[1026] input:

[1027] Your facial and voice data

[1028] output:

[1029] Inferred emotional state

[1030] Specific behavior:

[1031] The device collects data using a camera and microphone, and the cloud server analyzes the data using an emotion engine (e.g., Microsoft Azure Cognitive Services) to estimate emotions.

[1032] Step 9:

[1033] The cloud server uses a generative AI model to generate advice messages to maintain motivation based on the user's emotional state and sends them to the user's device.

[1034] input:

[1035] Inferred emotional state

[1036] output:

[1037] Advice Message

[1038] Specific behavior:

[1039] The cloud server uses a generative AI model to create a message based on the emotion analysis data, and the generated message is sent to the user's device and displayed on the screen.

[1040] Above is a detailed flow of each processing step, which allows users to consistently receive personalized diet plans, healthy food recommendations, and advice based on their emotional state.

[1041] 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.

[1042] 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.

[1043] 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.

[1044] [Third embodiment]

[1045] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1046] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1047] 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).

[1048] 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.

[1049] 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.

[1050] 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).

[1051] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1052] 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.

[1053] 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.

[1054] 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.

[1055] 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.

[1056] 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."

[1057] The present invention is a health management system that inputs a user's height, weight, body fat percentage, target weight, and target body fat percentage and provides a customized diet plan based on the input. Specific embodiments for carrying out the present invention will be described below.

[1058] 1. System Configuration

[1059] This system consists of a terminal for users to input information, a server that performs calculations and analysis, and a terminal that outputs and displays data.

[1060] 2. Input Method

[1061] Users use a device such as a mobile phone or tablet to launch the application and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server.

[1062] 3. Means of calculation

[1063] Based on the information received, the server uses generative AI to generate a customized diet plan, applying algorithms that take into account the user's current health status and goals.

[1064] 4. Generation means

[1065] Based on the diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, allowing users to efficiently prepare and shop for ingredients.

[1066] 5. Display means

[1067] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, where the user can review and work on the actual meal plan.

[1068] 6. Upload Method

[1069] After eating a meal, users can use their device to take a photo of the meal and upload it to the system, allowing the system to record their calorie intake.

[1070] 7. Analysis method

[1071] The server analyzes the uploaded photos and estimates the contents and calories of the food depicted in them, which are then compared to the user's calorie goal and stored in a database.

[1072] 8. Remedies

[1073] The server detects when the user's total calorie intake exceeds their goal and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[1074] 9. Support methods

[1075] To prevent a decline in motivation, the server analyzes past data to learn patterns of declining motivation and then sends advice messages to users at appropriate times based on the results of that learning.

[1076] Specific examples

[1077] For example, suppose a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. When the user enters this information into the device and submits it, the server uses generative AI to create a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and a list of necessary ingredients is also automatically generated.

[1078] Users upload photos of the meals they eat each day, and the server analyzes them to estimate the calories. If the total calories consumed in a day exceeds the target, the server adjusts the menu for the next day to balance the calorie intake. In addition, when the server predicts that the user's motivation will decrease, it sends encouraging messages and specific action plans.

[1079] As described above, the system of the present invention provides users with consistent and personalized diet and health management support.

[1080] The processing flow will be explained below.

[1081] Step 1:

[1082] A user starts the application using a mobile device or tablet. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage into an input form.

[1083] Step 2:

[1084] The terminal displays the entered information on the screen in real time and temporarily stores it after all the information has been entered.

[1085] Step 3:

[1086] The terminal transmits the temporarily stored user information data to the server.

[1087] Step 4:

[1088] The server stores the received user information data in a database.

[1089] Step 5:

[1090] The server retrieves the stored user information from the database and passes it to the generative AI engine.

[1091] Step 6:

[1092] The generative AI engine creates a customized diet plan based on the user's height, weight, body fat percentage, target weight and target body fat percentage.

[1093] Step 7:

[1094] The server generates a diet plan based on the user's preferences and lifestyle, along with meal plans, recipes, and a list of ingredients needed.

[1095] Step 8:

[1096] The server sends the generated menu, recipes, and ingredient list to the terminal.

[1097] Step 9:

[1098] The device displays meal plans, recipes, and ingredient lists to the user, who can then confirm the information and follow the meal plan.

[1099] Step 10:

[1100] After a user consumes a meal, they use their device to take a photo of the meal and upload it to the system.

[1101] Step 11:

[1102] The device sends photo data of the meal to the server.

[1103] Step 12:

[1104] The server analyzes the received photo data and estimates the contents and calories of the food depicted.

[1105] Step 13:

[1106] The server compares the estimated calories with the user's calorie restriction goal and stores the results in a database.

[1107] Step 14:

[1108] The server monitors the total calorie intake stored in the database, and if the target is exceeded, it instructs the generative AI engine to regenerate a revised menu.

[1109] Step 15:

[1110] The server sends the revised menu to the terminal and notifies the user.

[1111] Step 16:

[1112] The user can check the revised menu on the device and adjust their meal plan for the next day.

[1113] Step 17:

[1114] The server analyzes the user's past data and learns patterns of declining motivation.

[1115] Step 18:

[1116] The server predicts when motivation is likely to wane and generates encouraging messages and specific advice.

[1117] Step 19:

[1118] The server sends the generated encouraging message or advice to the terminal and notifies the user.

[1119] Step 20:

[1120] The user checks the message on their device and follows the advice provided.

[1121] The above steps will efficiently support the user's diet and health management.

[1122] Example 1

[1123] 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."

[1124] Conventional diet support systems have been unable to adequately support individual users' dietary management and calorie restriction, and have not provided specific methods for effectively achieving target weight or body fat percentage. Furthermore, they lacked the ability to predict and appropriately respond to users' declining motivation, making continuous health management difficult.

[1125] 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.

[1126] In this invention, the server includes input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage, transmission means for transmitting data to the server, calculation means for creating a customized diet plan based on the input information using artificial intelligence, generation means for generating a meal plan, recipes, and a list of necessary ingredients based on the diet plan, display means for presenting the generated meal plan, recipes, and ingredient list to the user, upload means for uploading photos of the meals the user has eaten, analysis means for analyzing the uploaded photos to estimate the meal contents and calories, correction means for generating a menu that corrects the user's calorie intake if the user's total calorie intake exceeds the target, and support means for predicting a decline in the user's motivation based on past data and providing advice. This allows the user to consistently follow the individually customized diet plan and effectively manage their health.

[1127] "Input means" refers to a device or function that allows a user to input information such as height, weight, body fat percentage, target weight, and target body fat percentage into the system.

[1128] "Transmission means" refers to a device or function that allows the terminal to transmit information entered by the user to the server.

[1129] "Generative artificial intelligence" refers to computational algorithms or models that create customized diet plans based on input information.

[1130] "Computational means" refers to the process or device that analyzes the received user information and generates a diet plan.

[1131] The "creation means" is a device or function for creating a meal plan, recipes, and a list of necessary ingredients based on the diet plan.

[1132] "Display means" refers to a device or function for visually presenting the generated menu, recipe, and ingredient list to the user.

[1133] "Uploading means" refers to a device or function that allows a user to send photos of the food they have eaten to the system.

[1134] "Analysis means" refers to a device or function that analyzes uploaded photos and estimates the contents and calories of meals.

[1135] The "correction means" is a device or function for generating a menu that corrects the course when the total calorie intake exceeds the target.

[1136] "Support tools" are devices or functions that predict a decline in a user's motivation based on past data and provide advice at the appropriate time.

[1137] The present invention is a system developed to support users' health management, which generates a customized diet plan based on information input by the user and supports continuous health management. The following describes in detail the embodiments of the present invention.

[1138] This system consists of a terminal for users to input information, a server that receives and analyzes the data, and a terminal for displaying the generated information.

[1139] Input Method

[1140] The user launches a dedicated application using a device such as a mobile phone or tablet. In the application, basic information such as height, weight, body fat percentage, target weight and target body fat percentage is entered, and this information is temporarily saved on the device. After entering the information, the user presses the "Send" button, and the entered information is sent to the server.

[1141] Transmission method

[1142] The device sends the stored user basic information to the server. This data is sent using a secure communication protocol (e.g. HTTPS).

[1143] means of calculation

[1144] The server then uses the received data to create a customized diet plan using a generative AI model that has previously learned various health data to generate the optimal plan based on the user's current health condition and goals.

[1145] generation means

[1146] The server generates daily or weekly meal plans, recipes, and ingredient lists based on the customized diet plan, a process that includes retrieving recipe and ingredient information from a database.

[1147] Display means

[1148] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, allowing the user to follow the actual meal plan.

[1149] Upload method

[1150] After consuming a meal, the user uses the device to take a photo of the meal and upload it to the system. The uploading operation is performed within the application, and the photo data is sent to the server.

[1151] Analysis means

[1152] The server analyzes the uploaded photos and estimates the food contents and calories of the food depicted in the photos using image recognition algorithms, and the results of the analysis are stored in a database.

[1153] Correction means

[1154] If the user's total calorie intake exceeds their goal, the server will use that information to regenerate a revised menu, which will then be sent back to the device and displayed.

[1155] Support means

[1156] The server analyzes past data to learn patterns of when users lose motivation, and then uses an AI model to generate advice messages to help users maintain their motivation at the right time and send them to their devices.

[1157] Specific examples

[1158] For example, consider a user who is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. The user enters this information into the device and presses the "Send" button. Based on the received information, the server uses a generative AI model to create a diet plan that includes calorie restriction and appropriate exercise. The plan includes specific menus and recipes for breakfast, lunch, and dinner, and an automatically generated list of ingredients is also included. The user uploads photos of their daily meals, and the server analyzes the photos to estimate calories. If the total daily calorie intake exceeds the target, the server adjusts the menu for the next day. The server also sends encouraging messages and specific action plans at times when the user's motivation is predicted to wane.

[1159] Example prompts to input to the generative AI model

[1160] User's current height: 170 cm

[1161] User's current weight: 75 kg

[1162] User's current body fat percentage: 25%

[1163] User target weight: 65 kg

[1164] User's target body fat percentage: 15%

[1165] Use this information to generate a customized diet plan that includes daily and weekly meal plans, recipes, and ingredient lists.

[1166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1167] Step 1:

[1168] The user starts up the device and accesses the application. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage. The entered information is temporarily stored in the device's memory.

[1169] Input: User's height, weight, body fat percentage, target weight, target body fat percentage

[1170] Output: User information temporarily stored on the device

[1171] Specific behavior: The user enters the required information into the input form and presses the "Submit" button.

[1172] Step 2:

[1173] The terminal uses a secure protocol (e.g., HTTPS) to send the input data to the server. The terminal generates JSON-formatted data as a transmission method and sends it to the server using an HTTP POST request.

[1174] Input: User information temporarily stored on the device

[1175] Output: User information sent to the server

[1176] Specific operation: When the "Send" button is pressed, the device generates data in JSON format, issues an HTTP request, and sends it to the server.

[1177] Step 3:

[1178] The server stores the received user information in a database and uses a generative AI model to create a customized diet plan. Based on the received information, the server inputs prompts into the AI ​​model to generate the diet plan.

