System

Abstract

To provide a system.SOLUTION: The system includes: means for collecting data on body sculpting of a user; means for analyzing the data by using a generative AI model and specifying a failure pattern of the body sculpting of the user; means for generating information of at least one of a training plan and meal guidance personalized on the basis of the specified failure pattern and proposing the information; and means for acquiring the emotion state of the user by using an emotion engine and providing a feedback based on the acquired emotion state.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] Conventional diet support services have had the challenge of making it difficult to provide personalized support tailored to the individual needs of users, and also to maintain users' motivation to continue their diet. [Means for solving the problem]

[0005] The diet support system of this invention combines VR technology and generative AI to provide training and dietary advice tailored to each individual user. It also sells special nutritional foods and training equipment to help users maintain their motivation to continue their diet. Furthermore, the generative AI analyzes the user's past failure data to support sustained diet success. These methods solve the problems of conventional diet support services. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The diet support system of the present invention combines VR technology and generative AI to provide users with personalized training and dietary advice. Specifically, the user wears a VR device at home and exercises according to the training program provided by the generative AI. The generative AI generates an optimal training menu based on information such as the user's body type, physical strength, and health condition. Similarly, the generative AI also provides dietary advice, taking into account the user's food preferences, health condition, and diet goals.

[0029] "Example 2"

[0030] The diet support system of the present invention also sells special nutritional foods and training equipment. These products work in conjunction with the training and dietary advice provided by the generation AI to more effectively support the user's diet. For example, based on the dietary advice provided by the generation AI, the system can recommend special nutritional foods and encourage their purchase. Similarly, the system can recommend training equipment optimized for the training program provided by the generation AI and encourage its purchase.

[0031] "Example 3"

[0032] The diet support system of this invention analyzes the user's past failure data and supports sustained diet success. Specifically, the generative AI analyzes the user's past diet history and failure patterns, and provides personalized training and dietary advice based on that. This prevents the user from repeating the same failures and supports sustained diet success.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The user puts on the VR device at home.

[0036] Step 2: The AI ​​generates the optimal training menu based on the user's body type, physical strength, health condition, and other information.

[0037] Step 3: The user exercises according to the training program provided by the generated AI.

[0038] Step 4: The generative AI provides dietary advice taking into account the user's food preferences, health status, diet goals, etc.

[0039] "Example 2"

[0040] Step 1: Sell special nutritional foods and training equipment in conjunction with training and dietary advice provided by generative AI.

[0041] Step 2: Based on the dietary advice provided by the generation AI, recommend special nutritional foods and encourage their purchase.

[0042] Step 3: Recommend training equipment optimized for the training program provided by the generative AI and encourage its purchase.

[0043] "Example 3"

[0044] Step 1: The generative AI analyzes the user's past diet history and failure patterns.

[0045] Step 2: Generative AI provides personalized training and dietary advice based on the analysis results.

[0046] Step 3: This prevents users from repeating the same setbacks and supports sustained diet success.

[0047] Example 1

[0048] Next, a description will be given of Example 1 of Form 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."

[0049] Conventional diet support systems provide uniform training and dietary advice to users, without sufficient personalization based on each user's body type, physical strength, health condition, food preferences, and diet goals. Furthermore, there is a lack of ongoing support that takes into account users' past failures, resulting in a low success rate for dieting. Furthermore, sales of special nutritional foods and training equipment are not integrated into the system, which means users have to go through the trouble of purchasing the necessary items separately.

[0050] 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.

[0051] In this invention, the server includes a means for using VR technology, a means for providing personalized training and dietary advice using generation AI, a means for generating an optimal training menu based on the user's body type information, physical strength information, and health status, a means for generating an optimal meal plan based on the user's food preferences, health status, and diet goals, and a means for selling special nutritional foods and training equipment. This makes it possible to provide individually optimized training and dietary advice to users and provide continuous support taking into account past failure data. Furthermore, by providing the necessary nutritional foods and training equipment all in one place, user convenience can be improved.

[0052] "VR technology" is a technology that uses virtual reality to provide users with an immersive experience.

[0053] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate optimal training and meal plans for users.

[0054] "Personalized training" means providing a training menu that is individually optimized based on the user's body type information, physical strength information, and health condition.

[0055] "Personalized dietary guidance" refers to providing a meal plan that is individually optimized based on the user's food preferences, health status, and diet goals.

[0056] "Body type information" refers to physical data such as the user's height, weight, and body fat percentage.

[0057] "Physical fitness information" is physical data such as the user's exercise experience and current exercise habits.

[0058] "Health status" refers to data related to the user's health, such as allergies and chronic illnesses.

[0059] "Dietary preferences" is data about the user's food preferences, such as favorite and least favorite ingredients.

[0060] A "diet goal" is a goal that a user wants to achieve, such as losing weight or gaining muscle.

[0061] "Specialty nutritional foods" are foods specifically designed to support diet and training.

[0062] "Training equipment" refers to equipment or devices that a user uses to train.

[0063] "Past failure data" is data relating to the user's past experiences of failure in dieting or training.

[0064] "Continuous support" means providing long-term support to help users continue their diet and training.

[0065] MODE FOR CARRYING OUT THE INVENTION

[0066] This invention is a diet support system that combines VR technology and generative AI to provide users with personalized training and dietary advice. Specific embodiments of this system are described below.

[0067] System configuration

[0068] This system consists of a server, a terminal (VR device or smartphone), and a user. The server generates training menus and meal plans using a generative AI model and sends them to the terminal. The terminal sends information entered by the user to the server and displays the training menus and meal plans received from the server to the user.

[0069] Hardware and software used

[0070] The server is a high-performance computer with software installed to run a generative AI model (e.g., OpenAI's GPT-4®). The terminal is a VR device or smartphone that provides an interface for users to input information and display training menus and meal plans.

[0071] Data processing and calculation

[0072] The server receives data sent by the user, such as body type information, physical strength information, health condition, food preferences, and diet goals, and uses a generative AI model to generate optimal training menus and meal plans. The generated menus and plans are sent to the device and displayed to the user.

[0073] Specific examples

[0074] Example 1: Generating a training menu

[0075] The user inputs information about their body type (height 170cm, weight 70kg, body fat percentage 20%) and physical fitness (no prior exercise experience, current exercise habit is walking once a week). Based on this information, the server uses a generative AI model to generate a training menu suited to the user. For example, the following prompt sentence is input to the generative AI model:

[0076] Prompt statement:

[0077] "Please create a training menu suitable for a 30-year-old man with a height of 170 cm, weight of 70 kg, body fat percentage of 20%, and no exercise experience."

[0078] Example 2: Generating a meal plan

[0079] The user inputs their food preferences (favorite ingredients are chicken and broccoli, least favorite ingredients are fish), health condition (no allergies, no chronic illnesses), and diet goal (weight loss). Based on this information, the server uses a generative AI model to generate a meal plan suitable for the user. For example, the following prompt sentence can be input into the generative AI model:

[0080] Prompt statement:

[0081] "Create a one-week meal plan suitable for a 30-year-old man whose favorite foods are chicken and broccoli, whose least favorite foods are fish, who has no allergies, and who is trying to lose weight."

[0082] In this way, the server, terminal, and user work together to create a system that provides personalized diet support.

[0083] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0084] Step 1:

[0085] Using a terminal (VR device or smartphone), the user inputs their body type information (height, weight, body fat percentage), physical fitness information (exercise experience, current exercise habits), and health status (allergies, chronic illnesses). This information is entered through the terminal's interface and sent to the server. The input data is stored in a database as the user's individual information.

[0086] Step 2:

[0087] The device sends the entered user information to the server. The server stores the received information in a database and generates prompts to input into the generative AI model. For example, based on the user's body type and physical strength information, it generates a prompt such as, "Please create a training menu suitable for a 30-year-old man with a height of 170 cm, a weight of 70 kg, a body fat percentage of 20%, and no exercise experience."

[0088] Step 3:

[0089] The server inputs the generated prompt sentences into the generative AI model to generate an optimal training menu for the user. The generative AI model analyzes the data based on the input prompt sentences and outputs a training menu. The output training menu is saved on the server.

[0090] Step 4:

[0091] The server sends the generated training menu to the device. The device displays the received training menu to the user. The user performs exercises according to the displayed training menu. The device tracks the user's movements in real time and provides feedback on correct form and movements.

[0092] Step 5:

[0093] Using a terminal, users input their food preferences (favorite and least favorite foods), health status (allergies, chronic illnesses), and diet goals (weight loss, muscle building). This information is entered through the terminal's interface and sent to the server. The input data is stored in a database as the user's individual information.

[0094] Step 6:

[0095] The server generates a meal plan using a generative AI model based on the stored dietary information. For example, based on the user's dietary preferences and health status, it generates a prompt such as, "Please create a one-week meal plan suitable for a 30-year-old man whose favorite ingredients are chicken and broccoli, whose least favorite ingredients are fish, who has no allergies, and who is trying to lose weight."

[0096] Step 7:

[0097] The server inputs the generated prompts into a generative AI model to generate an optimal meal plan for the user. The generative AI model analyzes the data based on the input prompts and outputs a meal plan. The output meal plan is saved on the server.

[0098] Step 8:

[0099] The server sends the generated meal plan to the device. The device displays the received meal plan to the user. The user eats according to the displayed meal plan. The device tracks whether the user is adhering to the meal plan and provides reminders and advice as needed.

[0100] In this way, the server, terminal, and user work together to create a system that provides personalized diet support.

[0101] (Application example 1)

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

[0103] Conventional diet support systems often provide uniform training and dietary advice to users, lacking personalized support that fully considers each user's body type, physical strength, health condition, food preferences, and diet goals. Furthermore, dietary advice requires users to source and cook ingredients themselves, which is time-consuming and makes it difficult to achieve sustained diet success. Furthermore, the lack of sustained support utilizing data on users' past failures also contributes to the low success rate of dieting.

[0104] 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.

[0105] In this invention, the server includes means for using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, and means for proposing optimal meal menus based on information such as the user's body type, physical strength, health condition, food preferences, and diet goals, and ordering meals through a food delivery service. This allows users to be provided with individually optimized training and dietary advice, and further reduces the hassle of using the food delivery service, enabling them to achieve sustainable diet success.

[0106] "VR technology" is a technology that provides users with virtual reality, allowing them to experience a virtual environment that is different from the real world through their eyes and ears.

[0107] "Generative AI" is a technology that uses artificial intelligence to analyze user data and generate individually optimized training and dietary advice.

[0108] "Personalized training" means providing the optimal training menu based on individual information such as the user's body type, physical strength, and health condition.

[0109] "Dietary guidance" means proposing an appropriate meal menu taking into consideration the user's food preferences, health condition, diet goals, etc.

[0110] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health, and are nutritionally balanced.

[0111] "Training equipment" refers to equipment for exercise and fitness that supports users in improving their physical strength and muscle training.

[0112] A "food delivery service" is a service that delivers meals ordered by users to their homes, saving users time and effort.

[0113] "User's body type" refers to the shape and size of the user's body, which serves as the basis for creating diet and training plans.

[0114] "Physical strength" refers to the user's physical endurance and muscle strength, and is an important factor when deciding on a training menu.

[0115] "Health status" refers to the user's current health condition, which should be taken into consideration when providing training and dietary advice.

[0116] "Dietary preferences" refers to the foods and eating style that a user prefers, and is important information when providing personalized dietary advice.

[0117] "Diet goals" refer to the weight and body shape goals that a user wishes to achieve, and serve as a basis for creating training and dietary guidance plans.

[0118] As an embodiment of the present invention, the following system is constructed.

[0119] First, the server provides a means using VR technology. Specifically, by wearing a VR device, users can train in a virtual reality environment. This VR device provides a realistic training environment through visual and auditory senses, enhancing the user's sense of immersion.

[0120] Next, the server has a means to provide personalized training and dietary advice using generative AI. The generative AI generates optimal training and dietary menus based on information such as the user's body type, physical strength, health condition, food preferences, and diet goals. This is done by using OpenAI's API to analyze the user's individual data and provide the optimal plan.

[0121] Furthermore, the server has a means for selling special nutritional foods and training equipment, which allows users to easily purchase the nutritional foods and training equipment they need.

[0122] The server also suggests optimal meal menus based on the user's body type, physical strength, health condition, food preferences, and diet goals, and provides a means to order meals through a food delivery service, allowing users to easily receive healthy meals at home.

[0123] Specifically, the system first obtains user information, including data entered by the user via a smartphone or computer. Next, it sends prompts to a generative AI model, which then generates optimal training and meal plans. The following is an example of a prompt:

[0124] User's body type: Normal

[0125] User's stamina: Medium

[0126] Patient's health: Good

[0127] User's food preference: Japanese food

[0128] User's diet goal: weight loss

[0129] Use this information to create the optimal training and meal plan for you.

[0130] The generated menu is displayed to the user through a VR device, and the user can follow the menu to train and order meals using a food delivery service based on the menu.

[0131] This system allows users to receive individually optimized training and dietary advice, and also eliminates the hassle through food delivery services, making it possible for them to achieve sustainable diet success.

[0132] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0133] Step 1:

[0134] The user uses a smartphone or PC to input personal information such as body type, physical strength, health status, food preferences, and diet goals. This information is sent to the server. The input data includes the user's height, weight, exercise habits, and food preferences. The server receives this data and stores it in a database.

[0135] Step 2:

[0136] The server creates prompts for the generative AI model based on the saved user information. The prompts include information about the user's body type, physical strength, health status, food preferences, and diet goals. Specific examples of prompts are as follows:

[0137] User's body type: Normal

[0138] User's stamina: Medium

[0139] Patient's health: Good

[0140] User's food preference: Japanese food

[0141] User's diet goal: weight loss

[0142] Use this information to create the optimal training and meal plan for you.

[0143] Step 3:

[0144] The server sends the prompt to the generative AI model, which then generates the optimal training and meal menus for the user. The generative AI model then analyzes the prompt and generates the optimal training and meal menus for the user. The generated menus are then returned to the server.

[0145] Step 4:

[0146] The server sends the generated training and meal menus to the VR device. The user wears the VR device and trains in a virtual reality environment. The VR device provides a realistic training environment through visual and auditory channels, enhancing the user's sense of immersion.

[0147] Step 5:

[0148] The server orders meals through a food delivery service based on the generated meal menu. The user uses a smartphone or computer to check the proposed meal menu and confirm the order. The server then sends the order information to the food delivery service, and the meal is delivered to the user's home.

[0149] Step 6:

[0150] The user receives the delivered meal and eats according to the suggested meal menu, which allows the user to continue eating healthy while saving time and effort.

[0151] Step 7:

[0152] The server collects the user's training and diet history data and reflects it in the next training session or dietary advice, thereby understanding the user's progress and providing more effective diet support.

[0153] Example 2

[0154] Next, a description will be given of Example 2 of Form 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."

[0155] Conventional diet support systems have had the problem of being unable to provide effective diet support because it is difficult to provide personalized dietary advice and training programs for each user. Furthermore, there have been issues with the inability to properly recommend special nutritional foods and training equipment, resulting in a low success rate for users in dieting.

[0156] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for the user to input personal data using a terminal, a means for the server to receive and store the input data, a means for the server to analyze the data using a generated AI model and generate dietary advice and a training program, a means for the server to recommend special nutritional foods based on the generated dietary advice, a means for the server to recommend training equipment based on the generated training program, and a means for the terminal to display to the user the advice and recommended products received from the server. This makes it possible to provide personalized dietary advice and training programs to the user and to appropriately recommend special nutritional foods and training equipment, thereby improving the user's success rate in dieting.

[0157] "User" refers to an individual who uses the system to input personal data and receive dietary advice and training programs.

[0158] "Terminal" refers to the device used by the User to input personal data and receive and display advice and recommended products from the Server, including, for example, a smartphone or PC.

[0159] "Server" refers to the computer system that stores data received from users, analyzes the data using a generative AI model, and generates dietary advice and training programs.

[0160] A "generative AI model" refers to an artificial intelligence model that takes a user's personal data and prompt text as input and generates appropriate dietary advice and training programs.

[0161] "Dietary guidance" refers to providing advice on appropriate dietary content and nutritional intake methods based on the user's diet goals and health condition.

[0162] A "training program" refers to advice on appropriate exercise content and training methods based on the user's diet goals and exercise habits.

[0163] "Special nutritional foods" refer to foods that are recommended based on the user's dietary advice and are effective for dieting and maintaining health, such as protein bars and low-calorie smoothies.

[0164] "Training equipment" refers to equipment recommended for effective exercise and training based on a user's training program, including, for example, running machines and dumbbell sets.

[0165] "Advice" refers to specific instructions or recommendations regarding dietary guidance or training programs provided by a generative AI model based on a user's personal data.

[0166] "Recommended Products" refers to specialized nutritional foods or training equipment that the generative AI model recommends based on the user's dietary guidance and training program.

[0167] This invention is a diet support system in which a user inputs personal data using a terminal, and a server analyzes the data to provide personalized dietary advice and training programs. Specific embodiments of this system are described below.

[0168] First, the user accesses the system using a device such as a smartphone or PC. Using a dedicated application or web form, the user enters personal data such as height, weight, age, gender, exercise habits, and dietary details. For example, the user might enter "height 160 cm, weight 70 kg, age 30, gender female, exercise twice a week, bread and coffee for breakfast, salad for lunch, and fish and vegetables for dinner."

[0169] The device then sends the entered data to a server, which stores the data using a relational database management system (RDBMS) such as MySQL (registered trademark) or PostgreSQL. For example, the server executes the following SQL query: "INSERT INTO user_data (height, weight, age, gender, exercise_habits, diet) VALUES (160, 70, 30, 'female', '2 times a week', 'bread and coffee for breakfast, salad for lunch, fish and vegetables for dinner');"

[0170] The server uses data analysis software such as Python (registered trademark) or R to analyze the stored user data. The server uses a generative AI model (e.g., GPT-4) to generate optimal dietary advice and training programs for the user's diet goals. The generative AI model receives a prompt sentence as input and generates appropriate advice. For example, the following prompt sentence is input to the generative AI model:

[0171] "I'm a 30-year-old woman who weighs 70 kg and is 160 cm tall. I exercise twice a week. My goal is to lose 5 kg in three months. Please provide me with the optimal dietary advice and training program."

[0172] The generative AI model generates dietary advice based on this prompt. For example, it generates advice such as, "We recommend eating high-protein foods for breakfast and including plenty of vegetables at lunch and dinner. It is also recommended to choose nuts or yogurt as snacks." It also recommends, "We recommend purchasing protein bars and low-calorie smoothies."

[0173] Similarly, a generative AI model can generate a training program, such as "Perform 30 minutes of aerobic exercise (e.g., jogging or cycling) three times a week, and add strength training twice a week," and further recommend "We recommend purchasing a treadmill and a set of dumbbells."

[0174] Finally, the server sends the generated advice and recommended products to the device. The device then displays the advice and recommended products to the user using the application's dashboard or notification function. For example, the device may display specific advice and recommended products along with a message such as, "A dietary guide and training program based on your diet goals has been generated. Details are below."

[0175] In this way, users can receive personalized dietary advice and training programs, and receive appropriate recommendations for special nutritional foods and training equipment. This system can improve the user's success rate in dieting.

[0176] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0177] Step 1:

[0178] Users access the system using a terminal and enter their personal data. Specifically, they enter information such as height, weight, age, gender, exercise habits, and diet through a dedicated application or web form. For example, they might enter "height 160 cm, weight 70 kg, age 30, gender female, exercise twice a week, bread and coffee for breakfast, salad for lunch, and fish and vegetables for dinner." The input data is saved on the terminal in JSON format or as form data.

[0179] Step 2:

[0180] The device sends the entered data to the server. Specifically, it uses an HTTP POST request to send user data to the server. For example, it sends JSON-formatted data to the endpoint " / submitUserData". The server stores the received data in a relational database management system (RDBMS) such as MySQL or PostgreSQL. For example, it executes the following SQL query: "INSERT INTO user_data (height, weight, age, gender, exercise_habits, diet) VALUES (160, 70, 30, 'female', '2 times a week', 'bread and coffee for breakfast, salad for lunch, fish and vegetables for dinner');".