[1179] Input: User information received by the server (JSON format)

[1180] Output: A customized diet plan

[1181] What it does: The server stores the data in a database and provides prompts to the AI ​​model to generate a diet plan.

[1182] Step 4:

[1183] The server analyzes the generated diet plan and generates daily and weekly meal plans, recipes, and ingredient lists, which are then encoded in JSON format and sent to the device.

[1184] Enter: your customized diet plan.

[1185] Output: JSON format meal plans, recipes, and ingredient lists

[1186] Specific operation: The server generates the necessary information based on the diet plan and encodes it for transmission to the device.

[1187] Step 5:

[1188] The device analyzes the received data and displays it in a visually easy-to-understand format for the user, allowing them to check the generated menu, recipes, and ingredient lists.

[1189] Input: JSON format data sent from the server

[1190] Output: Menus, recipes, and ingredient lists displayed on the device screen

[1191] Specific behavior: The device parses the JSON formatted data and displays the information based on the user interface.

[1192] Step 6:

[1193] After consuming a meal, the user uses the device to take a photo of the meal and upload it to the system. The user then presses the "upload" button in the application to send the photo to the server.

[1194] Input: A photo of a meal taken by the user

[1195] Output: Food photos uploaded to the server

[1196] Specific actions: The user takes a photo with the device's camera and presses the "upload" button within the app.

[1197] Step 7:

[1198] The server analyzes the uploaded photos and uses image recognition algorithms to estimate the contents and calories of the meal, and the analysis results are stored in a database.

[1199] Input: User-uploaded food photos

[1200] Output: Estimated meal contents and calorie information

[1201] What it does: The server runs an image recognition algorithm to identify the type of food and its calories.

[1202] Step 8:

[1203] If the user's total calorie intake exceeds their goal, the server will use that information to revise the next day's meal plan, which will then be sent back to the device and displayed to the user.

[1204] Input: Estimated meal contents and calorie information

[1205] Output: Modified meal plan

[1206] Specific operation: The server analyzes the historical data, generates a new meal plan, and sends it to the device.

[1207] Step 9:

[1208] The server analyzes past data and learns patterns of when the user's motivation declines, then generates encouraging and advice messages at the appropriate time and sends them to the device.

[1209] Input: User's historical data

[1210] Output: Advice message

[1211] What happens: The server uses machine learning models to analyze the data, generate advice messages, and send them.

[1212] (Application example 1)

[1213] 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."

[1214] Health management requires not only providing users with personalized diet plans tailored to their individual needs, but also a wide range of support, such as purchasing the ingredients needed to actually follow the plan and maintaining motivation. However, current systems are not adequately able to suggest individual health foods to users, provide support for purchasing in physical stores, or maintain ongoing motivation. Therefore, there is a need to provide comprehensive support to help users achieve their goals.

[1215] 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.

[1216] In this invention, the server includes: an input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; a calculation means for creating a customized diet plan based on the input information using artificial intelligence; a generation means for generating a meal plan, recipes, and a list of necessary ingredients based on the diet plan; a display means for presenting the generated meal plan, recipes, and ingredient list to the user; an upload means for uploading photos of the meals the user has eaten; an analysis means for analyzing the uploaded photos and estimating the meal contents and calories; a correction means for generating a menu that corrects the user's calorie intake if the total calorie intake exceeds the target; a support means for predicting a decrease in motivation and providing timely advice; and a means for suggesting health foods sold in physical stores to the user and providing purchasing support based on the list of necessary ingredients. This allows users to receive comprehensive support for their individual health goals and effectively manage their health.

[1217] "User" refers to an individual who uses the health care system.

[1218] "Height" is the vertical distance from the bottom of the user's feet to the top of their head.

[1219] "Weight" is the total body mass of the user.

[1220] "Body fat percentage" is the percentage of fat in relation to the user's body weight.

[1221] "Goal weight" is the weight that a user wishes to achieve.

[1222] "Target body fat percentage" is the percentage of body fat that the user wishes to achieve.

[1223] "Input means" refers to a device or method that allows a user to input their physical information into the system.

[1224] "Generative AI" is an AI technology that generates customized diet plans based on user information.

[1225] A "computing means" is a device or method for carrying out the process of generating a diet plan based on user input information.

[1226] The "generation means" refers to a device or method for creating a specific meal plan, recipes, and a list of ingredients based on the diet plan generated by the calculation means.

[1227] "Display means" refers to a device or method for presenting the generated menu, recipe, and ingredient list to the user.

[1228] An "uploading means" is a device or method that allows a user to send a photo of the meal they have eaten to the system.

[1229] The "analysis means" refers to a device or method for analyzing uploaded photos and estimating the contents and calories of meals.

[1230] A "modifier" is a device or method for reformulating a diet plan if total calorie intake exceeds the target.

[1231] "Support measures" are devices and methods for predicting a decline in motivation and providing appropriate advice.

[1232] A "brick and mortar store" is a store that sells health foods in a physical location.

[1233] "Health foods" are foods marketed to support the health of users.

[1234] A "purchasing support tool" is a device or method that supports users in efficiently shopping at physical stores based on a list of necessary ingredients.

[1235] The system of the present invention is configured as follows to enhance individual health support for users.

[1236] 1. System Configuration

[1237] The system consists of a terminal for users to input information, a server that performs calculations and analysis, and a terminal that outputs and displays data. The main hardware used is a smartphone or tablet, and the software includes React Native (mobile application development), Python, MySQL, and TensorFlow.

[1238] 2. Input Method

[1239] Users launch the application using a device such as a smartphone or tablet and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server via HTTPS.

[1240] 3. Means of calculation

[1241] The server uses the received information to generate a customized diet plan using a generative AI model, applying algorithms that take into account the user's current health status and goals.

[1242] 4. Generation means

[1243] The server generates specific meal plans, recipes, and ingredient lists based on the generated diet plan, making it easier for the user to prepare and shop for specific ingredients.

[1244] 5. Display means

[1245] The generated menu, recipes, and ingredient list are sent from the server to the terminal and displayed on the user's terminal screen, allowing the user to actually carry out the plan.

[1246] 6. Upload Method

[1247] After eating, users use their device to take a photo of the meal and upload it to the system, which records their calorie intake.

[1248] 7. Analysis method

[1249] The server analyzes the uploaded photos and estimates the contents and calories of the food photographed, which are then compared to the user's calorie goal and stored in a database.

[1250] 8. Remedies

[1251] If the total calorie intake exceeds the target, the server detects this and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[1252] 9. Support methods

[1253] The server analyzes past data and learns patterns of declining motivation. Based on the results of this learning, it sends advice messages to users at appropriate times, thereby maintaining their motivation.

[1254] 10. Healthy food recommendations and purchasing support methods

[1255] In addition, the service suggests health foods sold in physical stores to users and provides purchasing support based on a list of necessary ingredients, allowing users to efficiently purchase the health foods they need in physical stores.

[1256] Specific examples

[1257] For example, if a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and target body fat percentage at 15%, the system operates as follows: When the user enters this information into their device and sends it to the server, the generative AI model creates a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and also generates a list of necessary ingredients. The user uploads photos of the meals they eat each day, and the server analyzes them to estimate calories. If the total calories consumed in a day exceed the target, the server adjusts the menu for the next day to balance calorie intake.

[1258] "Generate a diet plan for a health management system. The user is 170 cm tall, 75 kg, and has a body fat percentage of 25%. Their target weight is 65 kg and their target body fat percentage is 15%."

[1259] In this way, the system provides users with consistent and personalized diet and health management support.

[1260] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1261] Step 1:

[1262] Using a device, a user launches the application and enters basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server via HTTPS. The entered data is related to the user's health status and goals.

[1263] Step 2:

[1264] The server then takes the received user information and generates a customized diet plan using a generative AI model. Specifically, it applies an algorithm that takes into account the user's current health status and goals. It receives the user's health data as input and generates a diet plan that includes calorie restriction and exercise as output.

[1265] Step 3:

[1266] The server generates specific meal plans, recipes, and ingredient lists based on the generated diet plan, including specific menus that are easy for users to follow. It receives the generated diet plan as input and generates specific meal plans and ingredient lists as output.

[1267] Step 4:

[1268] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the user's device screen. The user checks this output and works on the actual meal plan. In this step, data transfer and UI updates are performed to display the data generated by the server on the user's device.

[1269] Step 5:

[1270] After consuming a meal, the user takes a photo of the meal using their device and uploads it to the system. This photo becomes input data for the next analysis step. The user's actions are to take a photo of the meal and upload it.

[1271] Step 6:

[1272] The server analyzes the uploaded photos and estimates the contents and calories of the food in them. Specifically, it uses TensorFlow and SSD models to analyze the images and recognize the food contents. It receives food images as input and generates food contents and calorie value data as output.

[1273] Step 7:

[1274] The calorie intake obtained as a result of the analysis is compared with the user's calorie restriction target and stored in a database. If the total calorie intake exceeds the target, the server detects this situation and regenerates a menu with corrected course. By adjusting the menu for the next day, the user can prevent over-intake. The system receives the analyzed calorie value data as input and generates a revised diet plan as output.

[1275] Step 8:

[1276] The server analyzes past data and learns patterns of declining motivation. Based on this learning, it sends advice messages to users at appropriate times. This helps maintain the user's motivation and support continued health management. It receives the user's behavioral data as input and generates advice messages as output.

[1277] Step 9:

[1278] The system suggests health foods sold in physical stores to users and provides purchasing support based on the required ingredient list. Users can use this information to shop efficiently in physical stores. The server provides optimal store information and product suggestions based on the user's ingredient list. It receives the ingredient list as input and generates purchasing support information for the store as output.

[1279] Through the above processing steps, the present invention becomes a system that provides users with consistently personalized diet and health management support.

[1280] 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.

[1281] The present invention is a health management system that allows a user to input their height, weight, body fat percentage, target weight, and target body fat percentage and provides a customized diet plan based on the input. Furthermore, by combining the present invention with an emotion engine that recognizes the user's emotions, the present invention more effectively supports maintaining motivation. Specific embodiments for implementing the present invention are described below.

[1282] 1. System Configuration

[1283] This system consists of a terminal for users to input information, a server that performs calculations and analysis, an emotion engine, and a terminal that outputs and displays data.

[1284] 2. Input Method

[1285] Users use a device such as a mobile phone or tablet to launch the application and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server.

[1286] 3. Means of calculation

[1287] Based on the information received, the server uses generative AI to generate a customized diet plan, applying algorithms that take into account the user's current health status and goals.

[1288] 4. Generation means

[1289] Based on the diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, allowing users to efficiently prepare and shop for ingredients.

[1290] 5. Display means

[1291] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, where the user can review and work on the actual meal plan.

[1292] 6. Upload Method

[1293] After eating a meal, users can use their device to take a photo of the meal and upload it to the system, allowing the system to record their calorie intake.

[1294] 7. Analysis method

[1295] The server analyzes the uploaded photos and estimates the contents and calories of the food depicted in them, which are then compared to the user's calorie goal and stored in a database.

[1296] 8. Remedies

[1297] The server detects when the user's total calorie intake exceeds their goal and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[1298] 9. Emotion Engine

[1299] The emotion engine has the ability to analyze the user's facial expressions and voice to estimate their emotional state. The emotion engine is installed on the device and collects data from the user's daily actions and speech.

[1300] Examples:

[1301] The user captures their facial expression through the device's camera and inputs voice information into the device.

[1302] The emotion engine analyzes the collected data and estimates the user's emotional state, which is then sent to the server in real time.

[1303] 10. Support methods

[1304] The server receives data from the emotion engine and predicts a decline in motivation based on the user's emotional state. Based on the predicted information, the generative AI generates timely advice messages and sends them to the device.

[1305] Examples:

[1306] The server predicts when a user shows signs of stress or fatigue and sends them messages of encouragement and tips on how to relax.

[1307] For example, a message might be sent saying, "You've worked hard today. Let's take a break and refresh yourself."

[1308] Specific examples

[1309] Let's say a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. When the user enters this information into the device and submits it, the server uses generative AI to create a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and a list of necessary ingredients is also automatically generated.

[1310] Users upload photos of the meals they eat each day, and the server analyzes them to estimate the calories. If the total calories consumed in a day exceeds the target, the server adjusts the menu for the next day to balance the calorie intake. In addition, when the server predicts that the user's motivation will decrease, it sends encouraging messages and specific action plans.

[1311] The emotion engine analyzes the user's facial expressions and voice and transmits their emotional state in real time to the server, which uses this information to provide appropriate advice when the user is feeling stressed or tired.

[1312] As described above, the system of the present invention provides users with consistent and personalized diet and health management support.

[1313] The processing flow will be explained below.

[1314] Step 1:

[1315] A user starts the application using a mobile device or tablet. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage into an input form.

[1316] Step 2:

[1317] The terminal displays the entered information on the screen in real time and temporarily stores it after all the information has been entered.

[1318] Step 3:

[1319] The terminal transmits the temporarily stored user information data to the server.

[1320] Step 4:

[1321] The server stores the received user information data in a database.

[1322] Step 5:

[1323] The server retrieves the stored user information from the database and passes it to the generative AI engine.

[1324] Step 6:

[1325] The generative AI engine creates a customized diet plan based on the user's height, weight, body fat percentage, target weight and target body fat percentage.

[1326] Step 7:

[1327] The server generates a diet plan based on the user's preferences and lifestyle, along with meal plans, recipes, and a list of ingredients needed.

[1328] Step 8:

[1329] The server sends the generated menu, recipes, and ingredient list to the terminal.

[1330] Step 9:

[1331] The device displays meal plans, recipes, and ingredient lists to the user, who can then confirm the information and follow the meal plan.

[1332] Step 10:

[1333] After a user consumes a meal, they use their device to take a photo of the meal and upload it to the system.

[1334] Step 11:

[1335] The device sends photo data of the meal to the server.

[1336] Step 12:

[1337] The server analyzes the received photo data and estimates the contents and calories of the food depicted.

[1338] Step 13:

[1339] The server compares the estimated calories with the user's calorie restriction goal and stores the results in a database.

[1340] Step 14:

[1341] The server monitors the total calorie intake stored in the database, and if the target is exceeded, it instructs the generative AI engine to regenerate a revised menu.

[1342] Step 15:

[1343] The server sends the revised menu to the terminal and notifies the user.

[1344] Step 16:

[1345] The user can check the revised menu on the device and adjust their meal plan for the next day.

[1346] Step 17:

[1347] The emotion engine captures the user's facial expressions and voice through the device, either by speaking into the sensor or by taking a photo.

[1348] Step 18:

[1349] Based on the data captured by the emotion engine, the user's emotional state is analyzed and the emotional information is sent to the server.

[1350] Step 19:

[1351] The server receives the emotion information sent from the emotion engine and predicts a decrease in the user's motivation.

[1352] Step 20:

[1353] Based on the predicted decline in motivation, the server instructs the generative AI engine to create timely advice messages.

[1354] Step 21:

[1355] The server sends the generated advice message to the terminal to notify the user.

[1356] Step 22:

[1357] The user checks the advice message on the device and follows the advice provided.

[1358] These steps help users manage their diet and health in an efficient and personalized way, and by taking into account the user's emotional state, the system can more effectively maintain motivation.

[1359] Example 2

[1360] 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."

[1361] Conventional health management systems have issues in that they do not adequately provide users with personalized diet plans, and do not respond appropriately to users' emotional state or a decline in motivation. Furthermore, they lack a mechanism for accurately analyzing the calories of the food a user consumes and providing appropriate meal plans in real time based on that information. Furthermore, there is a need for a system that can provide appropriate advice when motivation declines, allowing users to continue effective health management over the long term.

[1362] 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.

[1363] In this invention, the server includes: input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; calculation means for creating a customized management plan based on the input information using artificial intelligence; generation means for generating a meal plan, recipes, and ingredient list based on the management plan; display means for presenting the generated meal plan, recipes, and ingredient list to the user; transfer means for uploading images of the meals the user has eaten; analysis means for analyzing the uploaded images and estimating the meal contents and calories; adjustment means for generating a revised meal plan based on the estimated calories if the total calorie intake exceeds the target; and support means for estimating the user's emotional state from facial expressions and voice, predicting a decrease in motivation based on the estimated calories, and providing timely advice. This enables the user to receive consistent, personalized diet and health management support, as well as timely advice based on the user's emotional state, to support long-term health management.

[1364] The "input means" is a means for a user to input data such as height, weight, body fat percentage, target weight, and target body fat percentage through a terminal.

[1365] "Calculation means" refers to a means for creating a customized management plan using generative artificial intelligence based on data input by a user.

[1366] The "generation means" is a means for generating a specific meal menu, recipes, and a list of necessary ingredients based on the management plan created by the calculation means.

[1367] The "display means" is a means for visually presenting to the user the menu, recipe, and ingredient list generated by the generation means.

[1368] The "transfer means" is a means for a user to upload images of the food they have eaten to the system via their terminal.

[1369] The "analysis means" is a means for analyzing the image of the meal uploaded via the transfer means and estimating the contents and calories of the meal.

[1370] The "adjustment means" is a means for generating a revised menu based on the calories estimated by the analysis means when the total calorie intake exceeds the target.

[1371] "Support measures" are means for estimating the user's emotional state from their facial expressions and voice, predicting a decline in motivation based on that information, and providing timely advice.

[1372] The present invention is a health management system that provides a customized diet plan based on the user's height, weight, body fat percentage, target weight, and target body fat percentage. Furthermore, the system has a function to recognize the user's emotions and help maintain motivation.

[1373] This system consists of eight main components: input means, calculation means, generation means, display means, transfer means, analysis means, adjustment means, and support means.

[1374] First, the user launches a dedicated app on their mobile phone or tablet. Then, the user enters information such as their height, weight, body fat percentage, target weight, and target body fat percentage. This information is temporarily stored on the device and then sent to a server via the Internet.

[1375] The server uses the received information to generate a customized diet plan for the user using a generative AI model (e.g., GPT-3). The following prompt is input to the generative AI model:

[1376] "Create a diet plan based on my height of 170 cm, weight of 75 kg, and body fat percentage of 25%, and my target weight of 65 kg and target body fat percentage of 15%."

[1377] The generated diet plan includes a daily or weekly meal plan, recipes, and a list of ingredients. The server sends this data to the device, which displays it to the user. The user can then refer to the displayed menu and recipes to prepare the actual meal.

[1378] After consuming a meal, the user takes a photo of the meal using their device and uploads it to the system. This data is sent to a server, which uses image analysis techniques (e.g., OpenCV or TensorFlow) to estimate the meal contents and calories. The estimated calorie information is compared with the user's calorie restriction goal and stored in a database.

[1379] If the total calorie intake for the day exceeds the target, the server will regenerate a menu with corrected calories and adjust the diet plan for the next day. This process helps users avoid overeating and enables effective health management.

[1380] The system is also equipped with an emotion engine that analyzes the user's facial expressions and voice to estimate their emotional state. The user inputs their facial expressions and voice using the device's camera and microphone, and the data is sent to the server in real time. The server predicts a decline in the user's motivation based on the emotional data, and uses a generative AI model to create an appropriate advice message and send it to the device. For example, a timely message such as, "You seem a little tired today. I recommend taking a short walk as a way to relax" is provided.

[1381] As described above, the system of the present invention can provide users with consistent and personalized diet and health management support, as well as timely advice based on the user's emotional state, to support long-term health management.

[1382] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1383] Step 1:

[1384] The user launches the dedicated app and inputs their height, weight, body fat percentage, target weight, and target body fat percentage. The input information is temporarily stored on the device.

[1385] Input: User's height, weight, body fat percentage, goal weight, and goal body fat percentage

[1386] Output: Temporarily stored user information data

[1387] Step 2:

[1388] The device sends the saved user information to the server using an HTTP request.

[1389] Input: Temporarily stored user information data

[1390] Output: User information data sent to the server

[1391] Step 3:

[1392] The server creates and inputs a prompt sentence into the generative AI model based on the received user information.

[1393] Input: Prompt and user information: "Create a diet plan based on a height of 170 cm, weight of 75 kg, body fat percentage of 25%, target weight of 65 kg, and target body fat percentage of 15%."

[1394] Output: A customized diet plan from the generative AI

[1395] Step 4:

[1396] Based on the generated diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, taking into account the user's calorie restrictions and nutritional balance.

[1397] Input: Generated diet plan

[1398] Output: Specific meal plans, recipes, and ingredient lists

[1399] Step 5:

[1400] The server transmits the generated menu, recipes, and ingredient list to the terminal.

[1401] Input: Specific meal plans, recipes, and ingredient lists

[1402] Output: Data sent to the terminal

[1403] Step 6:

[1404] The device displays the received data on a user interface, allowing the user to confirm the displayed information and follow the actual meal plan.

[1405] Input: Data sent to the terminal

[1406] Output: Meal plan displayed in the user interface

[1407] Step 7:

[1408] After eating a meal, the user takes a photo of the meal using the device's camera and uploads it to the system.

[1409] Input: Food photo

[1410] Output: Food image data sent to the server

[1411] Step 8:

[1412] The server uses image analysis software, such as OpenCV and TensorFlow, to analyze the uploaded food image data and recognize and estimate the food contents and calories.

[1413] Input: Food image data

[1414] Output: Estimated meal contents and calorie information

[1415] Step 9:

[1416] The server compares the estimated calorie information with the user's calorie goal, and if the total calorie intake exceeds the goal, the server regenerates a modified menu.

[1417] Input: Estimated meal content and calorie information, user's calorie restriction goal

[1418] Output: Modified menu

[1419] Step 10:

[1420] Users use the device's camera and microphone to input their facial expressions and voice, and the emotion engine analyzes this data to estimate the user's emotional state.