[0181] Step 3:

[0182] The server analyzes the stored user data. Specifically, it uses data analysis software such as Python or R to calculate the user's BMI (Body Mass Index) and calorie consumption. For example, to calculate BMI, it uses the formula "BMI = weight / (height / 100)^2." The analysis results are used to generate prompt sentences to input into the generative AI model.

[0183] Step 4:

[0184] The server uses a generative AI model (e.g., GPT-4) to generate optimal dietary advice and training programs for the user's diet goals. Specifically, a prompt sentence is input into the generative AI model, which then generates appropriate advice. For example, the following prompt sentence is input into the generative AI model: "I am a 30-year-old woman, weighing 70 kg, standing 160 cm tall, and exercising twice a week. My diet goal is to lose 5 kg in three months. Please provide me with the optimal dietary advice and training program." The generative AI model then generates dietary advice and training programs based on this prompt sentence.

[0185] Step 5:

[0186] The server recommends specific nutritional foods based on the generated dietary advice. Specifically, it selects nutritional foods suitable for the user based on the dietary advice provided by the generative AI model. For example, it may give advice such as "We recommend that you eat high-protein foods for breakfast and include plenty of vegetables for lunch and dinner. It is also recommended that you choose nuts or yogurt as snacks," along with a recommendation such as "We recommend that you purchase protein bars and low-calorie smoothies."

[0187] Step 6:

[0188] The server recommends training equipment based on the generated training program. Specifically, it selects training equipment suitable for the user based on the training program provided by the generative AI model. For example, it advises, "Perform 30 minutes of aerobic exercise (e.g., jogging or cycling) three times a week, and add strength training twice a week," along with a recommendation that, "We recommend purchasing a treadmill and a set of dumbbells."

[0189] Step 7:

[0190] The server sends the generated advice and recommended products to the device. Specifically, it sends the generated advice and recommended products to the device using an HTTP response. The device displays the received information to the user using the application's dashboard or notification function. For example, it displays the specific advice and recommended products along with a message saying, "A dietary guide and training program based on your diet goals has been generated. Details are as follows."

[0191] (Application example 2)

[0192] Next, a description will be given of Application Example 2 of Form 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."

[0193] Conventional diet support systems have had problems such as difficulty in providing optimal dietary advice and training programs for individual users, and ineffective recommendations of special nutritional foods and training equipment. Furthermore, there is a lack of support for sustained diet success that takes into account the user's past failure data, resulting in a low long-term diet success rate.

[0194] 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.

[0195] In this invention, the server includes a means for collecting user data, a means for analyzing the data using a generation AI to generate optimal dietary advice and training programs, and a means for recommending and encouraging the purchase of special nutritional foods and training equipment based on the programs provided by the generation AI. This allows for the provision of optimal diet plans for individual users and the effective recommendation of special nutritional foods and training equipment. Furthermore, by analyzing the user's past failure data, it is possible to support sustained diet success.

[0196] "User data" refers to information such as the user's diet goals, current physical condition, eating habits, and exercise habits.

[0197] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate optimal dietary advice and training programs.

[0198] "Dietary guidance" refers to suggesting appropriate meal content and meal timing based on the user's health condition and diet goals.

[0199] A "training program" refers to suggesting appropriate exercise content and frequency based on the user's physical strength and diet goals.

[0200] "Nutritional foods" refer to foods specifically designed for weight loss or health maintenance.

[0201] "Training equipment" refers to any equipment or device used to effectively exercise or train.

[0202] "Recommendation" refers to the generative AI suggesting specific nutritional foods or training equipment to users based on the analysis results.

[0203] "Encouraging purchases" refers to making it easy for users to purchase recommended nutritional foods and training equipment.

[0204] "Past failure data" refers to information about the user's past failed diet attempts and the reasons for them.

[0205] "Sustained diet success" refers to a user continuing a diet over a long period of time and achieving their goal.

[0206] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[0207] System Configuration

[0208] 1. Collection of User Data

[0209] Hardware: Smartphone

[0210] Software: Application user interface

[0211] Data: Your diet goals, current physical condition, eating habits, and exercise habits

[0212] 2. Data analysis using generative AI

[0213] Hardware: Cloud Server

[0214] Software: Generative AI models (e.g., GPT-4)

[0215] Data processing: Analyze user data and generate optimal dietary advice and training programs

[0216] 3. Selection of recommended products

[0217] Hardware: Cloud Server

[0218] Software: Product database, recommendation engine

[0219] Data calculation: Selecting special nutritional foods and training equipment based on programs provided by generative AI

[0220] 4. Suggestions for users

[0221] Hardware: Smartphone

[0222] Software: Application user interface

[0223] Data Display: Display of recommended dietary advice, training programs, and related products

[0224] 5. Purchase Process

[0225] Hardware: Smartphone

[0226] Software: Electronic payment system

[0227] Data processing: processing and verifying purchases

[0228] Processing Description

[0229] 1. Collection of User Data:

[0230] Through a smartphone application, users input information such as their diet goals, current physical condition, eating habits, exercise habits, etc. This data is sent to a cloud server and stored for analysis by the generative AI.

[0231] 2. Generative AI data analysis:

[0232] A generative AI model (e.g., GPT-4) on a cloud server analyzes the collected user data and generates optimal dietary and training programs for each individual user, taking into account the user's past failure data.

[0233] 3. Recommended product selection:

[0234] Based on the program provided by the generative AI, a recommendation engine on the cloud server selects special nutritional foods and training equipment, thereby recommending the best products for users.

[0235] 4. Suggestions for users:

[0236] Through a smartphone application, users are provided with personalized dietary advice, training programs, and recommended nutritional foods and training equipment.

[0237] 5. Purchasing process:

[0238] Users can easily purchase recommended nutritional foods and training equipment through the application, with the purchase process handled using an electronic payment system.

[0239] Specific examples

[0240] The user opens the application and enters the following information:

[0241] Diet goal: Lose 5kg

[0242] Current health condition: Healthy

[0243] Eating habits: 3 meals a day, snacks

[0244] Exercise habits: Jogging twice a week

[0245] The generative AI analyzes this data and provides dietary advice and training programs such as:

[0246] Dietary advice: High-protein, low-calorie diet, snacks changed to nuts

[0247] Training program: Add strength training three times a week

[0248] Additionally, the following specialized nutritional and training equipment is recommended:

[0249] Nutritional foods: high protein bars, protein shakes

[0250] Training equipment: dumbbell set, yoga mat

[0251] Prompt Sentence Examples

[0252] "Generate optimal dietary advice and training programs based on the user's diet goals, current physical condition, eating habits, and exercise habits, and recommend nutritional foods and training equipment optimized for them."

[0253] This way, users can easily find and purchase the diet plan and related products that best suit them.

[0254] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0255] Step 1:

[0256] Users open the application on their smartphone and enter information such as their diet goals, current physical condition, eating habits, and exercise habits.

[0257] Input: diet goal, current physical condition, eating habits, exercise habits

[0258] Output: User data

[0259] Specific behavior: The user enters the required information into the application's input form and presses the submit button.

[0260] Step 2:

[0261] The device sends the collected user data to a cloud server.

[0262] Input: User data

[0263] Output: User data stored on cloud servers

[0264] Specific behavior: The application calls an API to send user data to the cloud server.

[0265] Step 3:

[0266] The server uses a generative AI model (e.g., GPT-4) to analyze the collected user data and generate optimal dietary advice and training programs.

[0267] Input: User data

[0268] Output: Dietary advice, training program

[0269] How it works: A generative AI model on a cloud server analyzes user data and generates optimal dietary advice and training programs based on prompts.

[0270] Step 4:

[0271] The server selects special nutritional foods and training equipment based on a program provided by the generated AI.

[0272] Input: dietary advice, training program

[0273] Output: Recommended nutrition, training equipment

[0274] Specific operation: A recommendation engine on a cloud server selects the optimal nutritional foods and training equipment from a product database based on dietary advice and training programs.

[0275] Step 5:

[0276] The server provides recommended dietary advice, training programs, and related products to the user.

[0277] Input: Recommended nutrition, training equipment

[0278] Output: Suggestions to the user

[0279] Specific operation: The cloud server sends the generated suggestions to the smartphone application and displays them to the user.

[0280] Step 6:

[0281] Users purchase recommended nutritional foods and training equipment through the application.

[0282] Input: User's purchase intent

[0283] Output: Purchase confirmation, payment completed

[0284] Specific action: The user presses the purchase button and completes payment through an electronic payment system.

[0285] This way, users can easily find and purchase the diet plan and related products that best suit them.

[0286] Example 3

[0287] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0288] Conventional diet support systems were unable to fully utilize data on users' past failures, making it difficult to support sustained diet success. Furthermore, because they did not provide personalized training or dietary advice, users were likely to repeat the same setbacks. Furthermore, they lacked a mechanism for effectively collecting and analyzing user feedback and updating plans, which led to problems with the effects of dieting not being sustained.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0290] In this invention, the server includes means for collecting user data, means for saving and managing the collected data, means for using a generative AI model to analyze the saved data, means for generating personalized training and dietary advice based on the analysis results, means for delivering the generated plan to the user, means for collecting user feedback, and means for analyzing the collected feedback and updating the plan. This makes it possible to utilize data on the user's past failures to support sustained diet success.

[0291] "User data" refers to information entered by the user, such as past diet history, failure patterns, current weight, target weight, dietary content, and exercise habits.

[0292] The "means of collection" refers to the system in which users enter data using a smartphone app or web form and send it to a server.

[0293] "Means for storing and managing" refers to a mechanism by which the server stores the data it receives in a database and manages it appropriately.

[0294] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates personalized training and dietary advice.

[0295] The "means of analysis" refers to a mechanism that uses a generative AI model to analyze stored data and identify users' past patterns of failure and success.

[0296] "Personalized training and dietary guidance" refers to individually customized training plans and dietary advice based on the analysis results.

[0297] The "means of delivery" refers to a mechanism for sending the generated training plan and dietary advice from the server to the terminal and notifying the user.

[0298] The "means of collecting feedback" is a system in which users enter their daily progress and new data into their devices and send it to the server.

[0299] The "means of analyzing feedback and updating plans" refers to a mechanism that re-enters the collected feedback into the generative AI model and updates the training plan and dietary advice based on the analysis results.

[0300] This invention is a system that analyzes data on users' past failures and supports their continued success in dieting. Specific embodiments of this system will be described below.

[0301] Collection of User Data

[0302] Using a smartphone app or web form, users enter information such as their past diet history, failure patterns, current weight, goal weight, dietary habits, exercise habits, etc. For example, a user might enter, "In the past, I often ate sweets late at night."

[0303] Data storage and management

[0304] The terminal sends the data entered by the user to the server, which then stores the received data in a database (e.g., MySQL or PostgreSQL) and manages it appropriately.

[0305] Data analysis

[0306] The server inputs the stored data into a generative AI model (for example, OpenAI's GPT-4) and performs analysis. Specifically, it identifies the user's past patterns of failure and success and obtains data-based insights. For example, the analysis may yield results such as, "This user has a tendency to eat sweets late at night."

[0307] Generate a personalized plan

[0308] The server uses the generative AI model to generate optimal training plans and dietary advice for the user. Specifically, it takes into account the user's past data and provides individually customized advice. For example, it generates specific advice such as "Prepare low-calorie snacks to suppress appetite late at night."

[0309] Plan Delivery

[0310] The server sends the generated training plan and dietary advice to the device. The device then displays the received plan to the user. Specifically, the device provides information using the smartphone app's notification function or dashboard. For example, the device may notify the user, "Prepare a low-calorie snack to suppress your appetite late at night."

[0311] Collecting user feedback

[0312] Users input their daily progress and new data (e.g., weight changes and dietary information) into the device. Specifically, they input their daily food and exercise records into the app. For example, they might input, "I had a low-calorie snack late at night today."

[0313] Analyzing feedback and updating plans

[0314] The device sends the new data entered by the user to the server. The server then inputs the received data into the generative AI model again and analyzes it. Based on the analysis results, the training plan and dietary advice are updated and sent again to the device. For example, if the analysis result shows that "low-calorie snacks are effective," the training plan and dietary advice are updated.

[0315] Prompt Sentence Examples

[0316] "Generate optimal training plans and dietary advice for the user based on their past diet history and failure patterns. The user has a tendency to eat sweets late at night. Please provide specific advice taking this failure pattern into consideration."

[0317] In this way, the diet support system utilizes the user's past data to support continuous diet success. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[0318] Step 1:

[0319] Users use a smartphone app or web form to enter information such as their past diet history, failure patterns, current weight, target weight, dietary habits, and exercise habits.

[0320] Input: past diet history, failure patterns, current weight, goal weight, dietary content, exercise habits

[0321] Output: User data entered

[0322] Step 2:

[0323] The terminal transmits the data entered by the user to the server.

[0324] Input: User data entered by the user

[0325] Output: User data sent to the server

[0326] Step 3:

[0327] The server stores the received data in a database and manages it appropriately, using a database management system such as MySQL or PostgreSQL.

[0328] Input: User data sent to the server

[0329] Output: User data stored in the database

[0330] Step 4:

[0331] The server inputs the stored data into a generative AI model (such as OpenAI's GPT-4) for analysis, specifically identifying the user's past patterns of failure and success and deriving insights from the data.

[0332] Input: User data stored in the database

[0333] Output: Analysis results (e.g., "This user tends to eat sweets late at night")

[0334] Step 5:

[0335] The server uses the generative AI model to generate optimal training plans and dietary advice for each user, taking into account the user's past data and providing individually customized advice.

[0336] Input: Analysis results

[0337] Output: Personalized training plan and dietary advice (e.g., "Pack a low-calorie snack to curb late-night cravings")

[0338] Step 6:

[0339] The server sends the generated training plan and dietary advice to the device, which then displays the received plan to the user. Specifically, the information is provided using the smartphone app's notification function and dashboard.

[0340] Input: Personalized training plans and nutrition advice

[0341] Output: A training plan or dietary advice displayed to the user (e.g., "Pack a low-calorie snack to curb your appetite later in the night")

[0342] Step 7:

[0343] The user inputs their daily progress and new data (e.g., weight changes and dietary information) into the device. Specifically, they input their daily diet and exercise records into the app.

[0344] Input: Daily progress and new data (e.g., "I had a low-calorie snack late at night today")

[0345] Output: New data entered

[0346] Step 8:

[0347] The device sends the new data to the server. The server then inputs the received data back into the generative AI model for analysis. Based on the analysis results, the training plan and dietary advice are updated and sent back to the device.

[0348] Input: New data

[0349] Output: Updated training plan and dietary advice (e.g., "Low-calorie snacks are effective")

[0350] In this way, the diet support system utilizes the user's past data to support sustained diet success.

[0351] (Application example 3)

[0352] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0353] Conventional diet support systems do not provide personalized guidance that takes into full consideration the user's past diet history or failure patterns, which means that users often end up repeating the same setbacks. Furthermore, because they do not sell special nutritional foods or training equipment, it is difficult for users to obtain the resources they need in one place. Furthermore, they lack the functionality to suggest optimal meal menus for users, making it difficult to support sustained diet success.

[0354] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, and means for analyzing the user's past diet history and failure patterns and proposing personalized meal menus. This makes it possible to prevent the user from repeating the same failures and support continuous diet success.

[0355] "VR technology" is a technology that uses virtual reality to provide users with a visual and experiential training environment.

[0356] "Generative AI" is an artificial intelligence technology that analyzes a user's past data and generates personalized training and dietary advice.

[0357] "Personalized training and dietary guidance" means providing customized training and dietary plans based on the user's individual needs and past history.

[0358] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health.

[0359] "Training equipment" refers to equipment used by users for physical training.

[0360] "User's past diet history" refers to records and data of diets that the user has undertaken in the past.

[0361] "Failure patterns" refer to the causes and tendencies that led users to abandon or fail diets in the past.

[0362] A "personalized meal menu" is a meal plan customized based on the user's past diet history and failure patterns.

[0363] A system for carrying out this invention is configured as follows: The server includes means for using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, and means for analyzing a user's past diet history and failure patterns and proposing a personalized meal menu.

[0364] The server first retrieves the user's past diet history and failure patterns from a database. This data includes records of the user's past diet attempts and the reasons for their failures. Next, it uses a generative AI to analyze this data and generate optimal training and meal plans for the user. The generative AI can use OpenAI's API.

[0365] Specifically, the server inputs the following prompt to the generation AI:

[0366] Analyze the user's past diet history and failure patterns to suggest a personalized meal menu.

[0367] User data: {'age': 30, 'gender': 'female', 'height': 160, 'weight': 65, 'diet_history': [{'date': '2022-01-01', 'weight': 70, 'calories': 2000, 'exercise': 'running', 'success': False}, {'date': '2022-02-01', 'weight': 68, 'calories': 1800, 'exercise': 'yoga', 'success': True}], 'failure_patterns': ['stress eating', 'lack of motivation']}

[0368] Based on this prompt, the AI ​​generates the optimal meal menu for the user, which is then displayed on the user's smartphone or tablet.

[0369] In addition, the server also sells special nutritional foods and training equipment, allowing users to centrally purchase the nutritional foods and training equipment they need based on the generated meal menu.

[0370] This system will help users avoid repeating past setbacks and support their continued success in dieting. It will also make it easier for them to continue their diet by providing all the necessary resources in one place.

[0371] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0372] Step 1:

[0373] The server retrieves the user's past diet history and failure patterns from a database. The input is the user's ID, and the output is the user's past diet history and failure patterns. This data includes the user's past diet records and the reasons for failure.

[0374] Step 2:

[0375] The server generates a prompt sentence to be input to the generation AI based on the acquired data. The input is data on the user's past diet history and failure patterns, and the output is a prompt sentence to be input to the generation AI. Specifically, the server generates a prompt sentence that includes information such as the user's age, gender, height, weight, past diet history, and failure patterns.

[0376] Step 3:

[0377] The server sends the generated prompt to the generation AI to generate a personalized meal menu. The input is the generated prompt, and the output is the personalized meal menu returned by the generation AI. The generation AI uses OpenAI's API to generate the optimal meal menu for the user.

[0378] Step 4:

[0379] The server sends the generated meal menu to the user's smartphone or tablet. The input is the personalized meal menu returned by the generation AI, and the output is the meal menu displayed on the user's device. The user can check the generated meal menu through their smartphone or tablet.

[0380] Step 5:

[0381] The server sells special nutritional foods and training equipment. The input is the generated meal menu and the user's purchase request, and the output is a link or information for the user to purchase. The user can centrally purchase the nutritional foods and training equipment they need based on the generated meal menu.

[0382] Step 6:

[0383] The user goes on a diet according to the provided meal menu and training plan. The input is the meal menu and training plan provided by the server, and the output is the user's diet progress data. The user can receive further personalized guidance by feeding the progress data back to the server.

[0384] 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.

[0385] "Example 1"

[0386] One embodiment of the present invention is a diet support system incorporating an emotion engine. In this system, users wear a VR device and receive personalized training and dietary advice provided by a generative AI. The emotion engine is able to interpret the user's facial expressions, tone of voice, and language.

[0387] The system recognizes the user's emotional state from factors such as how they select leaves, and feeds that information back to the AI ​​generator. The AI ​​generator then adjusts the training menu and dietary advice based on this emotional information. For example, if it determines that the user is feeling stressed, it will suggest a training menu with a relaxation effect or recommend a meal that is effective in relieving stress.

[0388] "Example 2"

[0389] In addition, recommendations for special nutritional foods and training equipment are also adjusted according to the user's emotional state. For example, if the system determines that the user is feeling depressed, it will recommend nutritional foods that will cheer them up or training equipment that will help them change their mood. This makes it possible to provide optimal diet support according to the user's emotional state.