[1421] Input: User's facial expressions and voice data

[1422] Output: Estimated emotional state

[1423] Step 11:

[1424] The server predicts a decline in the user's motivation based on the emotional data received from the emotion engine, and uses a generative AI model to create an appropriate advice message and send it to the device.

[1425] Input: Estimated emotional state

[1426] Output: Advice message

[1427] Through these steps, the system can provide users with consistent, personalized health management and motivation.

[1428] (Application example 2)

[1429] 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."

[1430] While conventional health management systems can provide customized diet plans tailored to individual users' health goals, they lack the support to appropriately respond to users' emotional changes and maintain their motivation. Furthermore, they have not implemented health management using virtual reality technology, nor have they suggested appropriate health foods and nutritional supplements in virtual stores to improve the user experience. As a result, there has been a lack of support for users to continue managing their health.

[1431] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1432] In this invention, the server includes: an input device for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; a calculation device using artificial intelligence to create a customized diet plan based on the input information; a generation device for generating a meal plan, recipes, and a list of ingredients based on the diet plan; a display device for presenting the generated meal plan, recipes, and ingredient list to the user; an upload device for uploading photos of the meals the user has eaten; an analysis device for analyzing the uploaded photos and estimating the meal contents and calories; a correction device for generating a menu that corrects the user's calorie intake if the total calorie intake exceeds the target; a support device for predicting a decrease in motivation and providing timely advice; a presentation device for suggesting health foods and nutritional supplements within the virtual store; an emotion analysis device for analyzing the user's emotional state using an emotion engine and providing messages to maintain motivation based on the analysis results; and a virtual interface device for supporting the user's activities within the virtual reality environment of the virtual store. This not only provides personalized support that responds to the user's emotional changes, but also enhances the shopping experience within the virtual store and enables continuous health management support.

[1433] The "input means" refers to a means for a user to input his / her height, weight, body fat percentage, target weight, and target body fat percentage through a terminal.

[1434] The "calculation means" is a means for creating a customized diet plan using artificial intelligence based on input information.

[1435] The "generation means" is a means for generating a meal plan, recipes, and a list of ingredients required based on a customized diet plan.

[1436] The "display means" is a means for presenting the generated menu, recipe, and ingredient list to the user.

[1437] The "uploading means" is a means for a user to upload a photo of the meal they have eaten to the system.

[1438] The "analysis means" is a means for analyzing uploaded photos and estimating the contents and calories of the food photographed.

[1439] The "correction means" is a means for generating a menu that corrects the user's calorie intake if the user's total calorie intake exceeds the target.

[1440] "Support measures" are means for predicting declines in motivation and providing timely advice.

[1441] The "presentation means" refers to a means for suggesting health foods and nutritional supplements to users within the virtual store.

[1442] The "emotion analysis means" is a means for analyzing the user's emotional state using an emotion engine and providing a message to maintain motivation based on the analysis results.

[1443] "Virtual interface means" refers to means for supporting user actions within the virtual reality environment of the virtual store.

[1444] This invention aims to develop a system for providing customized diet plans using a virtual store, with the aim of helping users manage their health. The system analyzes the user's health information and emotional state, and provides personalized meal plans, suggested products, and advice to maintain motivation.

[1445] 1. Input Method

[1446] First, the user inputs their height, weight, body fat percentage, target weight, and target body fat percentage using a device such as a smartphone, tablet, or head-mounted display (HMD). This information is temporarily stored on the device.

[1447] 2. Means of calculation

[1448] The information entered by the user is sent to a cloud server, which uses a generative AI model (e.g., OpenAI's GPT-4) to generate a customized diet plan based on the information entered. This AI model is pre-trained on a large health management dataset and has highly accurate algorithms to create the optimal plan for each user.

[1449] 3. Generation means

[1450] Based on the generated diet plan, specific daily meal plans, recipes, and ingredient lists are created, allowing users to efficiently prepare meals and shop daily.

[1451] 4. Display means

[1452] The generated menu, recipes, and ingredient list are sent from the cloud server to the user's device and displayed on the device screen, allowing the user to check and act in accordance with the meal plan.

[1453] 5. Uploading Method

[1454] Users take photos of the food they have eaten using their device camera and upload them to the system, where the data is then sent to a cloud server and stored in a database.

[1455] 6. Analysis method

[1456] The cloud server analyzes the uploaded photos and estimates the contents and calories of the meal. The technology used is an image analysis algorithm (e.g., Amazon Rekognition or Google Cloud Vision). This allows for a highly accurate understanding of the user's calorie intake.

[1457] 7. Remedies

[1458] If the total calorie intake exceeds the target, the cloud server will adjust the menu for the next day to maintain a balanced diet, helping users to comfortably approach their target weight.

[1459] 8. Presentation means

[1460] Health foods and nutritional supplements that are useful for health management are presented in a virtual store. The virtual store is constructed using, for example, a head-mounted display (HMD). Users can select and purchase products in the virtual space.

[1461] 9. Emotion analysis method

[1462] To analyze the user's emotional state, facial expression and voice data are collected using the device's camera and microphone. An emotion engine (e.g., Microsoft Azure Cognitive Services) analyzes this data and estimates the user's emotional state. The analysis results are sent to a cloud server and stored in a database.

[1463] 10. Support methods

[1464] The cloud server uses emotion analysis data to provide motivational advice. For example, if a user feels fatigued or stressed, the AI ​​will generate a message such as "Take a short break to refresh yourself" and display it on the device.

[1465] Specific examples

[1466] Specific user scenarios:

[1467] The user inputs information such as height 170 cm, weight 75 kg, body fat percentage 25%, target weight 65 kg, and target body fat percentage 15%. Based on this information, the generative AI model generates a diet plan including appropriate calorie restrictions and exercise plans. Specific menus and recipes for breakfast, lunch, and dinner are presented, and a list of ingredients is automatically generated.

[1468] Example prompt sentence:

[1469] After entering basic user information, the generative AI model receives prompts like this:

[1470] User Basic Information:

[1471] Height: 170 cm

[1472] Weight: 75 kg

[1473] Body fat percentage: 25%

[1474] Target weight: 65 kg

[1475] Target body fat percentage: 15%

[1476] Based on this data, your application should generate a customized diet plan for the user, recommend suitable health foods and supplements in a virtual store, and analyze the user's facial expressions and voice to estimate their emotional state and display advice messages to keep them motivated as needed.

[1477] This example demonstrates how the invention can be put into practice, allowing users to receive consistent, personalized diet and health management support.

[1478] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1479] Step 1:

[1480] The user uses the device to input their height, weight, body fat percentage, target weight, and target body fat percentage. This input information is temporarily stored on the device and later sent to the cloud server. The server receives this information and stores it as data for the next process.

[1481] input:

[1482] User's health information (height, weight, body fat percentage, target weight, target body fat percentage)

[1483] output:

[1484] User health information data stored on a cloud server

[1485] Specific behavior:

[1486] The user enters the necessary information using a smartphone, tablet, or HMD and presses the send button.

[1487] Step 2:

[1488] The cloud server generates a customized diet plan using a generative AI model based on the input health information. The server inputs prompts into the generative AI model to generate the diet plan.

[1489] input:

[1490] Stored user health information data

[1491] output:

[1492] Customized diet plans

[1493] Specific behavior:

[1494] The cloud server inputs data into a generative AI model (e.g., GPT-4) to generate a diet plan, which is then stored on the server.

[1495] Step 3:

[1496] Based on the generated diet plan, the cloud server creates a specific meal plan, recipes, and a list of ingredients, which are then sent to the user's device.

[1497] input:

[1498] Customized diet plans

[1499] output:

[1500] Meal plans, recipes, and ingredient lists

[1501] Specific behavior:

[1502] The cloud server references the recipe database and ingredient database to generate a specific meal plan, which is then sent to the device and displayed for the user to review.

[1503] Step 4:

[1504] After consuming a meal, users use their device to take a photo of the meal and upload it to the system, which then sends the photo to a cloud server for further processing.

[1505] input:

[1506] Food photos taken by users

[1507] output:

[1508] Meal photos uploaded to a cloud server

[1509] Specific behavior:

[1510] Users take a photo of their meal with their device's camera and press the upload button to send it to the server, where it is stored.

[1511] Step 5:

[1512] The cloud server analyzes the uploaded photos and estimates the contents and calories of the meal using image analysis algorithms.

[1513] input:

[1514] Uploaded food photos

[1515] output:

[1516] Analysis data of meal contents and estimated calories

[1517] Specific behavior:

[1518] The cloud server runs an image analysis algorithm (e.g., Amazon Rekognition) to analyze the type and portion size of food from the photo and calculate calories.

[1519] Step 6:

[1520] The server records the analyzed calorie data as the total calories. If the total calorie intake exceeds the target, the menu for the next day is revised. This revised data is then sent back to the user's device.

[1521] input:

[1522] Analyzed food content data and estimated calories

[1523] output:

[1524] Revised meal plans, recipes, and ingredient lists

[1525] Specific behavior:

[1526] The cloud server compares the user's calorie intake history with their goals and adjusts the meal plan for the next day if necessary, then sends the revised plan to the user's device and displays it again on the device screen.

[1527] Step 7:

[1528] Health foods and nutritional supplements are suggested to users in the virtual store, and they can purchase these products using their devices.

[1529] input:

[1530] Customized diet plans and virtual store data

[1531] output:

[1532] Suggested health foods and nutritional supplements

[1533] Specific behavior:

[1534] Users wear a head-mounted display and access a virtual store, select food or supplements using the virtual interface, and press the purchase button.

[1535] Step 8:

[1536] The user's facial expressions and voice are collected through the device's camera and microphone and sent to a cloud server, where an emotion engine analyzes them to estimate the user's emotional state.

[1537] input:

[1538] Your facial and voice data

[1539] output:

[1540] Inferred emotional state

[1541] Specific behavior:

[1542] The device collects data using a camera and microphone, and the cloud server analyzes the data using an emotion engine (e.g., Microsoft Azure Cognitive Services) to estimate emotions.

[1543] Step 9:

[1544] The cloud server uses a generative AI model to generate advice messages to maintain motivation based on the user's emotional state and sends them to the user's device.

[1545] input:

[1546] Inferred emotional state

[1547] output:

[1548] Advice Message

[1549] Specific behavior:

[1550] The cloud server uses a generative AI model to create a message based on the emotion analysis data, and the generated message is sent to the user's device and displayed on the screen.

[1551] Above is a detailed flow of each processing step, which allows users to consistently receive personalized diet plans, healthy food recommendations, and advice based on their emotional state.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] [Fourth embodiment]

[1556] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1557] 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.

[1558] 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).

[1559] 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.

[1560] 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.

[1561] 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).

[1562] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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."

[1569] The present invention is a health management system that inputs a user's height, weight, body fat percentage, target weight, and target body fat percentage and provides a customized diet plan based on the input. Specific embodiments for carrying out the present invention will be described below.

[1570] 1. System Configuration

[1571] This system consists of a terminal for users to input information, a server that performs calculations and analysis, and a terminal that outputs and displays data.

[1572] 2. Input Method

[1573] Users use a device such as a mobile phone or tablet to launch the application and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server.