[0390] "Example 3"

[0391] Furthermore, this system will be offered on a monthly fee basis, with the aim of expanding its scale and achieving sales of over 10 billion yen in the future. By incorporating an emotion engine, it will be possible to increase user satisfaction and encourage long-term use. This will ensure stable revenue and enable large-scale business development.

[0392] The processing flow of each embodiment will be described below.

[0393] "Example 1"

[0394] Step 1: The user puts on the VR device.

[0395] Step 2: Generative AI provides users with personalized training and dietary advice.

[0396] Step 3: The emotion engine recognizes the user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[0397] Step 4: The emotion engine feeds that information back to the generative AI.

[0398] Step 5: The generative AI adjusts the training menu and dietary advice based on this emotional information.

[0399] "Example 2"

[0400] Step 1: The emotion engine determines that the user's emotional state is depressed.

[0401] Step 2: Generative AI recommends nutritious foods to boost your energy.

[0402] Step 3: The generative AI recommends training equipment that will provide a refreshing change of pace.

[0403] "Example 3"

[0404] Step 1: The system is offered on a monthly subscription basis.

[0405] Step 2: Incorporating an emotional engine increases user satisfaction and encourages long-term usage.

[0406] Step 3: Secure stable profits and realize large-scale business expansion.

[0407] Example 1

[0408] Next, a description will be given of Example 1 of Form 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."

[0409] Conventional diet support systems do not adequately provide personalized training and dietary advice based on the user's individual body type, physical strength, and health condition. Furthermore, they do not adjust the training and dietary advice to take into account the user's emotional state, making it difficult to achieve sustained diet success. Furthermore, they lack comprehensive support that combines the sale of special nutritional foods and training equipment.

[0410] 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.

[0411] In this invention, the server includes a means for using VR technology, a means for providing personalized training and dietary advice using a generating AI, a means for analyzing the user's emotional state using an emotion engine and feeding that information back to the generating AI, and a means for selling special nutritional foods and training equipment. This not only provides personalized training and dietary advice based on the user's individual body type, physical strength, and health condition, but also makes it possible to make adjustments taking into account the user's emotional state, supporting sustainable diet success. Furthermore, by combining the sale of special nutritional foods and training equipment, comprehensive diet support is realized.

[0412] "VR technology" is a technology that uses virtual reality to provide users with an immersive experience.

[0413] "Generative AI" is a technology that uses artificial intelligence to analyze user data and generate personalized training and dietary advice.

[0414] "Personalized training" refers to a training menu that is individually optimized based on the user's body type, physical strength, and health condition.

[0415] "Dietary advice" refers to dietary advice provided based on a user's dietary preferences, health status, and dietary goals.

[0416] The "emotion engine" is a technology that analyzes a user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[0417] "Feedback" is the process by which the system provides appropriate actions to the user based on analyzed information.

[0418] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health.

[0419] "Training equipment" refers to equipment that a user uses to train.

[0420] "Sales" is the act of providing goods or services and receiving payment in return.

[0421] "Sustained weight loss success" refers to achieving and maintaining weight loss goals over an extended period of time.

[0422] The diet support system of the present invention is a system that combines VR technology and generative AI to provide users with personalized training and dietary advice. Specific embodiments of this system are described below.

[0423] Hardware and software used

[0424] Hardware: VR device

[0425] Software: Generative AI models (e.g., OpenAI GPT-4), emotion engines

[0426] Program processing

[0427] Data collection and analysis

[0428] When a user wears a VR device, the system collects the user's motion data and environmental data in real time. The server collects basic information such as the user's body type (height, weight), physical strength, and health condition (e.g., blood pressure, heart rate). This information is either entered by the user in advance or obtained from the wearable device.

[0429] Generative AI model for generating training menus

[0430] The server sends a prompt to the generative AI model based on the collected user information. For example, it sends a prompt such as, "Please generate the optimal training menu for a user who is 170 cm tall, weighs 70 kg, and has moderate physical strength." The generative AI model receives the prompt and generates the optimal training menu for the user. For example, it generates a menu that combines 30 minutes of aerobic exercise and strength training.

[0431] Providing training menus

[0432] The server sends the training menu received from the generative AI model to the user's device (VR device). The user checks the training menu through the VR device and starts exercising. The VR device tracks the user's movements in real time and provides feedback.

[0433] Emotional state analysis using emotion engine

[0434] The server uses an emotion engine to analyze the user's emotional state based on their facial expressions, tone of voice, and choice of words. For example, it can determine whether the user is feeling stressed. The analyzed emotional information is fed back to the generative AI model.

[0435] Adjustment based on emotional information

[0436] The generative AI model adjusts training menus and dietary advice based on the emotional information fed back. For example, it might suggest a yoga menu with a relaxing effect or recommend a meal that is effective for relieving stress. The server then sends the adjusted training menu and dietary advice to the user's device.

[0437] Specific examples

[0438] Generate a training menu:

[0439] User's body data: Height 170cm, Weight 70kg

[0440] User's stamina data: Medium stamina

[0441] Based on this data, the generative AI generates a menu that combines 30 minutes of aerobic exercise and strength training.

[0442] Dietary advice provided:

[0443] User's dietary preferences: Likes vegetables, less meat

[0444] User's health condition: High blood pressure

[0445] Based on this data, the generative AI suggests low-salt, vegetable-based meal menus.

[0446] Emotion Engine Feedback:

[0447] The emotion engine recognizes when a user is feeling stressed

[0448] The generative AI suggests relaxing yoga exercises and recommends foods that are effective in relieving stress (e.g., dark chocolate and nuts).

[0449] Prompt Sentence Examples

[0450] "Generate the optimal training menu based on the user's body type and physical fitness data."

[0451] "Provide personalized dietary advice that takes into account the user's dietary preferences and health status."

[0452] "Recognize your emotional state and adjust your training and dietary recommendations accordingly."

[0453] The above is a specific embodiment of the diet support system of the present invention.

[0454] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0455] Step 1:

[0456] The user puts on the VR device.

[0457] Specifically, the user wears the VR device at home and logs in to the system, which then prepares the system to collect user motion and environmental data in real time.

[0458] Input: User login information, VR device connection

[0459] Output: Start collecting user behavior data and environmental data

[0460] Step 2:

[0461] The server collects basic information about the user.

[0462] Specifically, the server collects basic information about the user, such as their body type (height, weight), physical strength, and health status (e.g., blood pressure, heart rate), which can be entered by the user in advance or obtained from the wearable device.

[0463] Input: User basic information, wearable device data

[0464] Output: Collected user basic information

[0465] Step 3:

[0466] The server sends a prompt to the generative AI model.

[0467] Specifically, the server sends a prompt to the generative AI model based on the collected user information, such as "Please generate the optimal training menu for a user who is 170 cm tall, weighs 70 kg, and has moderate physical strength."

[0468] Input: User's basic information, prompt text

[0469] Output: Sending a prompt to the generative AI model

[0470] Step 4:

[0471] A generative AI model generates a training menu.

[0472] Specifically, a generative AI model (e.g., OpenAI GPT-4) receives a prompt and generates an optimal training menu for the user, such as a 30-minute combination of cardio and strength training.

[0473] Input: prompt statement

[0474] Output: Generated training menu

[0475] Step 5:

[0476] The server transmits the generated training menu to the terminal.

[0477] Specifically, the server sends the training menu received from the generative AI model to the user's device (VR device), where the user can view the training menu.

[0478] Input: Generated training menu

[0479] Output: Sending the training menu to the user's device

[0480] Step 6:

[0481] The user begins training.

[0482] Specifically, the user begins exercising according to the training menu provided through the VR device, which tracks the user's movements in real time and provides feedback.

[0483] Input: Training Menu

[0484] Output: User training data, feedback

[0485] Step 7:

[0486] The server analyzes the user's emotional state using an emotion engine.

[0487] Specifically, the server uses an emotion engine to analyze the user's emotional state based on their facial expressions, tone of voice, and choice of words, for example, to determine whether they are feeling stressed.

[0488] Input: User facial expression data, tone of voice, and word choice

[0489] Output: Parsed emotion information

[0490] Step 8:

[0491] The server feeds emotional information back to the generative AI model.

[0492] Specifically, the server feeds back the analyzed emotional information to the generative AI model, for example, sending information that the user is feeling stressed.

[0493] Input: Parsed emotion information

[0494] Output: Feedback to the generative AI model

[0495] Step 9:

[0496] The generative AI model adjusts training menus and dietary advice based on emotional information.

[0497] Specifically, the generative AI model adjusts training menus and dietary advice based on the emotional information fed back to it, for example, by suggesting yoga menus with a relaxing effect or recommending meals that are effective for relieving stress.

[0498] Input: Feedback emotional information

[0499] Output: Tailored training menus and dietary advice

[0500] Step 10:

[0501] The server sends tailored menus and instructions to the terminal.

[0502] Specifically, the server sends the adjusted training menu and dietary advice to the user's device, and the user receives the new menu and advice through the VR device.

[0503] Input: Tailored training menus and dietary advice

[0504] Output: sent to the user's terminal

[0505] (Application example 1)

[0506] Next, a description will be given of Application Example 1 of Embodiment 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."

[0507] Conventional diet support systems do not provide personalized training or dietary advice that fully considers the individual needs and emotional state of the user. Furthermore, few systems are designed for use in brick-and-mortar stores, limiting the opportunities for users to directly experience the system. Furthermore, there is no system that recognizes the user's emotional state in real time and provides guidance accordingly, making it difficult to maintain user motivation.

[0508] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means using VR technology, means for providing personalized training and dietary advice using a generation AI, means for selling special nutritional foods and training equipment, means for recognizing the user's emotional state using an emotion engine and feeding that information back to the generation AI, and means for the user to wear a VR device in a physical store and receive personalized training and dietary advice provided by the generation AI. This enables personalized training and dietary advice tailored to the user's individual needs and emotional state, making it easier to maintain the user's motivation through the experience at the physical store.

[0509] "VR technology" is a technology for realizing virtual reality, allowing users to immerse themselves in a virtual environment.

[0510] "Generative AI" is a technology that uses artificial intelligence to automatically generate optimal training and dietary advice for users.

[0511] "Personalized training and dietary guidance" means providing a training program and / or meal plan that is customized based on the user's individual needs and condition.

[0512] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health.

[0513] "Training equipment" refers to equipment used for fitness and exercise.

[0514] The "emotion engine" is a technology that recognizes the user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[0515] A "physical store" is a store that exists in a physical location and where customers can visit in person to receive services.

[0516] A "VR device" is a hardware device for experiencing virtual reality, including a head-mounted display and controllers.

[0517] "Feedback" means that the system adjusts the content of training and dietary advice based on information it receives from the user.

[0518] This invention is a system in which users wear a VR device in a physical store and receive personalized training and dietary advice provided by a generating AI. Specific embodiments for realizing this system are described below.

[0519] System configuration

[0520] Hardware

[0521] VR devices: use head-mounted displays, which allow users to immerse themselves in a virtual reality environment.

[0522] Camera: Used to capture the user's facial expressions in real time, which allows the emotion engine to recognize the user's emotional state.

[0523] Server: Provides the computational resources to run the generative AI model and generate training and dietary advice.

[0524] software

[0525] Generative AI model: An artificial intelligence model that generates training and dietary advice based on the user's individual needs and condition.

[0526] Emotion engine: Software that recognizes the user's emotional state from facial expressions, tone of voice, choice of words, etc.

[0527] VR application: An application that allows users to receive training and dietary advice through a VR device.

[0528] System Operation

[0529] 1. Acquisition of user information: The server acquires information such as the user's body type, physical strength, health condition, food preferences, and diet goals.

[0530] 2. Emotional state recognition: The camera captures the user's facial expressions, and the emotion engine analyzes the data to recognize the emotional state.

[0531] 3. Generating training and dietary advice: The generative AI model generates optimal training menus and dietary advice based on the acquired user information and emotional state.

[0532] 4. Display on VR device: The generated training menu and dietary advice are displayed on the VR device, and the user experiences it in a virtual reality environment.

[0533] Specific examples

[0534] For example, consider the case where the user information is as follows:

[0535] Body Type: Standard

[0536] Physical fitness: moderate

[0537] Health: Good

[0538] Food preference: Japanese food

[0539] Diet goal: Weight loss

[0540] Based on this information, the generative AI model generates a prompt like this:

[0541] The user has a normal build, is moderately fit, and is in good health. He or she enjoys Japanese food and is trying to lose weight. The user is currently feeling stressed. Based on this information, generate an optimal training menu and dietary advice.

[0542] By inputting this prompt into a generative AI model, the system generates a training menu and dietary advice that is optimal for the user. The generated content is then provided to the user via a VR device, allowing them to receive training and dietary advice in a virtual reality environment.

[0543] This allows for personalized training and dietary advice tailored to each user's individual needs and emotional state, making it easier to maintain user motivation through the physical store experience.

[0544] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0545] Step 1:

[0546] The server acquires information about the user, such as their body type, physical strength, health status, food preferences, and diet goals. This information is collected from data previously entered by the user and from past history data. The input data is saved as the user's profile information and used as input for the generative AI model.

[0547] Step 2:

[0548] The terminal (a camera connected to the VR device) captures the user's facial expressions in real time. The captured video data is sent to the emotion engine. The emotion engine analyzes the video data and recognizes the user's emotional state (e.g., stress, joy, fatigue, etc.). The recognized emotional state is generated as the output.

[0549] Step 3:

[0550] The server generates a prompt sentence for the generative AI model based on the acquired user information and emotional state. The prompt sentence is generated in the following format:

[0551] The user has a normal build, is moderately fit, and is in good health. He or she enjoys Japanese food and is trying to lose weight. The user is currently feeling stressed. Based on this information, generate an optimal training menu and dietary advice.

[0552] The generated prompt sentences are used as input for a generative AI model.

[0553] Step 4:

[0554] The server inputs prompts into the generative AI model to generate optimal training menus and dietary advice. The generative AI model takes into account the user's information and emotional state and outputs personalized training menus and dietary advice. The output training menus and dietary advice are stored on the server.

[0555] Step 5:

[0556] The server sends the generated training menu and dietary advice to the VR device, which then displays the received training menu and dietary advice to the user. The user can then receive training and dietary advice in a virtual reality environment through the VR device.

[0557] Step 6:

[0558] The user wears a VR device and performs exercises according to the generated training menu. Exercise progress and feedback are sent to the server in real time. The server then adjusts the training menu and dietary advice accordingly based on this data.

[0559] Step 7:

[0560] The server analyzes the user's exercise data and feedback and reflects it in the next training menu and dietary advice, thereby continuously providing optimal advice based on the user's progress and emotional state.

[0561] Example 2

[0562] Next, a description will be given of Example 2 of Form 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."

[0563] Conventional diet support systems lack personalized support tailored to the user's individual needs and emotional state. They generally recommend specific nutritional foods or training equipment, which are not optimized for the user's specific situation. Furthermore, they do not utilize data on the user's past failures to support sustained diet success, making it difficult for users to maintain their motivation.

[0564] 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.

[0565] In this invention, the server includes a means for receiving and storing data entered by the user, a means for generating optimal meal plans and training plans for the user using a generative AI model, a means for recommending special nutritional foods and training equipment based on the generated plans, and a means for analyzing the user's emotional state and recommending nutritional foods and training equipment according to the emotional state. This enables personalized support according to the user's individual needs and emotional state, and can support sustained diet success.

[0566] "Means for receiving and storing data entered by the user" refers to the function by which the server receives data entered by the user through the terminal, such as weight, height, target weight, dietary preferences, exercise habits, and emotional state, and stores it in a database.

[0567] "Means for generating optimal meal plans and training plans for users using a generative AI model" refers to a function that uses a generative AI model to create individually optimized meal plans and training plans based on data input by the user.

[0568] "Means for recommending special nutritional foods and training equipment based on the generated plan" refers to a function that analyzes the meal plan and training plan created by the generative AI model, selects the most suitable nutritional foods and training equipment from a database, and recommends them to the user.

[0569] "Means for analyzing the user's emotional state and recommending nutritional foods and training equipment according to the emotional state" refers to a function that uses emotion analysis software to analyze the user's emotional state and, based on the results, recommends nutritional foods and training equipment that are appropriate for the user's emotional state.

[0570] The "means for supporting sustained diet success" is a function that analyzes the user's past failure data and provides support to help the user continue their diet.

[0571] This invention is a diet support system that includes means for receiving and storing data entered by a user, means for generating optimal meal plans and training plans for a user using a generative AI model, means for recommending special nutritional foods and training equipment based on the generated plans, and means for analyzing the user's emotional state and recommending nutritional foods and training equipment according to the emotional state.

[0572] 1. User data entry and storage

[0573] A user accesses the diet support system application using a device (smartphone or PC). The user inputs data such as weight, height, target weight, food preferences, exercise habits, and current emotional state. The device sends the input data to the server. The server stores the received data in a database. This data is used in subsequent processing.

[0574] 2. Nutrition advice and training plan generation

[0575] The server sends prompts to a generative AI model (e.g., OpenAI's GPT-4) based on the stored user data. The prompts include the user's weight, goal weight, food preferences, exercise habits, etc. The generative AI model generates optimal meal and training plans for the user based on the prompts.

[0576] Example prompt sentence:

[0577] "The user's current weight is 70kg and their goal weight is 65kg. They eat a high-protein diet and exercise three times a week. Please suggest the best meal and training plan for the user."

[0578] Based on this prompt, the generative AI model generates a specific meal plan (e.g., oatmeal and a protein shake for breakfast, chicken breast and a vegetable salad for lunch, and salmon and broccoli for dinner) and a training plan (e.g., strength training and cardio three times a week).

[0579] 3. Recommending special nutritional foods and training equipment

[0580] The server analyzes the generated meal and training plans and references a database of specialized nutritional foods and training equipment. The server then selects and recommends products that best fit the plan. For example, if a high-protein meal plan is generated, the server may recommend high-protein protein bars. The server may also recommend the best training equipment for a specific training program.

[0581] 4. Analyzing and regulating emotional states

[0582] The server analyzes the user's emotional state using emotion analysis software (e.g., IBM Watson® emotion analysis API) based on the user's input data and past behavioral data. If the server determines that the user is depressed, it will recommend nutritious foods to cheer them up or exercise equipment to refresh their mood.

[0583] 5. Displaying recommendations and encouraging purchases

[0584] The server sends the generated meal plan, training plan, and recommended special nutritional foods and training equipment to the terminal, which displays this information to the user. The user can check the displayed information and purchase the recommended products if necessary.

[0585] In this way, the server provides the user with the most suitable plan and product to effectively support their diet.

[0586] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0587] Step 1:

[0588] A user uses a terminal to access the diet support system application and inputs data such as weight, height, target weight, food preferences, exercise habits, and current emotional state. The input data is sent from the terminal to the server. The input data includes the user's individual needs and conditions.

[0589] Step 2:

[0590] The server receives the user data sent from the terminal and stores it in a database. The stored data is used in subsequent processing. Specifically, it is stored in the database in association with the user ID.

[0591] Step 3:

[0592] The server sends prompts to the generative AI model based on the stored user data, including the user's weight, target weight, food preferences, exercise habits, etc. The generative AI model then generates optimal meal and training plans for the user based on the prompts.

[0593] Example prompt sentence:

[0594] "The user's current weight is 70kg and their goal weight is 65kg. They eat a high-protein diet and exercise three times a week. Please suggest the best meal and training plan for the user."

[0595] Step 4:

[0596] The generative AI model generates specific meal and training plans based on the prompts. The generated plans are sent to a server. The specific meal plan includes breakfast, lunch, and dinner menus, and the training plan includes the type and frequency of exercise to be performed each week.

[0597] Step 5:

[0598] The server analyzes the generated meal and training plans and consults a database of specialized nutritional foods and training equipment. The server selects and recommends products that best fit the plan to the user. For example, if a high-protein meal plan is generated, the server will recommend high-protein protein bars. A list of recommended products is generated as an output.

[0599] Step 6:

[0600] The server uses emotion analysis software to analyze the user's emotional state based on the user's input data and past behavioral data. If the server determines that the user is depressed, it will recommend nutritional foods to cheer them up or workout equipment to refresh their mood. A list of recommendations based on the user's emotional state is output.

[0601] Step 7:

[0602] The server transmits the generated meal plan, training plan, and recommended special nutritional foods and training equipment to the terminal. The terminal displays this information to the user. The user can check the displayed information and purchase the recommended products if necessary. Information displayed to the user is generated as an output.

[0603] (Application example 2)

[0604] Next, a description will be given of Application Example 2 of Form 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."

[0605] Conventional diet support systems lack personalized support tailored to the user's individual needs and emotional state. Furthermore, they generally recommend specific nutritional foods or training equipment, but do not recommend optimal products based on the user's emotional state. Furthermore, purchasing the recommended products requires a separate procedure, which is time-consuming for users.

[0606] 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.

[0607] In this invention, the server includes a means for using VR technology, a means for providing personalized training and dietary advice using generative AI, a means for selling special nutritional foods and training equipment, a means for analyzing the user's emotional state and recommending optimal nutritional foods and training equipment, and a means for making the recommended products available for purchase within the application. This enables optimal diet support tailored to the user's individual needs and emotional state, and simplifies the process of purchasing the recommended products.

[0608] "VR technology" is a technology that uses virtual reality to provide users with an immersive experience.

[0609] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate training and dietary advice tailored to individual needs.

[0610] "Personalized training and dietary guidance" means providing a training program and meal plan that is customized based on the user's individual data and needs.

[0611] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health.

[0612] "Training equipment" means equipment used for fitness or exercise.

[0613] "Analyzing emotional state" means analyzing the user's text or voice data to quantify or classify their emotions at that time.

[0614] "Recommending optimal nutritional foods and training equipment" means suggesting the most suitable nutritional foods and training equipment based on the user's emotional state and individual needs.

[0615] "Making the recommended product available for purchase within the application" means that the purchase procedure can be completed within the application.

[0616] The following system configuration is used as an embodiment of the present invention: The system includes a server, a user terminal, and a generative AI model.

[0617] The server includes means for using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, means for analyzing the emotional state of the user and recommending optimal nutritional foods and training equipment, and means for making the recommended products available for purchase within the application.

[0618] The user terminal may be a smartphone, smart glasses, a head-mounted display, or a robot. These terminals communicate with a server and provide personalized training and dietary advice to the user.

[0619] The generative AI model analyzes the user's diet, training history, and emotional state to recommend optimal nutritional foods and training equipment. The TextBlob library is used to analyze emotional states, and the Requests library is used to send and receive data.

[0620] For example, if a user types "I'm tired today" on their smartphone, TextBlob analyzes their emotions and the AI ​​generator will recommend nutritious foods that have a relaxing effect. Similarly, if a user logs that they "ran for 30 minutes," the AI ​​generator will recommend the best training equipment.

[0621] An example prompt sentence would be of the following format:

[0622] User ID: 12345

[0623] Food log: [{'food_item': 'Apple', 'calories': 95, 'date': '2023-10-01T12:00:00'}]

[0624] Training log: [{'workout_type': 'Running', 'duration': 30, 'date': '2023-10-01T12:30:00'}]

[0625] Sentiment log: [{'text': 'I feel great today!', 'polarity': 0.8, 'date': '2023-10-01T13:00:00'}]

[0626] By inputting this prompt into a generative AI model, you can receive recommendations for optimal nutritional foods and training equipment.

[0627] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0628] Step 1:

[0629] The user uses a smartphone to input a log of their meals and training. The input data includes the contents of the meal, calories, type of training, time, etc. This records the user's meal and training history.

[0630] Step 2:

[0631] The user inputs their emotional state. For example, they input text such as "I'm tired today." This text data becomes the input data for emotion analysis.

[0632] Step 3:

[0633] The device uses the TextBlob library to analyze the input emotional state text. As a result, the emotional polarity (positive or negative) is quantified. This records the user's emotional state as numerical data.

[0634] Step 4:

[0635] The device sends the meal log, training log, and emotion log to the server. The sent data includes the user ID, meal log, training log, and emotion log. This allows the server to receive the user's latest data.

[0636] Step 5:

[0637] The server uses a generative AI model to analyze the received data, taking into account the user's past data and emotional state, and then recommends optimal nutritional foods and training equipment for the user.

[0638] Step 6:

[0639] The server sends information about recommended nutritional foods and training equipment to the device. The sent data includes detailed information about the recommended products. The device then displays the recommended products to the user.

[0640] Step 7:

[0641] The user purchases the recommended product within the application. When the user presses the purchase button, the device sends a purchase request to the server, allowing the user to easily purchase the product.

[0642] Step 8:

[0643] The server receives the purchase request and processes the purchase, which includes checking inventory, processing payment, and arranging delivery. The purchased product is then delivered to the user's hands.

[0644] Example 3

[0645] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0646] Conventional diet support systems do not provide personalized support that takes into account each user's individual failure patterns and emotional state, making it difficult to achieve sustained diet success. Furthermore, there is a lack of effective methods to increase user satisfaction and promote long-term use.

[0647] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0648] In this invention, the server includes means for collecting user data, means for saving and managing the collected data, means for analyzing the data using a generative AI model and identifying the user's failure patterns, means for generating personalized training plans and dietary advice based on the analysis results, means for monitoring the user's emotional state using an emotion engine and providing appropriate feedback, and means for notifying the user of suggestions and support. This enables personalized support that takes into account the user's individual failure patterns and emotional state, and can support sustained diet success.

[0649] "Means for collecting user data" refers to a device or software that provides an interface for users to input their past diet history and experiences of failure.

[0650] "Means for storing and managing collected data" refers to a device or software for storing data collected from users in a database and managing the data while maintaining its integrity.

[0651] "Means for analyzing data using a generative AI model to identify user failure patterns" refers to devices or software that use machine learning algorithms to analyze users' past data and identify causes and patterns of failure.

[0652] "Means for generating personalized training plans and dietary advice based on analysis results" refers to a device or software that automatically generates optimal training plans and dietary advice for users based on the results of data analysis.

[0653] "Means for monitoring a user's emotional state using an emotion engine and providing appropriate feedback" refers to a device or software for monitoring a user's emotional state in real time and providing feedback according to that state.

[0654] "Means for notifying users of suggestions and support" refers to devices or software that notify users of generated plans and feedback and encourage them to take action.

[0655] This invention is a system that analyzes a user's past diet history and failure patterns to support sustained diet success. A specific embodiment of this system is described below.

[0656] First, the server provides a means for collecting user data. Users use devices such as smartphones or PCs to input their past diet history and failure experiences through a dedicated application or web interface. This data includes dietary details, exercise history, weight fluctuations, and reasons for failure.

[0657] The server then uses a means to store and manage the collected data. The data is stored in a relational database such as MySQL or PostgreSQL. The server validates the data to ensure its integrity.

[0658] The server then analyzes the data using a generative AI model. Specifically, it uses Python and TENSORFLOW® to build a machine learning model and identify patterns of user frustration. Based on past data, the server predicts the situations in which users are likely to fail.

[0659] Based on the analysis results, the server generates personalized training plans and dietary advice. For example, if a user has a tendency to eat sweets late at night, the server will suggest low-calorie snacks. It also provides exercise plans tailored to the user's lifestyle and preferences.

[0660] Furthermore, the server monitors the user's emotional state using an emotion engine. The emotion engine estimates emotions based on the user's input data and behavioral patterns and monitors them in real time. For example, if the user is feeling stressed, the server will suggest yoga or meditation to help them relax.

[0661] Finally, the server notifies the user of the generated plan and feedback. The device receives the notification and displays it to the user. The user acts on the proposed plan and re-enters their progress, allowing the server to continuously update the data and provide support.

[0662] Examples of specific prompts include:

[0663] "Analyze their past diet history and failure patterns, and suggest low-calorie snacks that are okay to eat late at night. Also, if the user is feeling stressed, suggest yoga or meditation to help them relax."

[0664] In this way, the server can prevent the user from repeating the same setbacks and support the user in achieving continuous diet success. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[0665] Step 1:

[0666] The user inputs his or her past diet history and experiences of failure.

[0667] Input: Data such as diet, exercise history, weight fluctuations, and reasons for setbacks.

[0668] Output: The entered data is sent from the terminal to the server.

[0669] Specific operation: The user uses a smartphone or PC to input data through a dedicated application or web interface.

[0670] Step 2:

[0671] The server stores and manages the collected data.

[0672] Input: Data submitted by the user.

[0673] Output: Data stored in the database.

[0674] Specific operation: The server stores data using a relational database such as MySQL or PostgreSQL and performs validation to maintain data integrity.

[0675] Step 3:

[0676] The server analyzes the data using a generative AI model.

[0677] Input: Saved user data.

[0678] Output: The failure pattern as an analysis result.

[0679] How it works: The server uses Python and TensorFlow to build a machine learning model that analyzes users' past data to identify patterns of frustration.

[0680] Step 4:

[0681] The server generates personalized training plans and dietary advice based on the analysis results.

[0682] Input: Failure pattern as analysis result.

[0683] Output: Personalized training plans and nutritional advice.

[0684] Specific operation: Based on the user's failure patterns, the server suggests low-calorie snacks and an exercise plan that suits their lifestyle.

[0685] Step 5:

[0686] The server uses an emotion engine to monitor the user's emotional state and provide appropriate feedback.

[0687] Input: User input data and behavior patterns.

[0688] Output: Feedback depending on emotional state.

[0689] Specific operation: The server uses an emotion engine to monitor the user's emotional state in real time and suggests yoga or meditation to relax if the user is feeling stressed.

[0690] Step 6:

[0691] The server notifies the user of the generated plan and feedback.

[0692] Input: Generated training plans, dietary advice, and emotional feedback.

[0693] Output: Suggestions and support notified to the user.

[0694] Specific operation: The server sends a notification to the terminal, which receives it and displays it to the user. The user acts according to the proposed plan and re-enters the progress.

[0695] (Application example 3)

[0696] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0697] Conventional diet support systems were unable to fully utilize data on users' past failures, making it difficult to support sustained diet success. They also lacked feedback and advice to maintain users' motivation, making it difficult to promote long-term use. Furthermore, physical fitness gyms and diet cafes lacked the means to provide personalized training plans and meal menus.

[0698] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means using VR technology, means for providing personalized training and dietary advice using a generative AI, means for selling special nutritional foods and training equipment, means for analyzing a user's past diet history and failure patterns at a physical fitness gym or diet cafe and providing personalized training plans and meal menus, and means for providing feedback and advice to maintain the user's motivation using an emotion engine. This prevents the user from repeating the same failures, supports sustained diet success, and promotes long-term use.

[0699] "VR technology" is a technology that uses virtual reality to provide users with visual and experiential feedback.

[0700] "Generative AI" is a technology that uses artificial intelligence to analyze user data and generate personalized training and dietary advice.

[0701] "Personalized training and dietary guidance" means providing optimized training plans and meal menus based on individual user data.

[0702] "Special nutritional foods" are foods that contain specific nutritional ingredients for the purpose of dieting or maintaining health.

[0703] "Training equipment" refers to equipment used for fitness and exercise.

[0704] A "physical store" is a facility such as a fitness gym or diet cafe that exists in a physical location.

[0705] A "fitness gym" is a facility equipped with the necessary equipment for exercise and training.

[0706] A "diet cafe" is a cafe that provides healthy meals and drinks to support dieting.

[0707] "User's past diet history" refers to records and data of diets that the user has undertaken in the past.

[0708] "Failure patterns" are data that show the reasons and trends behind a user's past failures in dieting.

[0709] The "emotion engine" is a technology that analyzes the user's emotional state and provides feedback and advice to help them maintain motivation.

[0710] "Feedback" is an evaluation or advice provided regarding a user's behavior or status.

[0711] "Advice" is guidance or advice provided to help users achieve their goals.

[0712] To implement the present invention, the following system configuration and processing procedure are used.

[0713] System Configuration

[0714] 1. Hardware

[0715] Server: A high-performance server for analyzing data and running generative AI models.

[0716] Device: The smartphone, tablet, or computer you use.

[0717] Physical store equipment: Training equipment and nutritional food sales equipment installed in fitness gyms and diet cafes.

[0718] 2. Software

[0719] Generative AI model: An AI model that analyzes a user's past diet history and failure patterns to generate personalized training plans and meal menus.

[0720] Emotion Engine: Software that analyzes the user's emotional state and provides feedback and advice to help them stay motivated.

[0721] Data analysis tools: Data analysis libraries such as Pandas and Scikit-learn.

[0722] Processing steps

[0723] 1. Data collection and preprocessing

[0724] The server collects the user's past diet history and failure patterns, including data such as weight, calorie intake, and exercise time.

[0725] The collected data is preprocessed and standardized using Pandas.

[0726] 2. Clustering

[0727] The server uses Scikit-learn's KMeans algorithm to cluster user frustration patterns.

[0728] 3. Plan generation using generative AI

[0729] The server uses a generative AI model to generate personalized training plans and meal menus for each cluster.

[0730] An example of a specific prompt is, "Create a personalized training plan based on the user's cluster 1. The user has experienced setbacks in the past due to lack of exercise and poor diet management. Provide specific advice to help him achieve sustainable weight loss success."

[0731] 4. Feedback from the Emotion Engine

[0732] The server uses an emotion engine to analyze the user's emotional state and provide feedback and advice to help them maintain their motivation.

[0733] Specific examples

[0734] User A has tried to diet many times in the past, but always failed for similar reasons. Using this system, his past data can be analyzed and a training plan and meal menu optimized for him can be provided. For example, the generative AI model generates a specific plan using the prompt, "Create a personalized training plan based on the user's cluster 1. The user has previously failed due to lack of exercise and poor diet management. Please provide specific advice to help him succeed in dieting sustainably."

[0735] In this way, it is possible to prevent users from repeating the same setbacks, support their continued success in dieting, and promote long-term use.

[0736] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0737] Step 1:

[0738] The server collects the user's past diet history and failure patterns. Specifically, data such as weight, calorie intake, and exercise time entered by the user using a smartphone or tablet is stored in a database. The input data is saved in CSV files or database format.

[0739] Step 2:

[0740] The server preprocesses the collected data. Specifically, it reads the data using the Pandas library, fills in missing values, and removes outliers. It then standardizes the data using StandardScaler. The input is the user's raw data, and the output is the preprocessed standardized data.

[0741] Step 3:

[0742] The server performs clustering using the preprocessed data. Specifically, it uses Scikit-learn's KMeans algorithm to cluster user failure patterns. The input is the standardized data, and the output is the label of the cluster to which each user belongs.

[0743] Step 4:

[0744] The server uses a generative AI model to generate personalized training plans and meal menus for each cluster. Specifically, it generates prompts based on the cluster labels and inputs them into the AI ​​model using the OpenAI API. The inputs are the cluster labels and prompts, and the output is the generated training plans and meal menus.

[0745] Step 5:

[0746] The server uses an emotion engine to analyze the user's emotional state. Specifically, it analyzes the feedback and diary data entered by the user and estimates the emotional state. The input is the user's feedback data, and the output is the estimated emotional state.

[0747] Step 6:

[0748] The server provides feedback and advice to maintain the user's motivation based on the analysis results of the emotion engine. Specifically, it generates appropriate feedback messages according to the estimated emotional state and sends them to the user's device. The input is the estimated emotional state, and the output is the feedback message.

[0749] Step 7:

[0750] The user executes the personalized training plan and meal menu provided by the server and inputs feedback. Specifically, the user uses a smartphone or tablet to record their training progress and meal details and send them to the server. The input is the user's execution data, and the output is updated diet history data.

[0751] In this way, it is possible to prevent users from repeating the same setbacks, support their continued success in dieting, and promote long-term use.

[0752] 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.

[0753] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0754] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0755] 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.

[0756] [Second embodiment]

[0757] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0758] 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.

[0759] 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).

[0760] 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.

[0761] 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.

[0762] 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).

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0769] "Example 1"

[0770] The diet support system of the present invention combines VR technology and generative AI to provide users with personalized training and dietary advice. Specifically, the user wears a VR device at home and exercises according to the training program provided by the generative AI. The generative AI generates an optimal training menu based on information such as the user's body type, physical strength, and health condition. Similarly, the generative AI also provides dietary advice, taking into account the user's food preferences, health condition, and diet goals.

[0771] "Example 2"

[0772] The diet support system of the present invention also sells special nutritional foods and training equipment. These products work in conjunction with the training and dietary advice provided by the generation AI to more effectively support the user's diet. For example, based on the dietary advice provided by the generation AI, the system can recommend special nutritional foods and encourage their purchase. Similarly, the system can recommend training equipment optimized for the training program provided by the generation AI and encourage its purchase.

[0773] "Example 3"

[0774] The diet support system of this invention analyzes the user's past failure data and supports sustained diet success. Specifically, the generative AI analyzes the user's past diet history and failure patterns, and provides personalized training and dietary advice based on that. This prevents the user from repeating the same failures and supports sustained diet success.

[0775] The processing flow of each embodiment will be described below.

[0776] "Example 1"

[0777] Step 1: The user puts on the VR device at home.

[0778] Step 2: The AI ​​generates the optimal training menu based on the user's body type, physical strength, health condition, and other information.

[0779] Step 3: The user exercises according to the training program provided by the generated AI.

[0780] Step 4: The generative AI provides dietary advice taking into account the user's food preferences, health status, diet goals, etc.

[0781] "Example 2"

[0782] Step 1: Sell special nutritional foods and training equipment in conjunction with training and dietary advice provided by generative AI.

[0783] Step 2: Based on the dietary advice provided by the generation AI, recommend special nutritional foods and encourage their purchase.

[0784] Step 3: Recommend training equipment optimized for the training program provided by the generative AI and encourage its purchase.

[0785] "Example 3"

[0786] Step 1: The generative AI analyzes the user's past diet history and failure patterns.

[0787] Step 2: Generative AI provides personalized training and dietary advice based on the analysis results.

[0788] Step 3: This prevents users from repeating the same setbacks and supports sustained diet success.

[0789] Example 1

[0790] Next, a description will be given of Example 1 of Form 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."

[0791] Conventional diet support systems provide uniform training and dietary advice to users, without sufficient personalization based on each user's body type, physical strength, health condition, food preferences, and diet goals. Furthermore, there is a lack of ongoing support that takes into account users' past failures, resulting in a low success rate for dieting. Furthermore, sales of special nutritional foods and training equipment are not integrated into the system, which means users have to go through the trouble of purchasing the necessary items separately.

[0792] 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.

[0793] In this invention, the server includes a means for using VR technology, a means for providing personalized training and dietary advice using generation AI, a means for generating an optimal training menu based on the user's body type information, physical strength information, and health status, a means for generating an optimal meal plan based on the user's food preferences, health status, and diet goals, and a means for selling special nutritional foods and training equipment. This makes it possible to provide individually optimized training and dietary advice to users and provide continuous support taking into account past failure data. Furthermore, by providing the necessary nutritional foods and training equipment all in one place, user convenience can be improved.

[0794] "VR technology" is a technology that uses virtual reality to provide users with an immersive experience.

[0795] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate optimal training and meal plans for users.

[0796] "Personalized training" means providing a training menu that is individually optimized based on the user's body type information, physical strength information, and health condition.

[0797] "Personalized dietary guidance" refers to providing a meal plan that is individually optimized based on the user's food preferences, health status, and diet goals.

[0798] "Body type information" refers to physical data such as the user's height, weight, and body fat percentage.

[0799] "Physical fitness information" is physical data such as the user's exercise experience and current exercise habits.

[0800] "Health status" refers to data related to the user's health, such as allergies and chronic illnesses.

[0801] "Dietary preferences" is data about the user's food preferences, such as favorite and least favorite ingredients.

[0802] A "diet goal" is a goal that a user wants to achieve, such as losing weight or gaining muscle.

[0803] "Specialty nutritional foods" are foods specifically designed to support diet and training.

[0804] "Training equipment" refers to equipment or devices that a user uses to train.

[0805] "Past failure data" is data relating to the user's past experiences of failure in dieting or training.