[1574] 3. Means of calculation

[1575] Based on the information received, the server uses generative AI to generate a customized diet plan, applying algorithms that take into account the user's current health status and goals.

[1576] 4. Generation means

[1577] Based on the diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, allowing users to efficiently prepare and shop for ingredients.

[1578] 5. Display means

[1579] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, where the user can review and work on the actual meal plan.

[1580] 6. Upload Method

[1581] After eating a meal, users can use their device to take a photo of the meal and upload it to the system, allowing the system to record their calorie intake.

[1582] 7. Analysis method

[1583] The server analyzes the uploaded photos and estimates the contents and calories of the food depicted in them, which are then compared to the user's calorie goal and stored in a database.

[1584] 8. Remedies

[1585] The server detects when the user's total calorie intake exceeds their goal and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[1586] 9. Support methods

[1587] To prevent a decline in motivation, the server analyzes past data to learn patterns of declining motivation and then sends advice messages to users at appropriate times based on the results of that learning.

[1588] Specific examples

[1589] For example, suppose a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. When the user enters this information into the device and submits it, the server uses generative AI to create a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and a list of necessary ingredients is also automatically generated.

[1590] Users upload photos of the meals they eat each day, and the server analyzes them to estimate the calories. If the total calories consumed in a day exceeds the target, the server adjusts the menu for the next day to balance the calorie intake. In addition, when the server predicts that the user's motivation will decrease, it sends encouraging messages and specific action plans.

[1591] As described above, the system of the present invention provides users with consistent and personalized diet and health management support.

[1592] The processing flow will be explained below.

[1593] Step 1:

[1594] A user starts the application using a mobile device or tablet. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage into an input form.

[1595] Step 2:

[1596] The terminal displays the entered information on the screen in real time and temporarily stores it after all the information has been entered.

[1597] Step 3:

[1598] The terminal transmits the temporarily stored user information data to the server.

[1599] Step 4:

[1600] The server stores the received user information data in a database.

[1601] Step 5:

[1602] The server retrieves the stored user information from the database and passes it to the generative AI engine.

[1603] Step 6:

[1604] The generative AI engine creates a customized diet plan based on the user's height, weight, body fat percentage, target weight and target body fat percentage.

[1605] Step 7:

[1606] The server generates a diet plan based on the user's preferences and lifestyle, along with meal plans, recipes, and a list of ingredients needed.

[1607] Step 8:

[1608] The server sends the generated menu, recipes, and ingredient list to the terminal.

[1609] Step 9:

[1610] The device displays meal plans, recipes, and ingredient lists to the user, who can then confirm the information and follow the meal plan.

[1611] Step 10:

[1612] After a user consumes a meal, they use their device to take a photo of the meal and upload it to the system.

[1613] Step 11:

[1614] The device sends photo data of the meal to the server.

[1615] Step 12:

[1616] The server analyzes the received photo data and estimates the contents and calories of the food depicted.

[1617] Step 13:

[1618] The server compares the estimated calories with the user's calorie restriction goal and stores the results in a database.

[1619] Step 14:

[1620] The server monitors the total calorie intake stored in the database, and if the target is exceeded, it instructs the generative AI engine to regenerate a revised menu.

[1621] Step 15:

[1622] The server sends the revised menu to the terminal and notifies the user.

[1623] Step 16:

[1624] The user can check the revised menu on the device and adjust their meal plan for the next day.

[1625] Step 17:

[1626] The server analyzes the user's past data and learns patterns of declining motivation.

[1627] Step 18:

[1628] The server predicts when motivation is likely to wane and generates encouraging messages and specific advice.

[1629] Step 19:

[1630] The server sends the generated encouraging message or advice to the terminal and notifies the user.

[1631] Step 20:

[1632] The user checks the message on their device and follows the advice provided.

[1633] The above steps will efficiently support the user's diet and health management.

[1634] Example 1

[1635] 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."

[1636] Conventional diet support systems have been unable to adequately support individual users' dietary management and calorie restriction, and have not provided specific methods for effectively achieving target weight or body fat percentage. Furthermore, they lacked the ability to predict and appropriately respond to users' declining motivation, making continuous health management difficult.

[1637] 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.

[1638] In this invention, the server includes input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage, transmission means for transmitting data to the server, calculation means for creating a customized diet plan based on the input information using artificial intelligence, generation means for generating a meal plan, recipes, and a list of necessary ingredients based on the diet plan, display means for presenting the generated meal plan, recipes, and ingredient list to the user, upload means for uploading photos of the meals the user has eaten, analysis means for analyzing the uploaded photos to estimate the meal contents and calories, correction means for generating a menu that corrects the user's calorie intake if the user's total calorie intake exceeds the target, and support means for predicting a decline in the user's motivation based on past data and providing advice. This allows the user to consistently follow the individually customized diet plan and effectively manage their health.

[1639] "Input means" refers to a device or function that allows a user to input information such as height, weight, body fat percentage, target weight, and target body fat percentage into the system.

[1640] "Transmission means" refers to a device or function that allows the terminal to transmit information entered by the user to the server.

[1641] "Generative artificial intelligence" refers to computational algorithms or models that create customized diet plans based on input information.

[1642] "Computational means" refers to the process or device that analyzes the received user information and generates a diet plan.

[1643] The "creation means" is a device or function for creating a meal plan, recipes, and a list of necessary ingredients based on the diet plan.

[1644] "Display means" refers to a device or function for visually presenting the generated menu, recipe, and ingredient list to the user.

[1645] "Uploading means" refers to a device or function that allows a user to send photos of the food they have eaten to the system.

[1646] "Analysis means" refers to a device or function that analyzes uploaded photos and estimates the contents and calories of meals.

[1647] The "correction means" is a device or function for generating a menu that corrects the course when the total calorie intake exceeds the target.

[1648] "Support tools" are devices or functions that predict a decline in a user's motivation based on past data and provide advice at the appropriate time.

[1649] The present invention is a system developed to support users' health management, which generates a customized diet plan based on information input by the user and supports continuous health management. The following describes in detail the embodiments of the present invention.

[1650] This system consists of a terminal for users to input information, a server that receives and analyzes the data, and a terminal for displaying the generated information.

[1651] Input Method

[1652] The user launches a dedicated application using a device such as a mobile phone or tablet. In the application, basic information such as height, weight, body fat percentage, target weight and target body fat percentage is entered, and this information is temporarily saved on the device. After entering the information, the user presses the "Send" button, and the entered information is sent to the server.

[1653] Transmission method

[1654] The device sends the stored user basic information to the server. This data is sent using a secure communication protocol (e.g. HTTPS).

[1655] means of calculation

[1656] The server then uses the received data to create a customized diet plan using a generative AI model that has previously learned various health data to generate the optimal plan based on the user's current health condition and goals.

[1657] generation means

[1658] The server generates daily or weekly meal plans, recipes, and ingredient lists based on the customized diet plan, a process that includes retrieving recipe and ingredient information from a database.

[1659] Display means

[1660] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, allowing the user to follow the actual meal plan.

[1661] Upload method

[1662] After consuming a meal, the user uses the device to take a photo of the meal and upload it to the system. The uploading operation is performed within the application, and the photo data is sent to the server.

[1663] Analysis means

[1664] The server analyzes the uploaded photos and estimates the food contents and calories of the food depicted in the photos using image recognition algorithms, and the results of the analysis are stored in a database.

[1665] Correction means

[1666] If the user's total calorie intake exceeds their goal, the server will use that information to regenerate a revised menu, which will then be sent back to the device and displayed.

[1667] Support means

[1668] The server analyzes past data to learn patterns of when users lose motivation, and then uses an AI model to generate advice messages to help users maintain their motivation at the right time and send them to their devices.

[1669] Specific examples

[1670] For example, consider a user who is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. The user enters this information into the device and presses the "Send" button. Based on the received information, the server uses a generative AI model to create a diet plan that includes calorie restriction and appropriate exercise. The plan includes specific menus and recipes for breakfast, lunch, and dinner, and an automatically generated list of ingredients is also included. The user uploads photos of their daily meals, and the server analyzes the photos to estimate calories. If the total daily calorie intake exceeds the target, the server adjusts the menu for the next day. The server also sends encouraging messages and specific action plans at times when the user's motivation is predicted to wane.

[1671] Example prompts to input to the generative AI model

[1672] User's current height: 170 cm

[1673] User's current weight: 75 kg

[1674] User's current body fat percentage: 25%

[1675] User target weight: 65 kg

[1676] User's target body fat percentage: 15%

[1677] Use this information to generate a customized diet plan that includes daily and weekly meal plans, recipes, and ingredient lists.

[1678] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1679] Step 1:

[1680] The user starts up the device and accesses the application. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage. The entered information is temporarily stored in the device's memory.

[1681] Input: User's height, weight, body fat percentage, target weight, target body fat percentage

[1682] Output: User information temporarily stored on the device

[1683] Specific behavior: The user enters the required information into the input form and presses the "Submit" button.

[1684] Step 2:

[1685] The terminal uses a secure protocol (e.g., HTTPS) to send the input data to the server. The terminal generates JSON-formatted data as a transmission method and sends it to the server using an HTTP POST request.

[1686] Input: User information temporarily stored on the device

[1687] Output: User information sent to the server

[1688] Specific operation: When the "Send" button is pressed, the device generates data in JSON format, issues an HTTP request, and sends it to the server.

[1689] Step 3:

[1690] The server stores the received user information in a database and uses a generative AI model to create a customized diet plan. Based on the received information, the server inputs prompts into the AI ​​model to generate the diet plan.

[1691] Input: User information received by the server (JSON format)

[1692] Output: A customized diet plan

[1693] What it does: The server stores the data in a database and provides prompts to the AI ​​model to generate a diet plan.

[1694] Step 4:

[1695] The server analyzes the generated diet plan and generates daily and weekly meal plans, recipes, and ingredient lists, which are then encoded in JSON format and sent to the device.

[1696] Enter: your customized diet plan.

[1697] Output: JSON format meal plans, recipes, and ingredient lists

[1698] Specific operation: The server generates the necessary information based on the diet plan and encodes it for transmission to the device.

[1699] Step 5:

[1700] The device analyzes the received data and displays it in a visually easy-to-understand format for the user, allowing them to check the generated menu, recipes, and ingredient lists.

[1701] Input: JSON format data sent from the server

[1702] Output: Menus, recipes, and ingredient lists displayed on the device screen

[1703] Specific behavior: The device parses the JSON formatted data and displays the information based on the user interface.

[1704] Step 6:

[1705] After consuming a meal, the user uses the device to take a photo of the meal and upload it to the system. The user then presses the "upload" button in the application to send the photo to the server.

[1706] Input: A photo of a meal taken by the user

[1707] Output: Food photos uploaded to the server

[1708] Specific actions: The user takes a photo with the device's camera and presses the "upload" button within the app.

[1709] Step 7:

[1710] The server analyzes the uploaded photos and uses image recognition algorithms to estimate the contents and calories of the meal, and the analysis results are stored in a database.