[0806] "Continuous support" means providing long-term support to help users continue their diet and training.

[0807] MODE FOR CARRYING OUT THE INVENTION

[0808] This invention is a diet support system that combines VR technology and generative AI to provide users with personalized training and dietary advice. Specific embodiments of this system are described below.

[0809] System configuration

[0810] This system consists of a server, a terminal (VR device or smartphone), and a user. The server generates training menus and meal plans using a generative AI model and sends them to the terminal. The terminal sends information entered by the user to the server and displays the training menus and meal plans received from the server to the user.

[0811] Hardware and software used

[0812] The server is a high-performance computer with software installed to run generative AI models (e.g., OpenAI's GPT-4), and the terminal is a VR device or smartphone that provides an interface for users to input information and display training menus and meal plans.

[0813] Data processing and calculation

[0814] The server receives data sent by the user, such as body type information, physical strength information, health condition, food preferences, and diet goals, and uses a generative AI model to generate optimal training menus and meal plans. The generated menus and plans are sent to the device and displayed to the user.

[0815] Specific examples

[0816] Example 1: Generating a training menu

[0817] The user inputs information about their body type (height 170cm, weight 70kg, body fat percentage 20%) and physical fitness (no prior exercise experience, current exercise habit is walking once a week). Based on this information, the server uses a generative AI model to generate a training menu suited to the user. For example, the following prompt sentence is input to the generative AI model:

[0818] Prompt statement:

[0819] "Please create a training menu suitable for a 30-year-old man with a height of 170 cm, weight of 70 kg, body fat percentage of 20%, and no exercise experience."

[0820] Example 2: Generating a meal plan

[0821] The user inputs their food preferences (favorite ingredients are chicken and broccoli, least favorite ingredients are fish), health condition (no allergies, no chronic illnesses), and diet goal (weight loss). Based on this information, the server uses a generative AI model to generate a meal plan suitable for the user. For example, the following prompt sentence can be input into the generative AI model:

[0822] Prompt statement:

[0823] "Create a one-week meal plan suitable for a 30-year-old man whose favorite foods are chicken and broccoli, whose least favorite foods are fish, who has no allergies, and who is trying to lose weight."

[0824] In this way, the server, terminal, and user work together to create a system that provides personalized diet support.

[0825] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0826] Step 1:

[0827] Using a terminal (VR device or smartphone), the user inputs their body type information (height, weight, body fat percentage), physical fitness information (exercise experience, current exercise habits), and health status (allergies, chronic illnesses). This information is entered through the terminal's interface and sent to the server. The input data is stored in a database as the user's individual information.

[0828] Step 2:

[0829] The device sends the entered user information to the server. The server stores the received information in a database and generates prompts to input into the generative AI model. For example, based on the user's body type and physical strength information, it generates a prompt such as, "Please create a training menu suitable for a 30-year-old man with a height of 170 cm, a weight of 70 kg, a body fat percentage of 20%, and no exercise experience."

[0830] Step 3:

[0831] The server inputs the generated prompt sentences into the generative AI model to generate an optimal training menu for the user. The generative AI model analyzes the data based on the input prompt sentences and outputs a training menu. The output training menu is saved on the server.

[0832] Step 4:

[0833] The server sends the generated training menu to the device. The device displays the received training menu to the user. The user performs exercises according to the displayed training menu. The device tracks the user's movements in real time and provides feedback on correct form and movements.

[0834] Step 5:

[0835] Using a terminal, users input their food preferences (favorite and least favorite foods), health status (allergies, chronic illnesses), and diet goals (weight loss, muscle building). This information is entered through the terminal's interface and sent to the server. The input data is stored in a database as the user's individual information.

[0836] Step 6:

[0837] The server generates a meal plan using a generative AI model based on the stored dietary information. For example, based on the user's dietary preferences and health status, it generates a prompt such as, "Please create a one-week meal plan suitable for a 30-year-old man whose favorite ingredients are chicken and broccoli, whose least favorite ingredients are fish, who has no allergies, and who is trying to lose weight."

[0838] Step 7:

[0839] The server inputs the generated prompts into a generative AI model to generate an optimal meal plan for the user. The generative AI model analyzes the data based on the input prompts and outputs a meal plan. The output meal plan is saved on the server.

[0840] Step 8:

[0841] The server sends the generated meal plan to the device. The device displays the received meal plan to the user. The user eats according to the displayed meal plan. The device tracks whether the user is adhering to the meal plan and provides reminders and advice as needed.

[0842] In this way, the server, terminal, and user work together to create a system that provides personalized diet support.

[0843] (Application example 1)

[0844] Next, a description will be given of Application Example 1 of Form 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."

[0845] Conventional diet support systems often provide uniform training and dietary advice to users, lacking personalized support that fully considers each user's body type, physical strength, health condition, food preferences, and diet goals. Furthermore, dietary advice requires users to source and cook ingredients themselves, which is time-consuming and makes it difficult to achieve sustained diet success. Furthermore, the lack of sustained support utilizing data on users' past failures also contributes to the low success rate of dieting.

[0846] 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.

[0847] In this invention, the server includes means for using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, and means for proposing optimal meal menus based on information such as the user's body type, physical strength, health condition, food preferences, and diet goals, and ordering meals through a food delivery service. This allows users to be provided with individually optimized training and dietary advice, and further reduces the hassle of using the food delivery service, enabling them to achieve sustainable diet success.

[0848] "VR technology" is a technology that provides users with virtual reality, allowing them to experience a virtual environment that is different from the real world through their eyes and ears.

[0849] "Generative AI" is a technology that uses artificial intelligence to analyze user data and generate individually optimized training and dietary advice.

[0850] "Personalized training" means providing the optimal training menu based on individual information such as the user's body type, physical strength, and health condition.

[0851] "Dietary guidance" means proposing an appropriate meal menu taking into consideration the user's food preferences, health condition, diet goals, etc.

[0852] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health, and are nutritionally balanced.

[0853] "Training equipment" refers to equipment for exercise and fitness that supports users in improving their physical strength and muscle training.

[0854] A "food delivery service" is a service that delivers meals ordered by users to their homes, saving users time and effort.

[0855] "User's body type" refers to the shape and size of the user's body, which serves as the basis for creating diet and training plans.

[0856] "Physical strength" refers to the user's physical endurance and muscle strength, and is an important factor when deciding on a training menu.

[0857] "Health status" refers to the user's current health condition, which should be taken into consideration when providing training and dietary advice.

[0858] "Dietary preferences" refers to the foods and eating style that a user prefers, and is important information when providing personalized dietary advice.

[0859] "Diet goals" refer to the weight and body shape goals that a user wishes to achieve, and serve as a basis for creating training and dietary guidance plans.

[0860] As an embodiment of the present invention, the following system is constructed.

[0861] First, the server provides a means using VR technology. Specifically, by wearing a VR device, users can train in a virtual reality environment. This VR device provides a realistic training environment through visual and auditory senses, enhancing the user's sense of immersion.

[0862] Next, the server has a means to provide personalized training and dietary advice using generative AI. The generative AI generates optimal training and dietary menus based on information such as the user's body type, physical strength, health condition, food preferences, and diet goals. This is done by using OpenAI's API to analyze the user's individual data and provide the optimal plan.

[0863] Furthermore, the server has a means for selling special nutritional foods and training equipment, which allows users to easily purchase the nutritional foods and training equipment they need.

[0864] The server also suggests optimal meal menus based on the user's body type, physical strength, health condition, food preferences, and diet goals, and provides a means to order meals through a food delivery service, allowing users to easily receive healthy meals at home.

[0865] Specifically, the system first obtains user information, including data entered by the user via a smartphone or computer. Next, it sends prompts to a generative AI model, which then generates optimal training and meal plans. The following is an example of a prompt:

[0866] User's body type: Normal

[0867] User's stamina: Medium

[0868] Patient's health: Good

[0869] User's food preference: Japanese food

[0870] User's diet goal: weight loss

[0871] Use this information to create the optimal training and meal plan for you.

[0872] The generated menu is displayed to the user through a VR device, and the user can follow the menu to train and order meals using a food delivery service based on the menu.

[0873] This system allows users to receive individually optimized training and dietary advice, and also eliminates the hassle through food delivery services, making it possible for them to achieve sustainable diet success.

[0874] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0875] Step 1:

[0876] The user uses a smartphone or PC to input personal information such as body type, physical strength, health status, food preferences, and diet goals. This information is sent to the server. The input data includes the user's height, weight, exercise habits, and food preferences. The server receives this data and stores it in a database.

[0877] Step 2:

[0878] The server creates prompts for the generative AI model based on the saved user information. The prompts include information about the user's body type, physical strength, health status, food preferences, and diet goals. Specific examples of prompts are as follows:

[0879] User's body type: Normal

[0880] User's stamina: Medium

[0881] Patient's health: Good

[0882] User's food preference: Japanese food

[0883] User's diet goal: weight loss

[0884] Use this information to create the optimal training and meal plan for you.

[0885] Step 3:

[0886] The server sends the prompt to the generative AI model, which then generates the optimal training and meal menus for the user. The generative AI model then analyzes the prompt and generates the optimal training and meal menus for the user. The generated menus are then returned to the server.

[0887] Step 4:

[0888] The server sends the generated training and meal menus to the VR device. The user wears the VR device and trains in a virtual reality environment. The VR device provides a realistic training environment through visual and auditory channels, enhancing the user's sense of immersion.

[0889] Step 5:

[0890] The server orders meals through a food delivery service based on the generated meal menu. The user uses a smartphone or computer to check the proposed meal menu and confirm the order. The server then sends the order information to the food delivery service, and the meal is delivered to the user's home.

[0891] Step 6:

[0892] The user receives the delivered meal and eats according to the suggested meal menu, which allows the user to continue eating healthy while saving time and effort.

[0893] Step 7:

[0894] The server collects the user's training and diet history data and reflects it in the next training session or dietary advice, thereby understanding the user's progress and providing more effective diet support.

[0895] Example 2

[0896] Next, a description will be given of Example 2 of Form 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."

[0897] Conventional diet support systems have had the problem of being unable to provide effective diet support because it is difficult to provide personalized dietary advice and training programs for each user. Furthermore, there have been issues with the inability to properly recommend special nutritional foods and training equipment, resulting in a low success rate for users in dieting.

[0898] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for the user to input personal data using a terminal, a means for the server to receive and store the input data, a means for the server to analyze the data using a generated AI model and generate dietary advice and a training program, a means for the server to recommend special nutritional foods based on the generated dietary advice, a means for the server to recommend training equipment based on the generated training program, and a means for the terminal to display to the user the advice and recommended products received from the server. This makes it possible to provide personalized dietary advice and training programs to the user and to appropriately recommend special nutritional foods and training equipment, thereby improving the user's success rate in dieting.

[0899] "User" refers to an individual who uses the system to input personal data and receive dietary advice and training programs.

[0900] "Terminal" refers to the device used by the User to input personal data and receive and display advice and recommended products from the Server, including, for example, a smartphone or PC.

[0901] "Server" refers to the computer system that stores data received from users, analyzes the data using a generative AI model, and generates dietary advice and training programs.

[0902] A "generative AI model" refers to an artificial intelligence model that takes a user's personal data and prompt text as input and generates appropriate dietary advice and training programs.

[0903] "Dietary guidance" refers to providing advice on appropriate dietary content and nutritional intake methods based on the user's diet goals and health condition.

[0904] A "training program" refers to advice on appropriate exercise content and training methods based on the user's diet goals and exercise habits.

[0905] "Special nutritional foods" refer to foods that are recommended based on the user's dietary advice and are effective for dieting and maintaining health, such as protein bars and low-calorie smoothies.

[0906] "Training equipment" refers to equipment recommended for effective exercise and training based on a user's training program, including, for example, running machines and dumbbell sets.

[0907] "Advice" refers to specific instructions or recommendations regarding dietary guidance or training programs provided by a generative AI model based on a user's personal data.

[0908] "Recommended Products" refers to specialized nutritional foods or training equipment that the generative AI model recommends based on the user's dietary guidance and training program.

[0909] This invention is a diet support system in which a user inputs personal data using a terminal, and a server analyzes the data to provide personalized dietary advice and training programs. Specific embodiments of this system are described below.

[0910] First, the user accesses the system using a device such as a smartphone or PC. Using a dedicated application or web form, the user enters personal data such as height, weight, age, gender, exercise habits, and dietary details. For example, the user might enter "height 160 cm, weight 70 kg, age 30, gender female, exercise twice a week, bread and coffee for breakfast, salad for lunch, and fish and vegetables for dinner."

[0911] The device then sends the entered data to a server, which stores it using a relational database management system (RDBMS) such as MySQL or PostgreSQL. For example, it executes the following SQL query: INSERT INTO user_data (height, weight, age, gender, exercise_habits, diet) VALUES (160, 70, 30, 'female', '2 times a week', 'bread and coffee for breakfast, salad for lunch, fish and vegetables for dinner');

[0912] The server uses data analysis software such as Python or R to analyze the stored user data. The server uses a generative AI model (e.g., GPT-4) to generate optimal dietary advice and training programs for the user's diet goals. The generative AI model receives a prompt as input and generates appropriate advice. For example, the following prompt is input into the generative AI model:

[0913] "I'm a 30-year-old woman who weighs 70 kg and is 160 cm tall. I exercise twice a week. My goal is to lose 5 kg in three months. Please provide me with the optimal dietary advice and training program."

[0914] The generative AI model generates dietary advice based on this prompt. For example, it generates advice such as, "We recommend eating high-protein foods for breakfast and including plenty of vegetables at lunch and dinner. It is also recommended to choose nuts or yogurt as snacks." It also recommends, "We recommend purchasing protein bars and low-calorie smoothies."

[0915] Similarly, a generative AI model can generate a training program, such as "Perform 30 minutes of aerobic exercise (e.g., jogging or cycling) three times a week, and add strength training twice a week," and further recommend "We recommend purchasing a treadmill and a set of dumbbells."

[0916] Finally, the server sends the generated advice and recommended products to the device. The device then displays the advice and recommended products to the user using the application's dashboard or notification function. For example, the device may display specific advice and recommended products along with a message such as, "A dietary guide and training program based on your diet goals has been generated. Details are below."

[0917] In this way, users can receive personalized dietary advice and training programs, and receive appropriate recommendations for special nutritional foods and training equipment. This system can improve the user's success rate in dieting.

[0918] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0919] Step 1:

[0920] Users access the system using a terminal and enter their personal data. Specifically, they enter information such as height, weight, age, gender, exercise habits, and diet through a dedicated application or web form. For example, they might enter "height 160 cm, weight 70 kg, age 30, gender female, exercise twice a week, bread and coffee for breakfast, salad for lunch, and fish and vegetables for dinner." The input data is saved on the terminal in JSON format or as form data.

[0921] Step 2:

[0922] The device sends the entered data to the server. Specifically, it uses an HTTP POST request to send user data to the server. For example, it sends JSON-formatted data to the endpoint " / submitUserData". The server stores the received data in a relational database management system (RDBMS) such as MySQL or PostgreSQL. For example, it executes the following SQL query: "INSERT INTO user_data (height, weight, age, gender, exercise_habits, diet) VALUES (160, 70, 30, 'female', '2 times a week', 'bread and coffee for breakfast, salad for lunch, fish and vegetables for dinner');".

[0923] Step 3:

[0924] The server analyzes the stored user data. Specifically, it uses data analysis software such as Python or R to calculate the user's BMI (Body Mass Index) and calorie consumption. For example, to calculate BMI, it uses the formula "BMI = weight / (height / 100)^2." The analysis results are used to generate prompt sentences to input into the generative AI model.

[0925] Step 4:

[0926] The server uses a generative AI model (e.g., GPT-4) to generate optimal dietary advice and training programs for the user's diet goals. Specifically, a prompt sentence is input into the generative AI model, which then generates appropriate advice. For example, the following prompt sentence is input into the generative AI model: "I am a 30-year-old woman, weighing 70 kg, standing 160 cm tall, and exercising twice a week. My diet goal is to lose 5 kg in three months. Please provide me with the optimal dietary advice and training program." The generative AI model then generates dietary advice and training programs based on this prompt sentence.

[0927] Step 5:

[0928] The server recommends specific nutritional foods based on the generated dietary advice. Specifically, it selects nutritional foods suitable for the user based on the dietary advice provided by the generative AI model. For example, it may give advice such as "We recommend that you eat high-protein foods for breakfast and include plenty of vegetables for lunch and dinner. It is also recommended that you choose nuts or yogurt as snacks," along with a recommendation such as "We recommend that you purchase protein bars and low-calorie smoothies."

[0929] Step 6:

[0930] The server recommends training equipment based on the generated training program. Specifically, it selects training equipment suitable for the user based on the training program provided by the generative AI model. For example, it advises, "Perform 30 minutes of aerobic exercise (e.g., jogging or cycling) three times a week, and add strength training twice a week," along with a recommendation that, "We recommend purchasing a treadmill and a set of dumbbells."

[0931] Step 7:

[0932] The server sends the generated advice and recommended products to the device. Specifically, it sends the generated advice and recommended products to the device using an HTTP response. The device displays the received information to the user using the application's dashboard or notification function. For example, it displays the specific advice and recommended products along with a message saying, "A dietary guide and training program based on your diet goals has been generated. Details are as follows."

[0933] (Application example 2)

[0934] Next, a description will be given of Application Example 2 of Form 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."

[0935] Conventional diet support systems have had problems such as difficulty in providing optimal dietary advice and training programs for individual users, and ineffective recommendations of special nutritional foods and training equipment. Furthermore, there is a lack of support for sustained diet success that takes into account the user's past failure data, resulting in a low long-term diet success rate.

[0936] 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.

[0937] In this invention, the server includes a means for collecting user data, a means for analyzing the data using a generation AI to generate optimal dietary advice and training programs, and a means for recommending and encouraging the purchase of special nutritional foods and training equipment based on the programs provided by the generation AI. This allows for the provision of optimal diet plans for individual users and the effective recommendation of special nutritional foods and training equipment. Furthermore, by analyzing the user's past failure data, it is possible to support sustained diet success.

[0938] "User data" refers to information such as the user's diet goals, current physical condition, eating habits, and exercise habits.

[0939] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate optimal dietary advice and training programs.

[0940] "Dietary guidance" refers to suggesting appropriate meal content and meal timing based on the user's health condition and diet goals.

[0941] A "training program" refers to suggesting appropriate exercise content and frequency based on the user's physical strength and diet goals.

[0942] "Nutritional foods" refer to foods specifically designed for weight loss or health maintenance.

[0943] "Training equipment" refers to any equipment or device used to effectively exercise or train.

[0944] "Recommendation" refers to the generative AI suggesting specific nutritional foods or training equipment to users based on the analysis results.

[0945] "Encouraging purchases" refers to making it easy for users to purchase recommended nutritional foods and training equipment.

[0946] "Past failure data" refers to information about the user's past failed diet attempts and the reasons for them.

[0947] "Sustained diet success" refers to a user continuing a diet over a long period of time and achieving their goal.

[0948] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[0949] System Configuration

[0950] 1. Collection of User Data

[0951] Hardware: Smartphone

[0952] Software: Application user interface

[0953] Data: Your diet goals, current physical condition, eating habits, and exercise habits

[0954] 2. Data analysis using generative AI

[0955] Hardware: Cloud Server

[0956] Software: Generative AI models (e.g., GPT-4)

[0957] Data processing: Analyze user data and generate optimal dietary advice and training programs

[0958] 3. Selection of recommended products

[0959] Hardware: Cloud Server

[0960] Software: Product database, recommendation engine

[0961] Data calculation: Selecting special nutritional foods and training equipment based on programs provided by generative AI

[0962] 4. Suggestions for users

[0963] Hardware: Smartphone

[0964] Software: Application user interface

[0965] Data Display: Display of recommended dietary advice, training programs, and related products

[0966] 5. Purchase Process

[0967] Hardware: Smartphone

[0968] Software: Electronic payment system

[0969] Data processing: processing and verifying purchases

[0970] Processing Description

[0971] 1. Collection of User Data:

[0972] Through a smartphone application, users input information such as their diet goals, current physical condition, eating habits, exercise habits, etc. This data is sent to a cloud server and stored for analysis by the generative AI.

[0973] 2. Generative AI data analysis:

[0974] A generative AI model (e.g., GPT-4) on a cloud server analyzes the collected user data and generates optimal dietary and training programs for each individual user, taking into account the user's past failure data.

[0975] 3. Recommended product selection:

[0976] Based on the program provided by the generative AI, a recommendation engine on the cloud server selects special nutritional foods and training equipment, thereby recommending the best products for users.

[0977] 4. Suggestions for users:

[0978] Through a smartphone application, users are provided with personalized dietary advice, training programs, and recommended nutritional foods and training equipment.

[0979] 5. Purchasing process:

[0980] Users can easily purchase recommended nutritional foods and training equipment through the application, with the purchase process handled using an electronic payment system.

[0981] Specific examples

[0982] The user opens the application and enters the following information:

[0983] Diet goal: Lose 5kg

[0984] Current health condition: Healthy

[0985] Eating habits: 3 meals a day, snacks

[0986] Exercise habits: Jogging twice a week

[0987] The generative AI analyzes this data and provides dietary advice and training programs such as:

[0988] Dietary advice: High-protein, low-calorie diet, snacks changed to nuts

[0989] Training program: Add strength training three times a week

[0990] Additionally, the following specialized nutritional and training equipment is recommended:

[0991] Nutritional foods: high protein bars, protein shakes

[0992] Training equipment: dumbbell set, yoga mat

[0993] Prompt Sentence Examples

[0994] "Generate optimal dietary advice and training programs based on the user's diet goals, current physical condition, eating habits, and exercise habits, and recommend nutritional foods and training equipment optimized for them."

[0995] This way, users can easily find and purchase the diet plan and related products that best suit them.

[0996] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0997] Step 1:

[0998] Users open the application on their smartphone and enter information such as their diet goals, current physical condition, eating habits, and exercise habits.

[0999] Input: diet goal, current physical condition, eating habits, exercise habits

[1000] Output: User data

[1001] Specific behavior: The user enters the required information into the application's input form and presses the submit button.

[1002] Step 2:

[1003] The device sends the collected user data to a cloud server.

[1004] Input: User data

[1005] Output: User data stored on cloud servers

[1006] Specific behavior: The application calls an API to send user data to the cloud server.

[1007] Step 3:

[1008] The server uses a generative AI model (e.g., GPT-4) to analyze the collected user data and generate optimal dietary advice and training programs.

[1009] Input: User data

[1010] Output: Dietary advice, training program

[1011] How it works: A generative AI model on a cloud server analyzes user data and generates optimal dietary advice and training programs based on prompts.

[1012] Step 4:

[1013] The server selects special nutritional foods and training equipment based on a program provided by the generated AI.

[1014] Input: dietary advice, training program

[1015] Output: Recommended nutrition, training equipment

[1016] Specific operation: A recommendation engine on a cloud server selects the optimal nutritional foods and training equipment from a product database based on dietary advice and training programs.

[1017] Step 5:

[1018] The server provides recommended dietary advice, training programs, and related products to the user.

[1019] Input: Recommended nutrition, training equipment

[1020] Output: Suggestions to the user

[1021] Specific operation: The cloud server sends the generated suggestions to the smartphone application and displays them to the user.

[1022] Step 6:

[1023] Users purchase recommended nutritional foods and training equipment through the application.

[1024] Input: User's purchase intent

[1025] Output: Purchase confirmation, payment completed

[1026] Specific action: The user presses the purchase button and completes payment through an electronic payment system.

[1027] This way, users can easily find and purchase the diet plan and related products that best suit them.

[1028] Example 3

[1029] Next, a description will be given of Example 3 of Form Example 3. 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."

[1030] Conventional diet support systems were unable to fully utilize data on users' past failures, making it difficult to support sustained diet success. Furthermore, because they did not provide personalized training or dietary advice, users were likely to repeat the same setbacks. Furthermore, they lacked a mechanism for effectively collecting and analyzing user feedback and updating plans, which led to problems with the effects of dieting not being sustained.

[1031] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1032] In this invention, the server includes means for collecting user data, means for saving and managing the collected data, means for using a generative AI model to analyze the saved data, means for generating personalized training and dietary advice based on the analysis results, means for delivering the generated plan to the user, means for collecting user feedback, and means for analyzing the collected feedback and updating the plan. This makes it possible to utilize data on the user's past failures to support sustained diet success.

[1033] "User data" refers to information entered by the user, such as past diet history, failure patterns, current weight, target weight, dietary content, and exercise habits.

[1034] The "means of collection" refers to the system in which users enter data using a smartphone app or web form and send it to a server.

[1035] "Means for storing and managing" refers to a mechanism by which the server stores the data it receives in a database and manages it appropriately.

[1036] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates personalized training and dietary advice.

[1037] The "means of analysis" refers to a mechanism that uses a generative AI model to analyze stored data and identify users' past patterns of failure and success.

[1038] "Personalized training and dietary guidance" refers to individually customized training plans and dietary advice based on the analysis results.

[1039] The "means of delivery" refers to a mechanism for sending the generated training plan and dietary advice from the server to the terminal and notifying the user.

[1040] The "means of collecting feedback" is a system in which users enter their daily progress and new data into their devices and send it to the server.

[1041] The "means of analyzing feedback and updating plans" refers to a mechanism that re-enters the collected feedback into the generative AI model and updates the training plan and dietary advice based on the analysis results.

[1042] This invention is a system that analyzes data on users' past failures and supports their continued success in dieting. Specific embodiments of this system will be described below.

[1043] Collection of User Data

[1044] Using a smartphone app or web form, users enter information such as their past diet history, failure patterns, current weight, goal weight, dietary habits, exercise habits, etc. For example, a user might enter, "In the past, I often ate sweets late at night."

[1045] Data storage and management

[1046] The terminal sends the data entered by the user to the server, which then stores the received data in a database (e.g., MySQL or PostgreSQL) and manages it appropriately.

[1047] Data analysis

[1048] The server inputs the stored data into a generative AI model (for example, OpenAI's GPT-4) and performs analysis. Specifically, it identifies the user's past patterns of failure and success and obtains data-based insights. For example, the analysis may yield results such as, "This user has a tendency to eat sweets late at night."

[1049] Generate a personalized plan

[1050] The server uses the generative AI model to generate optimal training plans and dietary advice for the user. Specifically, it takes into account the user's past data and provides individually customized advice. For example, it generates specific advice such as "Prepare low-calorie snacks to suppress appetite late at night."

[1051] Plan Delivery

[1052] The server sends the generated training plan and dietary advice to the device. The device then displays the received plan to the user. Specifically, the device provides information using the smartphone app's notification function or dashboard. For example, the device may notify the user, "Prepare a low-calorie snack to suppress your appetite late at night."

[1053] Collecting user feedback

[1054] Users input their daily progress and new data (e.g., weight changes and dietary information) into the device. Specifically, they input their daily food and exercise records into the app. For example, they might input, "I had a low-calorie snack late at night today."

[1055] Analyzing feedback and updating plans

[1056] The device sends the new data entered by the user to the server. The server then inputs the received data into the generative AI model again and analyzes it. Based on the analysis results, the training plan and dietary advice are updated and sent again to the device. For example, if the analysis result shows that "low-calorie snacks are effective," the training plan and dietary advice are updated.

[1057] Prompt Sentence Examples

[1058] "Generate optimal training plans and dietary advice for the user based on their past diet history and failure patterns. The user has a tendency to eat sweets late at night. Please provide specific advice taking this failure pattern into consideration."

[1059] In this way, the diet support system utilizes the user's past data to support continuous diet success. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1060] Step 1:

[1061] Users use a smartphone app or web form to enter information such as their past diet history, failure patterns, current weight, target weight, dietary habits, and exercise habits.

[1062] Input: past diet history, failure patterns, current weight, goal weight, dietary content, exercise habits

[1063] Output: User data entered

[1064] Step 2:

[1065] The terminal transmits the data entered by the user to the server.

[1066] Input: User data entered by the user

[1067] Output: User data sent to the server

[1068] Step 3:

[1069] The server stores the received data in a database and manages it appropriately, using a database management system such as MySQL or PostgreSQL.

[1070] Input: User data sent to the server

[1071] Output: User data stored in the database

[1072] Step 4:

[1073] The server inputs the stored data into a generative AI model (such as OpenAI's GPT-4) for analysis, specifically identifying the user's past patterns of failure and success and deriving insights from the data.

[1074] Input: User data stored in the database

[1075] Output: Analysis results (e.g., "This user tends to eat sweets late at night")

[1076] Step 5:

[1077] The server uses the generative AI model to generate optimal training plans and dietary advice for each user, taking into account the user's past data and providing individually customized advice.

[1078] Input: Analysis results

[1079] Output: Personalized training plan and dietary advice (e.g., "Pack a low-calorie snack to curb late-night cravings")

[1080] Step 6:

[1081] The server sends the generated training plan and dietary advice to the device, which then displays the received plan to the user. Specifically, the information is provided using the smartphone app's notification function and dashboard.

[1082] Input: Personalized training plans and nutrition advice

[1083] Output: A training plan or dietary advice displayed to the user (e.g., "Pack a low-calorie snack to curb your appetite later in the night")

[1084] Step 7:

[1085] The user inputs their daily progress and new data (e.g., weight changes and dietary information) into the device. Specifically, they input their daily diet and exercise records into the app.

[1086] Input: Daily progress and new data (e.g., "I had a low-calorie snack late at night today")

[1087] Output: New data entered

[1088] Step 8:

[1089] The device sends the new data to the server. The server then inputs the received data back into the generative AI model for analysis. Based on the analysis results, the training plan and dietary advice are updated and sent back to the device.

[1090] Input: New data

[1091] Output: Updated training plan and dietary advice (e.g., "Low-calorie snacks are effective")

[1092] In this way, the diet support system utilizes the user's past data to support sustained diet success.

[1093] (Application example 3)

[1094] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1095] Conventional diet support systems do not provide personalized guidance that takes into full consideration the user's past diet history or failure patterns, which means that users often end up repeating the same setbacks. Furthermore, because they do not sell special nutritional foods or training equipment, it is difficult for users to obtain the resources they need in one place. Furthermore, they lack the functionality to suggest optimal meal menus for users, making it difficult to support sustained diet success.

[1096] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, and means for analyzing the user's past diet history and failure patterns and proposing personalized meal menus. This makes it possible to prevent the user from repeating the same failures and support continuous diet success.

[1097] "VR technology" is a technology that uses virtual reality to provide users with a visual and experiential training environment.

[1098] "Generative AI" is an artificial intelligence technology that analyzes a user's past data and generates personalized training and dietary advice.

[1099] "Personalized training and dietary guidance" means providing customized training and dietary plans based on the user's individual needs and past history.

[1100] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health.

[1101] "Training equipment" refers to equipment used by users for physical training.

[1102] "User's past diet history" refers to records and data of diets that the user has undertaken in the past.

[1103] "Failure patterns" refer to the causes and tendencies that led users to abandon or fail diets in the past.

[1104] A "personalized meal menu" is a meal plan customized based on the user's past diet history and failure patterns.

[1105] A system for carrying out this invention is configured as follows: The server includes means for using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, and means for analyzing a user's past diet history and failure patterns and proposing a personalized meal menu.

[1106] The server first retrieves the user's past diet history and failure patterns from a database. This data includes records of the user's past diet attempts and the reasons for their failures. Next, it uses a generative AI to analyze this data and generate optimal training and meal plans for the user. The generative AI can use OpenAI's API.

[1107] Specifically, the server inputs the following prompt to the generation AI:

[1108] Analyze the user's past diet history and failure patterns to suggest a personalized meal menu.

[1109] User data: {'age': 30, 'gender': 'female', 'height': 160, 'weight': 65, 'diet_history': [{'date': '2022-01-01', 'weight': 70, 'calories': 2000, 'exercise': 'running', 'success': False}, {'date': '2022-02-01', 'weight': 68, 'calories': 1800, 'exercise': 'yoga', 'success': True}], 'failure_patterns': ['stress eating', 'lack of motivation']}

[1110] Based on this prompt, the AI ​​generates the optimal meal menu for the user, which is then displayed on the user's smartphone or tablet.

[1111] In addition, the server also sells special nutritional foods and training equipment, allowing users to centrally purchase the nutritional foods and training equipment they need based on the generated meal menu.

[1112] This system will help users avoid repeating past setbacks and support their continued success in dieting. It will also make it easier for them to continue their diet by providing all the necessary resources in one place.

[1113] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1114] Step 1:

[1115] The server retrieves the user's past diet history and failure patterns from a database. The input is the user's ID, and the output is the user's past diet history and failure patterns. This data includes the user's past diet records and the reasons for failure.

[1116] Step 2:

[1117] The server generates a prompt sentence to be input to the generation AI based on the acquired data. The input is data on the user's past diet history and failure patterns, and the output is a prompt sentence to be input to the generation AI. Specifically, the server generates a prompt sentence that includes information such as the user's age, gender, height, weight, past diet history, and failure patterns.

[1118] Step 3:

[1119] The server sends the generated prompt to the generation AI to generate a personalized meal menu. The input is the generated prompt, and the output is the personalized meal menu returned by the generation AI. The generation AI uses OpenAI's API to generate the optimal meal menu for the user.

[1120] Step 4:

[1121] The server sends the generated meal menu to the user's smartphone or tablet. The input is the personalized meal menu returned by the generation AI, and the output is the meal menu displayed on the user's device. The user can check the generated meal menu through their smartphone or tablet.

[1122] Step 5:

[1123] The server sells special nutritional foods and training equipment. The input is the generated meal menu and the user's purchase request, and the output is a link or information for the user to purchase. The user can centrally purchase the nutritional foods and training equipment they need based on the generated meal menu.

[1124] Step 6:

[1125] The user goes on a diet according to the provided meal menu and training plan. The input is the meal menu and training plan provided by the server, and the output is the user's diet progress data. The user can receive further personalized guidance by feeding the progress data back to the server.

[1126] 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.

[1127] "Example 1"

[1128] One embodiment of the present invention is a diet support system incorporating an emotion engine. In this system, a user wears a VR device and receives personalized training and dietary advice provided by a generation AI. The emotion engine recognizes the user's emotional state from their facial expressions, tone of voice, and choice of words, and feeds that information back to the generation AI. The generation AI then adjusts the training menu and dietary advice based on this emotional information. For example, if it determines that the user is feeling stressed, it will suggest a training menu with a relaxation effect or recommend a meal that is effective for relieving stress.

[1129] "Example 2"

[1130] In addition, recommendations for special nutritional foods and training equipment are also adjusted according to the user's emotional state. For example, if the system determines that the user is feeling depressed, it will recommend nutritional foods that will cheer them up or training equipment that will help them change their mood. This makes it possible to provide optimal diet support according to the user's emotional state.

[1131] "Example 3"

[1132] Furthermore, this system will be offered on a monthly fee basis, with the aim of expanding its scale and achieving sales of over 10 billion yen in the future. By incorporating an emotion engine, it will be possible to increase user satisfaction and encourage long-term use. This will ensure stable revenue and enable large-scale business development.

[1133] The processing flow of each embodiment will be described below.

[1134] "Example 1"

[1135] Step 1: The user puts on the VR device.

[1136] Step 2: Generative AI provides users with personalized training and dietary advice.

[1137] Step 3: The emotion engine recognizes the user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[1138] Step 4: The emotion engine feeds that information back to the generative AI.

[1139] Step 5: The generative AI adjusts the training menu and dietary advice based on this emotional information.

[1140] "Example 2"

[1141] Step 1: The emotion engine determines that the user's emotional state is depressed.

[1142] Step 2: Generative AI recommends nutritious foods to boost your energy.

[1143] Step 3: The generative AI recommends training equipment that will provide a refreshing change of pace.

[1144] "Example 3"

[1145] Step 1: The system is offered on a monthly subscription basis.

[1146] Step 2: Incorporating an emotional engine increases user satisfaction and encourages long-term usage.

[1147] Step 3: Secure stable profits and realize large-scale business expansion.

[1148] Example 1

[1149] Next, a description will be given of Example 1 of Form 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."

[1150] Conventional diet support systems do not adequately provide personalized training and dietary advice based on the user's individual body type, physical strength, and health condition. Furthermore, they do not adjust the training and dietary advice to take into account the user's emotional state, making it difficult to achieve sustained diet success. Furthermore, they lack comprehensive support that combines the sale of special nutritional foods and training equipment.

[1151] 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.

[1152] In this invention, the server includes a means for using VR technology, a means for providing personalized training and dietary advice using a generating AI, a means for analyzing the user's emotional state using an emotion engine and feeding that information back to the generating AI, and a means for selling special nutritional foods and training equipment. This not only provides personalized training and dietary advice based on the user's individual body type, physical strength, and health condition, but also makes it possible to make adjustments taking into account the user's emotional state, supporting sustainable diet success. Furthermore, by combining the sale of special nutritional foods and training equipment, comprehensive diet support is realized.

[1153] "VR technology" is a technology that uses virtual reality to provide users with an immersive experience.

[1154] "Generative AI" is a technology that uses artificial intelligence to analyze user data and generate personalized training and dietary advice.

[1155] "Personalized training" refers to a training menu that is individually optimized based on the user's body type, physical strength, and health condition.

[1156] "Dietary advice" refers to dietary advice provided based on a user's dietary preferences, health status, and dietary goals.

[1157] The "emotion engine" is a technology that analyzes a user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[1158] "Feedback" is the process by which the system provides appropriate actions to the user based on analyzed information.

[1159] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health.

[1160] "Training equipment" refers to equipment that a user uses to train.

[1161] "Sales" is the act of providing goods or services and receiving payment in return.

[1162] "Sustained weight loss success" refers to achieving and maintaining weight loss goals over an extended period of time.

[1163] The diet support system of the present invention is a system that combines VR technology and generative AI to provide users with personalized training and dietary advice. Specific embodiments of this system are described below.

[1164] Hardware and software used

[1165] Hardware: VR device

[1166] Software: Generative AI models (e.g., OpenAI GPT-4), emotion engines

[1167] Program processing

[1168] Data collection and analysis

[1169] When a user wears a VR device, the system collects the user's motion data and environmental data in real time. The server collects basic information such as the user's body type (height, weight), physical strength, and health condition (e.g., blood pressure, heart rate). This information is either entered by the user in advance or obtained from the wearable device.

[1170] Generative AI model for generating training menus

[1171] The server sends a prompt to the generative AI model based on the collected user information. For example, it sends a prompt such as, "Please generate the optimal training menu for a user who is 170 cm tall, weighs 70 kg, and has moderate physical strength." The generative AI model receives the prompt and generates the optimal training menu for the user. For example, it generates a menu that combines 30 minutes of aerobic exercise and strength training.

[1172] Providing training menus

[1173] The server sends the training menu received from the generative AI model to the user's device (VR device). The user checks the training menu through the VR device and starts exercising. The VR device tracks the user's movements in real time and provides feedback.

[1174] Emotional state analysis using emotion engine

[1175] The server uses an emotion engine to analyze the user's emotional state based on their facial expressions, tone of voice, and choice of words. For example, it can determine whether the user is feeling stressed. The analyzed emotional information is fed back to the generative AI model.

[1176] Adjustment based on emotional information

[1177] The generative AI model adjusts training menus and dietary advice based on the emotional information fed back. For example, it might suggest a yoga menu with a relaxing effect or recommend a meal that is effective for relieving stress. The server then sends the adjusted training menu and dietary advice to the user's device.

[1178] Specific examples

[1179] Generate a training menu:

[1180] User's body data: Height 170cm, Weight 70kg

[1181] User's stamina data: Medium stamina

[1182] Based on this data, the generative AI generates a menu that combines 30 minutes of aerobic exercise and strength training.