[1711] Input: User-uploaded food photos

[1712] Output: Estimated meal contents and calorie information

[1713] What it does: The server runs an image recognition algorithm to identify the type of food and its calories.

[1714] Step 8:

[1715] If the user's total calorie intake exceeds their goal, the server will use that information to revise the next day's meal plan, which will then be sent back to the device and displayed to the user.

[1716] Input: Estimated meal contents and calorie information

[1717] Output: Modified meal plan

[1718] Specific operation: The server analyzes the historical data, generates a new meal plan, and sends it to the device.

[1719] Step 9:

[1720] The server analyzes past data and learns patterns of when the user's motivation declines, then generates encouraging and advice messages at the appropriate time and sends them to the device.

[1721] Input: User's historical data

[1722] Output: Advice message

[1723] What happens: The server uses machine learning models to analyze the data, generate advice messages, and send them.

[1724] (Application example 1)

[1725] 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."

[1726] Health management requires not only providing users with personalized diet plans tailored to their individual needs, but also a wide range of support, such as purchasing the ingredients needed to actually follow the plan and maintaining motivation. However, current systems are not adequately able to suggest individual health foods to users, provide support for purchasing in physical stores, or maintain ongoing motivation. Therefore, there is a need to provide comprehensive support to help users achieve their goals.

[1727] 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.

[1728] In this invention, the server includes: an input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; a calculation means for creating a customized diet plan based on the input information using artificial intelligence; a generation means for generating a meal plan, recipes, and a list of necessary ingredients based on the diet plan; a display means for presenting the generated meal plan, recipes, and ingredient list to the user; an upload means for uploading photos of the meals the user has eaten; an analysis means for analyzing the uploaded photos and estimating the meal contents and calories; a correction means for generating a menu that corrects the user's calorie intake if the total calorie intake exceeds the target; a support means for predicting a decrease in motivation and providing timely advice; and a means for suggesting health foods sold in physical stores to the user and providing purchasing support based on the list of necessary ingredients. This allows users to receive comprehensive support for their individual health goals and effectively manage their health.

[1729] "User" refers to an individual who uses the health care system.

[1730] "Height" is the vertical distance from the bottom of the user's feet to the top of their head.

[1731] "Weight" is the total body mass of the user.

[1732] "Body fat percentage" is the percentage of fat in relation to the user's body weight.

[1733] "Goal weight" is the weight that a user wishes to achieve.

[1734] "Target body fat percentage" is the percentage of body fat that the user wishes to achieve.

[1735] "Input means" refers to a device or method that allows a user to input their physical information into the system.

[1736] "Generative AI" is an AI technology that generates customized diet plans based on user information.

[1737] A "computing means" is a device or method for carrying out the process of generating a diet plan based on user input information.

[1738] The "generation means" refers to a device or method for creating a specific meal plan, recipes, and a list of ingredients based on the diet plan generated by the calculation means.

[1739] "Display means" refers to a device or method for presenting the generated menu, recipe, and ingredient list to the user.

[1740] An "uploading means" is a device or method that allows a user to send a photo of the meal they have eaten to the system.

[1741] The "analysis means" refers to a device or method for analyzing uploaded photos and estimating the contents and calories of meals.

[1742] A "modifier" is a device or method for reformulating a diet plan if total calorie intake exceeds the target.

[1743] "Support measures" are devices and methods for predicting a decline in motivation and providing appropriate advice.

[1744] A "brick and mortar store" is a store that sells health foods in a physical location.

[1745] "Health foods" are foods marketed to support the health of users.

[1746] A "purchasing support tool" is a device or method that supports users in efficiently shopping at physical stores based on a list of necessary ingredients.

[1747] The system of the present invention is configured as follows to enhance individual health support for users.

[1748] 1. System Configuration

[1749] The system consists of a terminal for users to input information, a server that performs calculations and analysis, and a terminal that outputs and displays data. The main hardware used is a smartphone or tablet, and the software includes React Native (mobile application development), Python, MySQL, and TensorFlow.

[1750] 2. Input Method

[1751] Users launch the application using a device such as a smartphone or tablet and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server via HTTPS.

[1752] 3. Means of calculation

[1753] The server uses the received information to generate a customized diet plan using a generative AI model, applying algorithms that take into account the user's current health status and goals.

[1754] 4. Generation means

[1755] The server generates specific meal plans, recipes, and ingredient lists based on the generated diet plan, making it easier for the user to prepare and shop for specific ingredients.

[1756] 5. Display means

[1757] The generated menu, recipes, and ingredient list are sent from the server to the terminal and displayed on the user's terminal screen, allowing the user to actually carry out the plan.

[1758] 6. Upload Method

[1759] After eating, users use their device to take a photo of the meal and upload it to the system, which records their calorie intake.

[1760] 7. Analysis method

[1761] The server analyzes the uploaded photos and estimates the contents and calories of the food photographed, which are then compared to the user's calorie goal and stored in a database.

[1762] 8. Remedies

[1763] If the total calorie intake exceeds the target, the server detects this and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[1764] 9. Support methods

[1765] The server analyzes past data and learns patterns of declining motivation. Based on the results of this learning, it sends advice messages to users at appropriate times, thereby maintaining their motivation.

[1766] 10. Healthy food recommendations and purchasing support methods

[1767] In addition, the service suggests health foods sold in physical stores to users and provides purchasing support based on a list of necessary ingredients, allowing users to efficiently purchase the health foods they need in physical stores.

[1768] Specific examples

[1769] For example, if a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and target body fat percentage at 15%, the system operates as follows: When the user enters this information into their device and sends it to the server, the generative AI model creates a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and also generates a list of necessary ingredients. The user uploads photos of the meals they eat each day, and the server analyzes them to estimate calories. If the total calories consumed in a day exceed the target, the server adjusts the menu for the next day to balance calorie intake.

[1770] "Generate a diet plan for a health management system. The user is 170 cm tall, 75 kg, and has a body fat percentage of 25%. Their target weight is 65 kg and their target body fat percentage is 15%."

[1771] In this way, the system provides users with consistent and personalized diet and health management support.

[1772] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1773] Step 1:

[1774] Using a device, a user launches the application and enters basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server via HTTPS. The entered data is related to the user's health status and goals.

[1775] Step 2:

[1776] The server then takes the received user information and generates a customized diet plan using a generative AI model. Specifically, it applies an algorithm that takes into account the user's current health status and goals. It receives the user's health data as input and generates a diet plan that includes calorie restriction and exercise as output.

[1777] Step 3:

[1778] The server generates specific meal plans, recipes, and ingredient lists based on the generated diet plan, including specific menus that are easy for users to follow. It receives the generated diet plan as input and generates specific meal plans and ingredient lists as output.

[1779] Step 4:

[1780] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the user's device screen. The user checks this output and works on the actual meal plan. In this step, data transfer and UI updates are performed to display the data generated by the server on the user's device.

[1781] Step 5:

[1782] After consuming a meal, the user takes a photo of the meal using their device and uploads it to the system. This photo becomes input data for the next analysis step. The user's actions are to take a photo of the meal and upload it.

[1783] Step 6:

[1784] The server analyzes the uploaded photos and estimates the contents and calories of the food in them. Specifically, it uses TensorFlow and SSD models to analyze the images and recognize the food contents. It receives food images as input and generates food contents and calorie value data as output.

[1785] Step 7:

[1786] The calorie intake obtained as a result of the analysis is compared with the user's calorie restriction target and stored in a database. If the total calorie intake exceeds the target, the server detects this situation and regenerates a menu with corrected course. By adjusting the menu for the next day, the user can prevent over-intake. The system receives the analyzed calorie value data as input and generates a revised diet plan as output.

[1787] Step 8:

[1788] The server analyzes past data and learns patterns of declining motivation. Based on this learning, it sends advice messages to users at appropriate times. This helps maintain the user's motivation and support continued health management. It receives the user's behavioral data as input and generates advice messages as output.

[1789] Step 9:

[1790] The system suggests health foods sold in physical stores to users and provides purchasing support based on the required ingredient list. Users can use this information to shop efficiently in physical stores. The server provides optimal store information and product suggestions based on the user's ingredient list. It receives the ingredient list as input and generates purchasing support information for the store as output.

[1791] Through the above processing steps, the present invention becomes a system that provides users with consistently personalized diet and health management support.

[1792] 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.

[1793] The present invention is a health management system that allows a user to input their height, weight, body fat percentage, target weight, and target body fat percentage and provides a customized diet plan based on the input. Furthermore, by combining the present invention with an emotion engine that recognizes the user's emotions, the present invention more effectively supports maintaining motivation. Specific embodiments for implementing the present invention are described below.

[1794] 1. System Configuration

[1795] This system consists of a terminal for users to input information, a server that performs calculations and analysis, an emotion engine, and a terminal that outputs and displays data.

[1796] 2. Input Method

[1797] Users use a device such as a mobile phone or tablet to launch the application and enter basic information such as height, weight, body fat percentage, target weight and target body fat percentage, etc. This information is temporarily stored on the device and then sent to the server.

[1798] 3. Means of calculation

[1799] Based on the information received, the server uses generative AI to generate a customized diet plan, applying algorithms that take into account the user's current health status and goals.

[1800] 4. Generation means

[1801] Based on the diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, allowing users to efficiently prepare and shop for ingredients.

[1802] 5. Display means

[1803] The generated menu, recipes, and ingredient list are sent from the server to the device and displayed on the device screen, where the user can review and work on the actual meal plan.

[1804] 6. Upload Method

[1805] After eating a meal, users can use their device to take a photo of the meal and upload it to the system, allowing the system to record their calorie intake.

[1806] 7. Analysis method

[1807] The server analyzes the uploaded photos and estimates the contents and calories of the food depicted in them, which are then compared to the user's calorie goal and stored in a database.

[1808] 8. Remedies

[1809] The server detects when the user's total calorie intake exceeds their goal and regenerates a menu that corrects the calorie intake, allowing the user to avoid overeating and manage their diet appropriately to achieve their goal.

[1810] 9. Emotion Engine

[1811] The emotion engine has the ability to analyze the user's facial expressions and voice to estimate their emotional state. The emotion engine is installed on the device and collects data from the user's daily actions and speech.

[1812] Examples:

[1813] The user captures their facial expression through the device's camera and inputs voice information into the device.

[1814] The emotion engine analyzes the collected data and estimates the user's emotional state, which is then sent to the server in real time.

[1815] 10. Support methods

[1816] The server receives data from the emotion engine and predicts a decline in motivation based on the user's emotional state. Based on the predicted information, the generative AI generates timely advice messages and sends them to the device.

[1817] Examples:

[1818] The server predicts when a user shows signs of stress or fatigue and sends them messages of encouragement and tips on how to relax.

[1819] For example, a message might be sent saying, "You've worked hard today. Let's take a break and refresh yourself."

[1820] Specific examples

[1821] Let's say a user is 170 cm tall, weighs 75 kg, and has a body fat percentage of 25%, and sets their target weight at 65 kg and body fat percentage at 15%. When the user enters this information into the device and submits it, the server uses generative AI to create a diet plan that includes calorie restriction and appropriate exercise. The created plan includes specific menus and recipes for breakfast, lunch, and dinner, and a list of necessary ingredients is also automatically generated.

[1822] Users upload photos of the meals they eat each day, and the server analyzes them to estimate the calories. If the total calories consumed in a day exceeds the target, the server adjusts the menu for the next day to balance the calorie intake. In addition, when the server predicts that the user's motivation will decrease, it sends encouraging messages and specific action plans.

[1823] The emotion engine analyzes the user's facial expressions and voice and transmits their emotional state in real time to the server, which uses this information to provide appropriate advice when the user is feeling stressed or tired.

[1824] As described above, the system of the present invention provides users with consistent and personalized diet and health management support.

[1825] The processing flow will be explained below.

[1826] Step 1:

[1827] A user starts the application using a mobile device or tablet. The user enters basic information such as height, weight, body fat percentage, target weight, and target body fat percentage into an input form.

[1828] Step 2:

[1829] The terminal displays the entered information on the screen in real time and temporarily stores it after all the information has been entered.

[1830] Step 3:

[1831] The terminal transmits the temporarily stored user information data to the server.

[1832] Step 4:

[1833] The server stores the received user information data in a database.

[1834] Step 5:

[1835] The server retrieves the stored user information from the database and passes it to the generative AI engine.

[1836] Step 6:

[1837] The generative AI engine creates a customized diet plan based on the user's height, weight, body fat percentage, target weight and target body fat percentage.

[1838] Step 7:

[1839] The server generates a diet plan based on the user's preferences and lifestyle, along with meal plans, recipes, and a list of ingredients needed.

[1840] Step 8:

[1841] The server sends the generated menu, recipes, and ingredient list to the terminal.

[1842] Step 9:

[1843] The device displays meal plans, recipes, and ingredient lists to the user, who can then confirm the information and follow the meal plan.

[1844] Step 10:

[1845] After a user consumes a meal, they use their device to take a photo of the meal and upload it to the system.

[1846] Step 11:

[1847] The device sends photo data of the meal to the server.

[1848] Step 12:

[1849] The server analyzes the received photo data and estimates the contents and calories of the food depicted.

[1850] Step 13:

[1851] The server compares the estimated calories with the user's calorie restriction goal and stores the results in a database.

[1852] Step 14:

[1853] The server monitors the total calorie intake stored in the database, and if the target is exceeded, it instructs the generative AI engine to regenerate a revised menu.

[1854] Step 15:

[1855] The server sends the revised menu to the terminal and notifies the user.

[1856] Step 16:

[1857] The user can check the revised menu on the device and adjust their meal plan for the next day.

[1858] Step 17:

[1859] The emotion engine captures the user's facial expressions and voice through the device, either by speaking into the sensor or by taking a photo.

[1860] Step 18:

[1861] Based on the data captured by the emotion engine, the user's emotional state is analyzed and the emotional information is sent to the server.

[1862] Step 19:

[1863] The server receives the emotion information sent from the emotion engine and predicts a decrease in the user's motivation.

[1864] Step 20:

[1865] Based on the predicted decline in motivation, the server instructs the generative AI engine to create timely advice messages.

[1866] Step 21:

[1867] The server sends the generated advice message to the terminal to notify the user.

[1868] Step 22:

[1869] The user checks the advice message on the device and follows the advice provided.

[1870] These steps help users manage their diet and health in an efficient and personalized way, and by taking into account the user's emotional state, the system can more effectively maintain motivation.

[1871] Example 2

[1872] 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."

[1873] Conventional health management systems have issues in that they do not adequately provide users with personalized diet plans, and do not respond appropriately to users' emotional state or a decline in motivation. Furthermore, they lack a mechanism for accurately analyzing the calories of the food a user consumes and providing appropriate meal plans in real time based on that information. Furthermore, there is a need for a system that can provide appropriate advice when motivation declines, allowing users to continue effective health management over the long term.

[1874] 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.

[1875] In this invention, the server includes: input means for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; calculation means for creating a customized management plan based on the input information using artificial intelligence; generation means for generating a meal plan, recipes, and ingredient list based on the management plan; display means for presenting the generated meal plan, recipes, and ingredient list to the user; transfer means for uploading images of the meals the user has eaten; analysis means for analyzing the uploaded images and estimating the meal contents and calories; adjustment means for generating a revised meal plan based on the estimated calories if the total calorie intake exceeds the target; and support means for estimating the user's emotional state from facial expressions and voice, predicting a decrease in motivation based on the estimated calories, and providing timely advice. This enables the user to receive consistent, personalized diet and health management support, as well as timely advice based on the user's emotional state, to support long-term health management.

[1876] The "input means" is a means for a user to input data such as height, weight, body fat percentage, target weight, and target body fat percentage through a terminal.

[1877] "Calculation means" refers to a means for creating a customized management plan using generative artificial intelligence based on data input by a user.

[1878] The "generation means" is a means for generating a specific meal menu, recipes, and a list of necessary ingredients based on the management plan created by the calculation means.

[1879] The "display means" is a means for visually presenting to the user the menu, recipe, and ingredient list generated by the generation means.

[1880] The "transfer means" is a means for a user to upload images of the food they have eaten to the system via their terminal.

[1881] The "analysis means" is a means for analyzing the image of the meal uploaded via the transfer means and estimating the contents and calories of the meal.

[1882] The "adjustment means" is a means for generating a revised menu based on the calories estimated by the analysis means when the total calorie intake exceeds the target.

[1883] "Support measures" are means for estimating the user's emotional state from their facial expressions and voice, predicting a decline in motivation based on that information, and providing timely advice.

[1884] The present invention is a health management system that provides a customized diet plan based on the user's height, weight, body fat percentage, target weight, and target body fat percentage. Furthermore, the system has a function to recognize the user's emotions and help maintain motivation.

[1885] This system consists of eight main components: input means, calculation means, generation means, display means, transfer means, analysis means, adjustment means, and support means.

[1886] First, the user launches a dedicated app on their mobile phone or tablet. Then, the user enters information such as their height, weight, body fat percentage, target weight, and target body fat percentage. This information is temporarily stored on the device and then sent to a server via the Internet.

[1887] The server uses the received information to generate a customized diet plan for the user using a generative AI model (e.g., GPT-3). The following prompt is input to the generative AI model:

[1888] "Create a diet plan based on my height of 170 cm, weight of 75 kg, and body fat percentage of 25%, and my target weight of 65 kg and target body fat percentage of 15%."

[1889] The generated diet plan includes a daily or weekly meal plan, recipes, and a list of ingredients. The server sends this data to the device, which displays it to the user. The user can then refer to the displayed menu and recipes to prepare the actual meal.

[1890] After consuming a meal, the user takes a photo of the meal using their device and uploads it to the system. This data is sent to a server, which uses image analysis techniques (e.g., OpenCV or TensorFlow) to estimate the meal contents and calories. The estimated calorie information is compared with the user's calorie restriction goal and stored in a database.

[1891] If the total calorie intake for the day exceeds the target, the server will regenerate a menu with corrected calories and adjust the diet plan for the next day. This process helps users avoid overeating and enables effective health management.

[1892] The system is also equipped with an emotion engine that analyzes the user's facial expressions and voice to estimate their emotional state. The user inputs their facial expressions and voice using the device's camera and microphone, and the data is sent to the server in real time. The server predicts a decline in the user's motivation based on the emotional data, and uses a generative AI model to create an appropriate advice message and send it to the device. For example, a timely message such as, "You seem a little tired today. I recommend taking a short walk as a way to relax" is provided.

[1893] As described above, the system of the present invention can provide users with consistent and personalized diet and health management support, as well as timely advice based on the user's emotional state, to support long-term health management.

[1894] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1895] Step 1:

[1896] The user launches the dedicated app and inputs their height, weight, body fat percentage, target weight, and target body fat percentage. The input information is temporarily stored on the device.

[1897] Input: User's height, weight, body fat percentage, goal weight, and goal body fat percentage

[1898] Output: Temporarily stored user information data

[1899] Step 2:

[1900] The device sends the saved user information to the server using an HTTP request.

[1901] Input: Temporarily stored user information data

[1902] Output: User information data sent to the server

[1903] Step 3:

[1904] The server creates and inputs a prompt sentence into the generative AI model based on the received user information.

[1905] Input: Prompt and user information: "Create a diet plan based on a height of 170 cm, weight of 75 kg, body fat percentage of 25%, target weight of 65 kg, and target body fat percentage of 15%."

[1906] Output: A customized diet plan from the generative AI

[1907] Step 4:

[1908] Based on the generated diet plan, the server generates daily and weekly meal plans, recipes, and ingredient lists, taking into account the user's calorie restrictions and nutritional balance.

[1909] Input: Generated diet plan

[1910] Output: Specific meal plans, recipes, and ingredient lists

[1911] Step 5:

[1912] The server transmits the generated menu, recipes, and ingredient list to the terminal.

[1913] Input: Specific meal plans, recipes, and ingredient lists

[1914] Output: Data sent to the terminal

[1915] Step 6:

[1916] The device displays the received data on a user interface, allowing the user to confirm the displayed information and follow the actual meal plan.

[1917] Input: Data sent to the terminal

[1918] Output: Meal plan displayed in the user interface

[1919] Step 7:

[1920] After eating a meal, the user takes a photo of the meal using the device's camera and uploads it to the system.

[1921] Input: Food photo

[1922] Output: Food image data sent to the server

[1923] Step 8:

[1924] The server uses image analysis software, such as OpenCV and TensorFlow, to analyze the uploaded food image data and recognize and estimate the food contents and calories.

[1925] Input: Food image data

[1926] Output: Estimated meal contents and calorie information

[1927] Step 9:

[1928] The server compares the estimated calorie information with the user's calorie goal, and if the total calorie intake exceeds the goal, the server regenerates a modified menu.

[1929] Input: Estimated meal content and calorie information, user's calorie restriction goal

[1930] Output: Modified menu

[1931] Step 10:

[1932] Users use the device's camera and microphone to input their facial expressions and voice, and the emotion engine analyzes this data to estimate the user's emotional state.

[1933] Input: User's facial expressions and voice data

[1934] Output: Estimated emotional state

[1935] Step 11:

[1936] The server predicts a decline in the user's motivation based on the emotional data received from the emotion engine, and uses a generative AI model to create an appropriate advice message and send it to the device.

[1937] Input: Estimated emotional state

[1938] Output: Advice message

[1939] Through these steps, the system can provide users with consistent, personalized health management and motivation.

[1940] (Application example 2)

[1941] 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."

[1942] While conventional health management systems can provide customized diet plans tailored to individual users' health goals, they lack the support to appropriately respond to users' emotional changes and maintain their motivation. Furthermore, they have not implemented health management using virtual reality technology, nor have they suggested appropriate health foods and nutritional supplements in virtual stores to improve the user experience. As a result, there has been a lack of support for users to continue managing their health.