[1183] Dietary advice provided:

[1184] User's dietary preferences: Likes vegetables, less meat

[1185] User's health condition: High blood pressure

[1186] Based on this data, the generative AI suggests low-salt, vegetable-based meal menus.

[1187] Emotion Engine Feedback:

[1188] The emotion engine recognizes when a user is feeling stressed

[1189] The generative AI suggests relaxing yoga exercises and recommends foods that are effective in relieving stress (e.g., dark chocolate and nuts).

[1190] Prompt Sentence Examples

[1191] "Generate the optimal training menu based on the user's body type and physical fitness data."

[1192] "Provide personalized dietary advice that takes into account the user's dietary preferences and health status."

[1193] "Recognize your emotional state and adjust your training and dietary recommendations accordingly."

[1194] The above is a specific embodiment of the diet support system of the present invention.

[1195] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1196] Step 1:

[1197] The user puts on the VR device.

[1198] Specifically, the user wears the VR device at home and logs in to the system, which then prepares the system to collect user motion and environmental data in real time.

[1199] Input: User login information, VR device connection

[1200] Output: Start collecting user behavior data and environmental data

[1201] Step 2:

[1202] The server collects basic information about the user.

[1203] Specifically, the server collects basic information about the user, such as their body type (height, weight), physical strength, and health status (e.g., blood pressure, heart rate), which can be entered by the user in advance or obtained from the wearable device.

[1204] Input: User basic information, wearable device data

[1205] Output: Collected user basic information

[1206] Step 3:

[1207] The server sends a prompt to the generative AI model.

[1208] Specifically, the server sends a prompt to the generative AI model based on the collected user information, such as "Please generate the optimal training menu for a user who is 170 cm tall, weighs 70 kg, and has moderate physical strength."

[1209] Input: User's basic information, prompt text

[1210] Output: Sending a prompt to the generative AI model

[1211] Step 4:

[1212] A generative AI model generates a training menu.

[1213] Specifically, a generative AI model (e.g., OpenAI GPT-4) receives a prompt and generates an optimal training menu for the user, such as a 30-minute combination of cardio and strength training.

[1214] Input: prompt statement

[1215] Output: Generated training menu

[1216] Step 5:

[1217] The server transmits the generated training menu to the terminal.

[1218] Specifically, the server sends the training menu received from the generative AI model to the user's device (VR device), where the user can view the training menu.

[1219] Input: Generated training menu

[1220] Output: Sending the training menu to the user's device

[1221] Step 6:

[1222] The user begins training.

[1223] Specifically, the user begins exercising according to the training menu provided through the VR device, which tracks the user's movements in real time and provides feedback.

[1224] Input: Training Menu

[1225] Output: User training data, feedback

[1226] Step 7:

[1227] The server analyzes the user's emotional state using an emotion engine.

[1228] Specifically, the server uses an emotion engine to analyze the user's emotional state based on their facial expressions, tone of voice, and choice of words, for example, to determine whether they are feeling stressed.

[1229] Input: User facial expression data, tone of voice, and word choice

[1230] Output: Parsed emotion information

[1231] Step 8:

[1232] The server feeds emotional information back to the generative AI model.

[1233] Specifically, the server feeds back the analyzed emotional information to the generative AI model, for example, sending information that the user is feeling stressed.

[1234] Input: Parsed emotion information

[1235] Output: Feedback to the generative AI model

[1236] Step 9:

[1237] The generative AI model adjusts training menus and dietary advice based on emotional information.

[1238] Specifically, the generative AI model adjusts training menus and dietary advice based on the emotional information fed back to it, for example, by suggesting yoga menus with a relaxing effect or recommending meals that are effective for relieving stress.

[1239] Input: Feedback emotional information

[1240] Output: Tailored training menus and dietary advice

[1241] Step 10:

[1242] The server sends tailored menus and instructions to the terminal.

[1243] Specifically, the server sends the adjusted training menu and dietary advice to the user's device, and the user receives the new menu and advice through the VR device.

[1244] Input: Tailored training menus and dietary advice

[1245] Output: sent to the user's terminal

[1246] (Application example 1)

[1247] Next, a description will be given of Application Example 1 of Form 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."

[1248] Conventional diet support systems do not provide personalized training or dietary advice that fully considers the individual needs and emotional state of the user. Furthermore, few systems are designed for use in brick-and-mortar stores, limiting the opportunities for users to directly experience the system. Furthermore, there is no system that recognizes the user's emotional state in real time and provides guidance accordingly, making it difficult to maintain user motivation.

[1249] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means using VR technology, means for providing personalized training and dietary advice using a generation AI, means for selling special nutritional foods and training equipment, means for recognizing the user's emotional state using an emotion engine and feeding that information back to the generation AI, and means for the user to wear a VR device in a physical store and receive personalized training and dietary advice provided by the generation AI. This enables personalized training and dietary advice tailored to the user's individual needs and emotional state, making it easier to maintain the user's motivation through the experience at the physical store.

[1250] "VR technology" is a technology for realizing virtual reality, allowing users to immerse themselves in a virtual environment.

[1251] "Generative AI" is a technology that uses artificial intelligence to automatically generate optimal training and dietary advice for users.

[1252] "Personalized training and dietary guidance" means providing a training program and / or meal plan that is customized based on the user's individual needs and condition.

[1253] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health.

[1254] "Training equipment" refers to equipment used for fitness and exercise.

[1255] The "emotion engine" is a technology that recognizes the user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[1256] A "physical store" is a store that exists in a physical location and where customers can visit in person to receive services.

[1257] A "VR device" is a hardware device for experiencing virtual reality, including a head-mounted display and controllers.

[1258] "Feedback" means that the system adjusts the content of training and dietary advice based on information it receives from the user.

[1259] This invention is a system in which users wear a VR device in a physical store and receive personalized training and dietary advice provided by a generating AI. Specific embodiments for realizing this system are described below.

[1260] System configuration

[1261] Hardware

[1262] VR devices: use head-mounted displays, which allow users to immerse themselves in a virtual reality environment.

[1263] Camera: Used to capture the user's facial expressions in real time, which allows the emotion engine to recognize the user's emotional state.

[1264] Server: Provides the computational resources to run the generative AI model and generate training and dietary advice.

[1265] software

[1266] Generative AI model: An artificial intelligence model that generates training and dietary advice based on the user's individual needs and condition.

[1267] Emotion engine: Software that recognizes the user's emotional state from facial expressions, tone of voice, choice of words, etc.

[1268] VR application: An application that allows users to receive training and dietary advice through a VR device.

[1269] System Operation

[1270] 1. Acquisition of user information: The server acquires information such as the user's body type, physical strength, health condition, food preferences, and diet goals.

[1271] 2. Emotional state recognition: The camera captures the user's facial expressions, and the emotion engine analyzes the data to recognize the emotional state.

[1272] 3. Generating training and dietary advice: The generative AI model generates optimal training menus and dietary advice based on the acquired user information and emotional state.

[1273] 4. Display on VR device: The generated training menu and dietary advice are displayed on the VR device, and the user experiences it in a virtual reality environment.

[1274] Specific examples

[1275] For example, consider the case where the user information is as follows:

[1276] Body Type: Standard

[1277] Physical fitness: moderate

[1278] Health: Good

[1279] Food preference: Japanese food

[1280] Diet goal: Weight loss

[1281] Based on this information, the generative AI model generates a prompt like this:

[1282] The user has a normal build, is moderately fit, and is in good health. He or she enjoys Japanese food and is trying to lose weight. The user is currently feeling stressed. Based on this information, generate an optimal training menu and dietary advice.

[1283] By inputting this prompt into a generative AI model, the system generates a training menu and dietary advice that is optimal for the user. The generated content is then provided to the user via a VR device, allowing them to receive training and dietary advice in a virtual reality environment.

[1284] This allows for personalized training and dietary advice tailored to each user's individual needs and emotional state, making it easier to maintain user motivation through the physical store experience.

[1285] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1286] Step 1:

[1287] The server acquires information about the user, such as their body type, physical strength, health status, food preferences, and diet goals. This information is collected from data previously entered by the user and from past history data. The input data is saved as the user's profile information and used as input for the generative AI model.

[1288] Step 2:

[1289] The terminal (a camera connected to the VR device) captures the user's facial expressions in real time. The captured video data is sent to the emotion engine. The emotion engine analyzes the video data and recognizes the user's emotional state (e.g., stress, joy, fatigue, etc.). The recognized emotional state is generated as the output.

[1290] Step 3:

[1291] The server generates a prompt sentence for the generative AI model based on the acquired user information and emotional state. The prompt sentence is generated in the following format:

[1292] The user has a normal build, is moderately fit, and is in good health. He or she enjoys Japanese food and is trying to lose weight. The user is currently feeling stressed. Based on this information, generate an optimal training menu and dietary advice.

[1293] The generated prompt sentences are used as input for a generative AI model.

[1294] Step 4:

[1295] The server inputs prompts into the generative AI model to generate optimal training menus and dietary advice. The generative AI model takes into account the user's information and emotional state and outputs personalized training menus and dietary advice. The output training menus and dietary advice are stored on the server.

[1296] Step 5:

[1297] The server sends the generated training menu and dietary advice to the VR device, which then displays the received training menu and dietary advice to the user. The user can then receive training and dietary advice in a virtual reality environment through the VR device.

[1298] Step 6:

[1299] The user wears a VR device and performs exercises according to the generated training menu. Exercise progress and feedback are sent to the server in real time. The server then adjusts the training menu and dietary advice accordingly based on this data.

[1300] Step 7:

[1301] The server analyzes the user's exercise data and feedback and reflects it in the next training menu and dietary advice, thereby continuously providing optimal advice based on the user's progress and emotional state.

[1302] Example 2

[1303] Next, a description will be given of Example 2 of Form 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."

[1304] Conventional diet support systems lack personalized support tailored to the user's individual needs and emotional state. They generally recommend specific nutritional foods or training equipment, which are not optimized for the user's specific situation. Furthermore, they do not utilize data on the user's past failures to support sustained diet success, making it difficult for users to maintain their motivation.

[1305] 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.

[1306] In this invention, the server includes a means for receiving and storing data entered by the user, a means for generating optimal meal plans and training plans for the user using a generative AI model, a means for recommending special nutritional foods and training equipment based on the generated plans, and a means for analyzing the user's emotional state and recommending nutritional foods and training equipment according to the emotional state. This enables personalized support according to the user's individual needs and emotional state, and can support sustained diet success.

[1307] "Means for receiving and storing data entered by the user" refers to the function by which the server receives data entered by the user through the terminal, such as weight, height, target weight, dietary preferences, exercise habits, and emotional state, and stores it in a database.

[1308] "Means for generating optimal meal plans and training plans for users using a generative AI model" refers to a function that uses a generative AI model to create individually optimized meal plans and training plans based on data input by the user.

[1309] "Means for recommending special nutritional foods and training equipment based on the generated plan" refers to a function that analyzes the meal plan and training plan created by the generative AI model, selects the most suitable nutritional foods and training equipment from a database, and recommends them to the user.

[1310] "Means for analyzing the user's emotional state and recommending nutritional foods and training equipment according to the emotional state" refers to a function that uses emotion analysis software to analyze the user's emotional state and, based on the results, recommends nutritional foods and training equipment that are appropriate for the user's emotional state.

[1311] The "means for supporting sustained diet success" is a function that analyzes the user's past failure data and provides support to help the user continue their diet.

[1312] This invention is a diet support system that includes means for receiving and storing data entered by a user, means for generating optimal meal plans and training plans for a user using a generative AI model, means for recommending special nutritional foods and training equipment based on the generated plans, and means for analyzing the user's emotional state and recommending nutritional foods and training equipment according to the emotional state.

[1313] 1. User data entry and storage

[1314] A user accesses the diet support system application using a device (smartphone or PC). The user inputs data such as weight, height, target weight, food preferences, exercise habits, and current emotional state. The device sends the input data to the server. The server stores the received data in a database. This data is used in subsequent processing.

[1315] 2. Nutrition advice and training plan generation

[1316] The server sends prompts to a generative AI model (e.g., OpenAI's GPT-4) based on the stored user data. The prompts include the user's weight, goal weight, food preferences, exercise habits, etc. The generative AI model generates optimal meal and training plans for the user based on the prompts.

[1317] Example prompt sentence:

[1318] "The user's current weight is 70kg and their goal weight is 65kg. They eat a high-protein diet and exercise three times a week. Please suggest the best meal and training plan for the user."

[1319] Based on this prompt, the generative AI model generates a specific meal plan (e.g., oatmeal and a protein shake for breakfast, chicken breast and a vegetable salad for lunch, and salmon and broccoli for dinner) and a training plan (e.g., strength training and cardio three times a week).

[1320] 3. Recommending special nutritional foods and training equipment

[1321] The server analyzes the generated meal and training plans and references a database of specialized nutritional foods and training equipment. The server then selects and recommends products that best fit the plan. For example, if a high-protein meal plan is generated, the server may recommend high-protein protein bars. The server may also recommend the best training equipment for a specific training program.

[1322] 4. Analyzing and regulating emotional states

[1323] The server analyzes the user's emotional state using emotion analysis software (e.g., IBM Watson's emotion analysis API) based on the user's input data and past behavioral data. If the server determines that the user is depressed, it will recommend nutritious foods to cheer them up or exercise equipment to refresh their mood.

[1324] 5. Displaying recommendations and encouraging purchases

[1325] The server sends the generated meal plan, training plan, and recommended special nutritional foods and training equipment to the terminal, which displays this information to the user. The user can check the displayed information and purchase the recommended products if necessary.

[1326] In this way, the server provides the user with the most suitable plan and product to effectively support their diet.

[1327] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1328] Step 1:

[1329] A user uses a terminal to access the diet support system application and inputs data such as weight, height, target weight, food preferences, exercise habits, and current emotional state. The input data is sent from the terminal to the server. The input data includes the user's individual needs and conditions.

[1330] Step 2:

[1331] The server receives the user data sent from the terminal and stores it in a database. The stored data is used in subsequent processing. Specifically, it is stored in the database in association with the user ID.

[1332] Step 3:

[1333] The server sends prompts to the generative AI model based on the stored user data, including the user's weight, target weight, food preferences, exercise habits, etc. The generative AI model then generates optimal meal and training plans for the user based on the prompts.

[1334] Example prompt sentence:

[1335] "The user's current weight is 70kg and their goal weight is 65kg. They eat a high-protein diet and exercise three times a week. Please suggest the best meal and training plan for the user."

[1336] Step 4:

[1337] The generative AI model generates specific meal and training plans based on the prompts. The generated plans are sent to a server. The specific meal plan includes breakfast, lunch, and dinner menus, and the training plan includes the type and frequency of exercise to be performed each week.

[1338] Step 5:

[1339] The server analyzes the generated meal and training plans and consults a database of specialized nutritional foods and training equipment. The server selects and recommends products that best fit the plan to the user. For example, if a high-protein meal plan is generated, the server will recommend high-protein protein bars. A list of recommended products is generated as an output.

[1340] Step 6:

[1341] The server uses emotion analysis software to analyze the user's emotional state based on the user's input data and past behavioral data. If the server determines that the user is depressed, it will recommend nutritional foods to cheer them up or workout equipment to refresh their mood. A list of recommendations based on the user's emotional state is output.

[1342] Step 7:

[1343] The server transmits the generated meal plan, training plan, and recommended special nutritional foods and training equipment to the terminal. The terminal displays this information to the user. The user can check the displayed information and purchase the recommended products if necessary. Information displayed to the user is generated as an output.

[1344] (Application example 2)

[1345] Next, a description will be given of Application Example 2 of Form 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."

[1346] Conventional diet support systems lack personalized support tailored to the user's individual needs and emotional state. Furthermore, they generally recommend specific nutritional foods or training equipment, but do not recommend optimal products based on the user's emotional state. Furthermore, purchasing the recommended products requires a separate procedure, which is time-consuming for users.

[1347] 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.

[1348] In this invention, the server includes a means for using VR technology, a means for providing personalized training and dietary advice using generative AI, a means for selling special nutritional foods and training equipment, a means for analyzing the user's emotional state and recommending optimal nutritional foods and training equipment, and a means for making the recommended products available for purchase within the application. This enables optimal diet support tailored to the user's individual needs and emotional state, and simplifies the process of purchasing the recommended products.

[1349] "VR technology" is a technology that uses virtual reality to provide users with an immersive experience.

[1350] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate training and dietary advice tailored to individual needs.

[1351] "Personalized training and dietary guidance" means providing a training program and meal plan that is customized based on the user's individual data and needs.

[1352] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health.

[1353] "Training equipment" means equipment used for fitness or exercise.

[1354] "Analyzing emotional state" means analyzing the user's text or voice data to quantify or classify their emotions at that time.

[1355] "Recommending optimal nutritional foods and training equipment" means suggesting the most suitable nutritional foods and training equipment based on the user's emotional state and individual needs.

[1356] "Making the recommended product available for purchase within the application" means that the purchase procedure can be completed within the application.

[1357] The following system configuration is used as an embodiment of the present invention: The system includes a server, a user terminal, and a generative AI model.

[1358] The server includes means for using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, means for analyzing the emotional state of the user and recommending optimal nutritional foods and training equipment, and means for making the recommended products available for purchase within the application.

[1359] The user terminal may be a smartphone, smart glasses, a head-mounted display, or a robot. These terminals communicate with a server and provide personalized training and dietary advice to the user.

[1360] The generative AI model analyzes the user's diet, training history, and emotional state to recommend optimal nutritional foods and training equipment. The TextBlob library is used to analyze emotional states, and the Requests library is used to send and receive data.

[1361] For example, if a user types "I'm tired today" on their smartphone, TextBlob analyzes their emotions and the AI ​​generator will recommend nutritious foods that have a relaxing effect. Similarly, if a user logs that they "ran for 30 minutes," the AI ​​generator will recommend the best training equipment.

[1362] An example prompt sentence would be of the following format:

[1363] User ID: 12345

[1364] Food log: [{'food_item': 'Apple', 'calories': 95, 'date': '2023-10-01T12:00:00'}]

[1365] Training log: [{'workout_type': 'Running', 'duration': 30, 'date': '2023-10-01T12:30:00'}]

[1366] Sentiment log: [{'text': 'I feel great today!', 'polarity': 0.8, 'date': '2023-10-01T13:00:00'}]

[1367] By inputting this prompt into a generative AI model, you can receive recommendations for optimal nutritional foods and training equipment.

[1368] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1369] Step 1:

[1370] The user uses a smartphone to input a log of their meals and training. The input data includes the contents of the meal, calories, type of training, time, etc. This records the user's meal and training history.

[1371] Step 2:

[1372] The user inputs their emotional state. For example, they input text such as "I'm tired today." This text data becomes the input data for emotion analysis.

[1373] Step 3:

[1374] The device uses the TextBlob library to analyze the input emotional state text. As a result, the emotional polarity (positive or negative) is quantified. This records the user's emotional state as numerical data.

[1375] Step 4:

[1376] The device sends the meal log, training log, and emotion log to the server. The sent data includes the user ID, meal log, training log, and emotion log. This allows the server to receive the user's latest data.

[1377] Step 5:

[1378] The server uses a generative AI model to analyze the received data, taking into account the user's past data and emotional state, and then recommends optimal nutritional foods and training equipment for the user.

[1379] Step 6:

[1380] The server sends information about recommended nutritional foods and training equipment to the device. The sent data includes detailed information about the recommended products. The device then displays the recommended products to the user.

[1381] Step 7:

[1382] The user purchases the recommended product within the application. When the user presses the purchase button, the device sends a purchase request to the server, allowing the user to easily purchase the product.

[1383] Step 8:

[1384] The server receives the purchase request and processes the purchase, which includes checking inventory, processing payment, and arranging delivery. The purchased product is then delivered to the user's hands.

[1385] Example 3

[1386] Next, a description will be given of Example 3 of Form Example 3. 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."