[1943] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1944] In this invention, the server includes: an input device for inputting the user's height, weight, body fat percentage, target weight, and target body fat percentage; a calculation device using artificial intelligence to create a customized diet plan based on the input information; a generation device for generating a meal plan, recipes, and a list of ingredients based on the diet plan; a display device for presenting the generated meal plan, recipes, and ingredient list to the user; an upload device for uploading photos of the meals the user has eaten; an analysis device for analyzing the uploaded photos and estimating the meal contents and calories; a correction device for generating a menu that corrects the user's calorie intake if the total calorie intake exceeds the target; a support device for predicting a decrease in motivation and providing timely advice; a presentation device for suggesting health foods and nutritional supplements within the virtual store; an emotion analysis device for analyzing the user's emotional state using an emotion engine and providing messages to maintain motivation based on the analysis results; and a virtual interface device for supporting the user's activities within the virtual reality environment of the virtual store. This not only provides personalized support that responds to the user's emotional changes, but also enhances the shopping experience within the virtual store and enables continuous health management support.

[1945] The "input means" refers to a means for a user to input his / her height, weight, body fat percentage, target weight, and target body fat percentage through a terminal.

[1946] The "calculation means" is a means for creating a customized diet plan using artificial intelligence based on input information.

[1947] The "generation means" is a means for generating a meal plan, recipes, and a list of ingredients required based on a customized diet plan.

[1948] The "display means" is a means for presenting the generated menu, recipe, and ingredient list to the user.

[1949] The "uploading means" is a means for a user to upload a photo of the meal they have eaten to the system.

[1950] The "analysis means" is a means for analyzing uploaded photos and estimating the contents and calories of the food photographed.

[1951] The "correction means" is a means for generating a menu that corrects the user's calorie intake if the user's total calorie intake exceeds the target.

[1952] "Support measures" are means for predicting declines in motivation and providing timely advice.

[1953] The "presentation means" refers to a means for suggesting health foods and nutritional supplements to users within the virtual store.

[1954] The "emotion analysis means" is a means for analyzing the user's emotional state using an emotion engine and providing a message to maintain motivation based on the analysis results.

[1955] "Virtual interface means" refers to means for supporting user actions within the virtual reality environment of the virtual store.

[1956] This invention aims to develop a system for providing customized diet plans using a virtual store, with the aim of helping users manage their health. The system analyzes the user's health information and emotional state, and provides personalized meal plans, suggested products, and advice to maintain motivation.

[1957] 1. Input Method

[1958] First, the user inputs their height, weight, body fat percentage, target weight, and target body fat percentage using a device such as a smartphone, tablet, or head-mounted display (HMD). This information is temporarily stored on the device.

[1959] 2. Means of calculation

[1960] The information entered by the user is sent to a cloud server, which uses a generative AI model (e.g., OpenAI's GPT-4) to generate a customized diet plan based on the information entered. This AI model is pre-trained on a large health management dataset and has highly accurate algorithms to create the optimal plan for each user.

[1961] 3. Generation means

[1962] Based on the generated diet plan, specific daily meal plans, recipes, and ingredient lists are created, allowing users to efficiently prepare meals and shop daily.

[1963] 4. Display means

[1964] The generated menu, recipes, and ingredient list are sent from the cloud server to the user's device and displayed on the device screen, allowing the user to check and act in accordance with the meal plan.

[1965] 5. Uploading Method

[1966] Users take photos of the food they have eaten using their device camera and upload them to the system, where the data is then sent to a cloud server and stored in a database.

[1967] 6. Analysis method

[1968] The cloud server analyzes the uploaded photos and estimates the contents and calories of the meal. The technology used is an image analysis algorithm (e.g., Amazon Rekognition or Google Cloud Vision). This allows for a highly accurate understanding of the user's calorie intake.

[1969] 7. Remedies

[1970] If the total calorie intake exceeds the target, the cloud server will adjust the menu for the next day to maintain a balanced diet, helping users to comfortably approach their target weight.

[1971] 8. Presentation means

[1972] Health foods and nutritional supplements that are useful for health management are presented in a virtual store. The virtual store is constructed using, for example, a head-mounted display (HMD). Users can select and purchase products in the virtual space.

[1973] 9. Emotion analysis method

[1974] To analyze the user's emotional state, facial expression and voice data are collected using the device's camera and microphone. An emotion engine (e.g., Microsoft Azure Cognitive Services) analyzes this data and estimates the user's emotional state. The analysis results are sent to a cloud server and stored in a database.

[1975] 10. Support methods

[1976] The cloud server uses emotion analysis data to provide motivational advice. For example, if a user feels fatigued or stressed, the AI ​​will generate a message such as "Take a short break to refresh yourself" and display it on the device.

[1977] Specific examples

[1978] Specific user scenarios:

[1979] The user inputs information such as height 170 cm, weight 75 kg, body fat percentage 25%, target weight 65 kg, and target body fat percentage 15%. Based on this information, the generative AI model generates a diet plan including appropriate calorie restrictions and exercise plans. Specific menus and recipes for breakfast, lunch, and dinner are presented, and a list of ingredients is automatically generated.

[1980] Example prompt sentence:

[1981] After entering basic user information, the generative AI model receives prompts like this:

[1982] User Basic Information:

[1983] Height: 170 cm

[1984] Weight: 75 kg

[1985] Body fat percentage: 25%

[1986] Target weight: 65 kg

[1987] Target body fat percentage: 15%

[1988] Based on this data, your application should generate a customized diet plan for the user, recommend suitable health foods and supplements in a virtual store, and analyze the user's facial expressions and voice to estimate their emotional state and display advice messages to keep them motivated as needed.

[1989] This example demonstrates how the invention can be put into practice, allowing users to receive consistent, personalized diet and health management support.

[1990] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1991] Step 1:

[1992] The user uses the device to input their height, weight, body fat percentage, target weight, and target body fat percentage. This input information is temporarily stored on the device and later sent to the cloud server. The server receives this information and stores it as data for the next process.

[1993] input:

[1994] User's health information (height, weight, body fat percentage, target weight, target body fat percentage)

[1995] output:

[1996] User health information data stored on a cloud server

[1997] Specific behavior:

[1998] The user enters the necessary information using a smartphone, tablet, or HMD and presses the send button.

[1999] Step 2:

[2000] The cloud server generates a customized diet plan using a generative AI model based on the input health information. The server inputs prompts into the generative AI model to generate the diet plan.

[2001] input:

[2002] Stored user health information data

[2003] output:

[2004] Customized diet plans

[2005] Specific behavior:

[2006] The cloud server inputs data into a generative AI model (e.g., GPT-4) to generate a diet plan, which is then stored on the server.

[2007] Step 3:

[2008] Based on the generated diet plan, the cloud server creates a specific meal plan, recipes, and a list of ingredients, which are then sent to the user's device.

[2009] input:

[2010] Customized diet plans

[2011] output:

[2012] Meal plans, recipes, and ingredient lists

[2013] Specific behavior:

[2014] The cloud server references the recipe database and ingredient database to generate a specific meal plan, which is then sent to the device and displayed for the user to review.

[2015] Step 4:

[2016] After consuming a meal, users use their device to take a photo of the meal and upload it to the system, which then sends the photo to a cloud server for further processing.

[2017] input:

[2018] Food photos taken by users

[2019] output:

[2020] Meal photos uploaded to a cloud server

[2021] Specific behavior:

[2022] Users take a photo of their meal with their device's camera and press the upload button to send it to the server, where it is stored.

[2023] Step 5:

[2024] The cloud server analyzes the uploaded photos and estimates the contents and calories of the meal using image analysis algorithms.

[2025] input:

[2026] Uploaded food photos

[2027] output:

[2028] Analysis data of meal contents and estimated calories

[2029] Specific behavior:

[2030] The cloud server runs an image analysis algorithm (e.g., Amazon Rekognition) to analyze the type and portion size of food from the photo and calculate calories.

[2031] Step 6:

[2032] The server records the analyzed calorie data as the total calories. If the total calorie intake exceeds the target, the menu for the next day is revised. This revised data is then sent back to the user's device.

[2033] input:

[2034] Analyzed food content data and estimated calories

[2035] output:

[2036] Revised meal plans, recipes, and ingredient lists

[2037] Specific behavior:

[2038] The cloud server compares the user's calorie intake history with their goals and adjusts the meal plan for the next day if necessary, then sends the revised plan to the user's device and displays it again on the device screen.

[2039] Step 7:

[2040] Health foods and nutritional supplements are suggested to users in the virtual store, and they can purchase these products using their devices.

[2041] input:

[2042] Customized diet plans and virtual store data

[2043] output:

[2044] Suggested health foods and nutritional supplements

[2045] Specific behavior:

[2046] Users wear a head-mounted display and access a virtual store, select food or supplements using the virtual interface, and press the purchase button.

[2047] Step 8:

[2048] The user's facial expressions and voice are collected through the device's camera and microphone and sent to a cloud server, where an emotion engine analyzes them to estimate the user's emotional state.

[2049] input:

[2050] Your facial and voice data

[2051] output:

[2052] Inferred emotional state

[2053] Specific behavior:

[2054] The device collects data using a camera and microphone, and the cloud server analyzes the data using an emotion engine (e.g., Microsoft Azure Cognitive Services) to estimate emotions.

[2055] Step 9:

[2056] The cloud server uses a generative AI model to generate advice messages to maintain motivation based on the user's emotional state and sends them to the user's device.

[2057] input:

[2058] Inferred emotional state

[2059] output:

[2060] Advice Message

[2061] Specific behavior:

[2062] The cloud server uses a generative AI model to create a message based on the emotion analysis data, and the generated message is sent to the user's device and displayed on the screen.

[2063] Above is a detailed flow of each processing step, which allows users to consistently receive personalized diet plans, healthy food recommendations, and advice based on their emotional state.

[2064] 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.

[2065] 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.

[2066] 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 robot 414.

[2067] 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.

[2068] 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.

[2069] 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.

[2070] 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).

[2071] 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.

[2072] 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."

[2073] 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.

[2074] 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).

[2075] 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.

[2076] 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.

[2077] 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.

[2078] 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.

[2079] 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.

[2080] 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.

[2081] 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.

[2082] 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.

[2083] 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 ...

Claims

1. input means for inputting the user's height, weight, body fat percentage, target weight and target body fat percentage; a computing means for generating a customized diet plan based on the input information using a generative artificial intelligence; A generating means for generating a meal plan, recipes, and a list of ingredients required based on the diet plan; a display means for presenting the generated menu, recipe, and ingredient list to the user; an uploading means for a user to upload photos of meals consumed; An analytical method that analyzes uploaded photos and estimates the contents and calories of meals; a correction means for generating a menu that is corrected when the total calorie intake exceeds the target; Support measures to predict declines in motivation and provide timely advice; A system including:

2. The system of claim 1 , wherein the means for generating the meal plan, recipe, and list of required ingredients further includes means for providing an ingredient delivery service when a premium plan is selected.

3. The system according to claim 1 , wherein the support means further comprises means for learning a pattern of decreased motivation from past data and transmitting an advice message at a predicted timing.

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