[1387] Conventional diet support systems do not provide personalized support that takes into account each user's individual failure patterns and emotional state, making it difficult to achieve sustained diet success. Furthermore, there is a lack of effective methods to increase user satisfaction and promote long-term use.

[1388] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1389] In this invention, the server includes means for collecting user data, means for saving and managing the collected data, means for analyzing the data using a generative AI model and identifying the user's failure patterns, means for generating personalized training plans and dietary advice based on the analysis results, means for monitoring the user's emotional state using an emotion engine and providing appropriate feedback, and means for notifying the user of suggestions and support. This enables personalized support that takes into account the user's individual failure patterns and emotional state, and can support sustained diet success.

[1390] "Means for collecting user data" refers to a device or software that provides an interface for users to input their past diet history and experiences of failure.

[1391] "Means for storing and managing collected data" refers to a device or software for storing data collected from users in a database and managing the data while maintaining its integrity.

[1392] "Means for analyzing data using a generative AI model to identify user failure patterns" refers to devices or software that use machine learning algorithms to analyze users' past data and identify causes and patterns of failure.

[1393] "Means for generating personalized training plans and dietary advice based on analysis results" refers to a device or software that automatically generates optimal training plans and dietary advice for users based on the results of data analysis.

[1394] "Means for monitoring a user's emotional state using an emotion engine and providing appropriate feedback" refers to a device or software for monitoring a user's emotional state in real time and providing feedback according to that state.

[1395] "Means for notifying users of suggestions and support" refers to devices or software that notify users of generated plans and feedback and encourage them to take action.

[1396] This invention is a system that analyzes a user's past diet history and failure patterns to support sustained diet success. A specific embodiment of this system is described below.

[1397] First, the server provides a means for collecting user data. Users use devices such as smartphones or PCs to input their past diet history and failure experiences through a dedicated application or web interface. This data includes dietary details, exercise history, weight fluctuations, and reasons for failure.

[1398] The server then uses a means to store and manage the collected data. The data is stored in a relational database such as MySQL or PostgreSQL. The server validates the data to ensure its integrity.

[1399] The server then analyzes the data using a generative AI model. Specifically, it uses Python and TensorFlow to build a machine learning model to identify patterns of user failure. Based on past data, the server predicts the situations in which users are likely to fail.

[1400] Based on the analysis results, the server generates personalized training plans and dietary advice. For example, if a user has a tendency to eat sweets late at night, the server will suggest low-calorie snacks. It also provides exercise plans tailored to the user's lifestyle and preferences.

[1401] Furthermore, the server monitors the user's emotional state using an emotion engine. The emotion engine estimates emotions based on the user's input data and behavioral patterns and monitors them in real time. For example, if the user is feeling stressed, the server will suggest yoga or meditation to help them relax.

[1402] Finally, the server notifies the user of the generated plan and feedback. The device receives the notification and displays it to the user. The user acts on the proposed plan and re-enters their progress, allowing the server to continuously update the data and provide support.

[1403] Examples of specific prompts include:

[1404] "Analyze their past diet history and failure patterns, and suggest low-calorie snacks that are okay to eat late at night. Also, if the user is feeling stressed, suggest yoga or meditation to help them relax."

[1405] In this way, the server can prevent the user from repeating the same setbacks and support the user in achieving continuous diet success. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1406] Step 1:

[1407] The user inputs his or her past diet history and experiences of failure.

[1408] Input: Data such as diet, exercise history, weight fluctuations, and reasons for setbacks.

[1409] Output: The entered data is sent from the terminal to the server.

[1410] Specific operation: The user uses a smartphone or PC to input data through a dedicated application or web interface.

[1411] Step 2:

[1412] The server stores and manages the collected data.

[1413] Input: Data submitted by the user.

[1414] Output: Data stored in the database.

[1415] Specific operation: The server stores data using a relational database such as MySQL or PostgreSQL and performs validation to maintain data integrity.

[1416] Step 3:

[1417] The server analyzes the data using a generative AI model.

[1418] Input: Saved user data.

[1419] Output: The failure pattern as an analysis result.

[1420] How it works: The server uses Python and TensorFlow to build a machine learning model that analyzes users' past data to identify patterns of frustration.

[1421] Step 4:

[1422] The server generates personalized training plans and dietary advice based on the analysis results.

[1423] Input: Failure pattern as analysis result.

[1424] Output: Personalized training plans and nutritional advice.

[1425] Specific operation: Based on the user's failure patterns, the server suggests low-calorie snacks and an exercise plan that suits their lifestyle.

[1426] Step 5:

[1427] The server uses an emotion engine to monitor the user's emotional state and provide appropriate feedback.

[1428] Input: User input data and behavior patterns.

[1429] Output: Feedback depending on emotional state.

[1430] Specific operation: The server uses an emotion engine to monitor the user's emotional state in real time and suggests yoga or meditation to relax if the user is feeling stressed.

[1431] Step 6:

[1432] The server notifies the user of the generated plan and feedback.

[1433] Input: Generated training plans, dietary advice, and emotional feedback.

[1434] Output: Suggestions and support notified to the user.

[1435] Specific operation: The server sends a notification to the terminal, which receives it and displays it to the user. The user acts according to the proposed plan and re-enters the progress.

[1436] (Application example 3)

[1437] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1438] Conventional diet support systems were unable to fully utilize data on users' past failures, making it difficult to support sustained diet success. They also lacked feedback and advice to maintain users' motivation, making it difficult to promote long-term use. Furthermore, physical fitness gyms and diet cafes lacked the means to provide personalized training plans and meal menus.

[1439] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means using VR technology, means for providing personalized training and dietary advice using a generative AI, means for selling special nutritional foods and training equipment, means for analyzing a user's past diet history and failure patterns at a physical fitness gym or diet cafe and providing personalized training plans and meal menus, and means for providing feedback and advice to maintain the user's motivation using an emotion engine. This prevents the user from repeating the same failures, supports sustained diet success, and promotes long-term use.

[1440] "VR technology" is a technology that uses virtual reality to provide users with visual and experiential feedback.

[1441] "Generative AI" is a technology that uses artificial intelligence to analyze user data and generate personalized training and dietary advice.

[1442] "Personalized training and dietary guidance" means providing optimized training plans and meal menus based on individual user data.

[1443] "Special nutritional foods" are foods that contain specific nutritional ingredients for the purpose of dieting or maintaining health.

[1444] "Training equipment" refers to equipment used for fitness and exercise.

[1445] A "physical store" is a facility such as a fitness gym or diet cafe that exists in a physical location.

[1446] A "fitness gym" is a facility equipped with the necessary equipment for exercise and training.

[1447] A "diet cafe" is a cafe that provides healthy meals and drinks to support dieting.

[1448] "User's past diet history" refers to records and data of diets that the user has undertaken in the past.

[1449] "Failure patterns" are data that show the reasons and trends behind a user's past failures in dieting.

[1450] The "emotion engine" is a technology that analyzes the user's emotional state and provides feedback and advice to help them maintain motivation.

[1451] "Feedback" is an evaluation or advice provided regarding a user's behavior or status.

[1452] "Advice" is guidance or advice provided to help users achieve their goals.

[1453] To implement the present invention, the following system configuration and processing procedure are used.

[1454] System Configuration

[1455] 1. Hardware

[1456] Server: A high-performance server for analyzing data and running generative AI models.

[1457] Device: The smartphone, tablet, or computer you use.

[1458] Physical store equipment: Training equipment and nutritional food sales equipment installed in fitness gyms and diet cafes.

[1459] 2. Software

[1460] Generative AI model: An AI model that analyzes a user's past diet history and failure patterns to generate personalized training plans and meal menus.

[1461] Emotion Engine: Software that analyzes the user's emotional state and provides feedback and advice to help them stay motivated.

[1462] Data analysis tools: Data analysis libraries such as Pandas and Scikit-learn.

[1463] Processing steps

[1464] 1. Data collection and preprocessing

[1465] The server collects the user's past diet history and failure patterns, including data such as weight, calorie intake, and exercise time.

[1466] The collected data is preprocessed and standardized using Pandas.

[1467] 2. Clustering

[1468] The server uses Scikit-learn's KMeans algorithm to cluster user frustration patterns.

[1469] 3. Plan generation using generative AI

[1470] The server uses a generative AI model to generate personalized training plans and meal menus for each cluster.

[1471] An example of a specific prompt is, "Create a personalized training plan based on the user's cluster 1. The user has experienced setbacks in the past due to lack of exercise and poor diet management. Provide specific advice to help him achieve sustainable weight loss success."

[1472] 4. Feedback from the Emotion Engine

[1473] The server uses an emotion engine to analyze the user's emotional state and provide feedback and advice to help them maintain their motivation.

[1474] Specific examples

[1475] User A has tried to diet many times in the past, but always failed for similar reasons. Using this system, his past data can be analyzed and a training plan and meal menu optimized for him can be provided. For example, the generative AI model generates a specific plan using the prompt, "Create a personalized training plan based on the user's cluster 1. The user has previously failed due to lack of exercise and poor diet management. Please provide specific advice to help him succeed in dieting sustainably."

[1476] In this way, it is possible to prevent users from repeating the same setbacks, support their continued success in dieting, and promote long-term use.

[1477] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1478] Step 1:

[1479] The server collects the user's past diet history and failure patterns. Specifically, data such as weight, calorie intake, and exercise time entered by the user using a smartphone or tablet is stored in a database. The input data is saved in CSV files or database format.

[1480] Step 2:

[1481] The server preprocesses the collected data. Specifically, it reads the data using the Pandas library, fills in missing values, and removes outliers. It then standardizes the data using StandardScaler. The input is the user's raw data, and the output is the preprocessed standardized data.

[1482] Step 3:

[1483] The server performs clustering using the preprocessed data. Specifically, it uses Scikit-learn's KMeans algorithm to cluster user failure patterns. The input is the standardized data, and the output is the label of the cluster to which each user belongs.

[1484] Step 4:

[1485] The server uses a generative AI model to generate personalized training plans and meal menus for each cluster. Specifically, it generates prompts based on the cluster labels and inputs them into the AI ​​model using the OpenAI API. The inputs are the cluster labels and prompts, and the output is the generated training plans and meal menus.

[1486] Step 5:

[1487] The server uses an emotion engine to analyze the user's emotional state. Specifically, it analyzes the feedback and diary data entered by the user and estimates the emotional state. The input is the user's feedback data, and the output is the estimated emotional state.

[1488] Step 6:

[1489] The server provides feedback and advice to maintain the user's motivation based on the analysis results of the emotion engine. Specifically, it generates appropriate feedback messages according to the estimated emotional state and sends them to the user's device. The input is the estimated emotional state, and the output is the feedback message.

[1490] Step 7:

[1491] The user executes the personalized training plan and meal menu provided by the server and inputs feedback. Specifically, the user uses a smartphone or tablet to record their training progress and meal details and send them to the server. The input is the user's execution data, and the output is updated diet history data.

[1492] In this way, it is possible to prevent users from repeating the same setbacks, support their continued success in dieting, and promote long-term use.

[1493] 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.

[1494] 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> ) 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.

[1495] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1496] 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.

[1497] [Third embodiment]

[1498] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1499] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1500] 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).

[1501] 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.

[1502] 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.

[1503] 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).

[1504] 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.

[1505] 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.

[1506] 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.

[1507] 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.

[1508] 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.

[1509] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1510] "Example 1"

[1511] The diet support system of the present invention combines VR technology and generative AI to provide users with personalized training and dietary advice. Specifically, the user wears a VR device at home and exercises according to the training program provided by the generative AI. The generative AI generates an optimal training menu based on information such as the user's body type, physical strength, and health condition. Similarly, the generative AI also provides dietary advice, taking into account the user's food preferences, health condition, and diet goals.

[1512] "Example 2"

[1513] The diet support system of the present invention also sells special nutritional foods and training equipment. These products work in conjunction with the training and dietary advice provided by the generation AI to more effectively support the user's diet. For example, based on the dietary advice provided by the generation AI, the system can recommend special nutritional foods and encourage their purchase. Similarly, the system can recommend training equipment optimized for the training program provided by the generation AI and encourage its purchase.

[1514] "Example 3"

[1515] The diet support system of this invention analyzes the user's past failure data and supports sustained diet success. Specifically, the generative AI analyzes the user's past diet history and failure patterns, and provides personalized training and dietary advice based on that. This prevents the user from repeating the same failures and supports sustained diet success.

[1516] The processing flow of each embodiment will be described below.

[1517] "Example 1"

[1518] Step 1: The user puts on the VR device at home.

[1519] Step 2: The AI ​​generates the optimal training menu based on the user's body type, physical strength, health condition, and other information.

[1520] Step 3: The user exercises according to the training program provided by the generated AI.

[1521] Step 4: The generative AI provides dietary advice taking into account the user's food preferences, health status, diet goals, etc.

[1522] "Example 2"

[1523] Step 1: Sell special nutritional foods and training equipment in conjunction with training and dietary advice provided by generative AI.

[1524] Step 2: Based on the dietary advice provided by the generation AI, recommend special nutritional foods and encourage their purchase.

[1525] Step 3: Recommend training equipment optimized for the training program provided by the generative AI and encourage its purchase.

[1526] "Example 3"

[1527] Step 1: The generative AI analyzes the user's past diet history and failure patterns.

[1528] Step 2: Generative AI provides personalized training and dietary advice based on the analysis results.

[1529] Step 3: This prevents users from repeating the same setbacks and supports sustained diet success.

[1530] Example 1

[1531] Next, a description will be given of Example 1 of Form 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."

[1532] Conventional diet support systems provide uniform training and dietary advice to users, without sufficient personalization based on each user's body type, physical strength, health condition, food preferences, and diet goals. Furthermore, there is a lack of ongoing support that takes into account users' past failures, resulting in a low success rate for dieting. Furthermore, sales of special nutritional foods and training equipment are not integrated into the system, which means users have to go through the trouble of purchasing the necessary items separately.

[1533] 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.

[1534] In this invention, the server includes a means for using VR technology, a means for providing personalized training and dietary advice using generation AI, a means for generating an optimal training menu based on the user's body type information, physical strength information, and health status, a means for generating an optimal meal plan based on the user's food preferences, health status, and diet goals, and a means for selling special nutritional foods and training equipment. This makes it possible to provide individually optimized training and dietary advice to users and provide continuous support taking into account past failure data. Furthermore, by providing the necessary nutritional foods and training equipment all in one place, user convenience can be improved.

[1535] "VR technology" is a technology that uses virtual reality to provide users with an immersive experience.

[1536] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate optimal training and meal plans for users.

[1537] "Personalized training" means providing a training menu that is individually optimized based on the user's body type information, physical strength information, and health condition.

[1538] "Personalized dietary guidance" refers to providing a meal plan that is individually optimized based on the user's food preferences, health status, and diet goals.

[1539] "Body type information" refers to physical data such as the user's height, weight, and body fat percentage.

[1540] "Physical fitness information" is physical data such as the user's exercise experience and current exercise habits.

[1541] "Health status" refers to data related to the user's health, such as allergies and chronic illnesses.

[1542] "Dietary preferences" is data about the user's food preferences, such as favorite and least favorite ingredients.

[1543] A "diet goal" is a goal that a user wants to achieve, such as losing weight or gaining muscle.

[1544] "Specialty nutritional foods" are foods specifically designed to support diet and training.

[1545] "Training equipment" refers to equipment or devices that a user uses to train.

[1546] "Past failure data" is data relating to the user's past experiences of failure in dieting or training.

[1547] "Continuous support" means providing long-term support to help users continue their diet and training.

[1548] MODE FOR CARRYING OUT THE INVENTION

[1549] This invention is a diet support system that combines VR technology and generative AI to provide users with personalized training and dietary advice. Specific embodiments of this system are described below.

[1550] System configuration

[1551] This system consists of a server, a terminal (VR device or smartphone), and a user. The server generates training menus and meal plans using a generative AI model and sends them to the terminal. The terminal sends information entered by the user to the server and displays the training menus and meal plans received from the server to the user.

[1552] Hardware and software used

[1553] The server is a high-performance computer with software installed to run generative AI models (e.g., OpenAI's GPT-4), and the terminal is a VR device or smartphone that provides an interface for users to input information and display training menus and meal plans.

[1554] Data processing and calculation

[1555] The server receives data sent by the user, such as body type information, physical strength information, health condition, food preferences, and diet goals, and uses a generative AI model to generate optimal training menus and meal plans. The generated menus and plans are sent to the device and displayed to the user.

[1556] Specific examples

[1557] Example 1: Generating a training menu

[1558] The user inputs information about their body type (height 170cm, weight 70kg, body fat percentage 20%) and physical fitness (no prior exercise experience, current exercise habit is walking once a week). Based on this information, the server uses a generative AI model to generate a training menu suited to the user. For example, the following prompt sentence is input to the generative AI model:

[1559] Prompt statement:

[1560] "Please create a training menu suitable for a 30-year-old man with a height of 170 cm, weight of 70 kg, body fat percentage of 20%, and no exercise experience."

[1561] Example 2: Generating a meal plan

[1562] The user inputs their food preferences (favorite ingredients are chicken and broccoli, least favorite ingredients are fish), health condition (no allergies, no chronic illnesses), and diet goal (weight loss). Based on this information, the server uses a generative AI model to generate a meal plan suitable for the user. For example, the following prompt sentence can be input into the generative AI model:

[1563] Prompt statement:

[1564] "Create a one-week meal plan suitable for a 30-year-old man whose favorite foods are chicken and broccoli, whose least favorite foods are fish, who has no allergies, and who is trying to lose weight."

[1565] In this way, the server, terminal, and user work together to create a system that provides personalized diet support.

[1566] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1567] Step 1:

[1568] Using a terminal (VR device or smartphone), the user inputs their body type information (height, weight, body fat percentage), physical fitness information (exercise experience, current exercise habits), and health status (allergies, chronic illnesses). This information is entered through the terminal's interface and sent to the server. The input data is stored in a database as the user's individual information.

[1569] Step 2:

[1570] The device sends the entered user information to the server. The server stores the received information in a database and generates prompts to input into the generative AI model. For example, based on the user's body type and physical strength information, it generates a prompt such as, "Please create a training menu suitable for a 30-year-old man with a height of 170 cm, a weight of 70 kg, a body fat percentage of 20%, and no exercise experience."

[1571] Step 3:

[1572] The server inputs the generated prompt sentences into the generative AI model to generate an optimal training menu for the user. The generative AI model analyzes the data based on the input prompt sentences and outputs a training menu. The output training menu is saved on the server.

[1573] Step 4:

[1574] The server sends the generated training menu to the device. The device displays the received training menu to the user. The user performs exercises according to the displayed training menu. The device tracks the user's movements in real time and provides feedback on correct form and movements.

[1575] Step 5:

[1576] Using a terminal, users input their food preferences (favorite and least favorite foods), health status (allergies, chronic illnesses), and diet goals (weight loss, muscle building). This information is entered through the terminal's interface and sent to the server. The input data is stored in a database as the user's individual information.

[1577] Step 6:

[1578] The server generates a meal plan using a generative AI model based on the stored dietary information. For example, based on the user's dietary preferences and health status, it generates a prompt such as, "Please create a one-week meal plan suitable for a 30-year-old man whose favorite ingredients are chicken and broccoli, whose least favorite ingredients are fish, who has no allergies, and who is trying to lose weight."

[1579] Step 7:

[1580] The server inputs the generated prompts into a generative AI model to generate an optimal meal plan for the user. The generative AI model analyzes the data based on the input prompts and outputs a meal plan. The output meal plan is saved on the server.

[1581] Step 8:

[1582] The server sends the generated meal plan to the device. The device displays the received meal plan to the user. The user eats according to the displayed meal plan. The device tracks whether the user is adhering to the meal plan and provides reminders and advice as needed.

[1583] In this way, the server, terminal, and user work together to create a system that provides personalized diet support.

[1584] (Application example 1)

[1585] Next, a description will be given of Application Example 1 of Form 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."

[1586] Conventional diet support systems often provide uniform training and dietary advice to users, lacking personalized support that fully considers each user's body type, physical strength, health condition, food preferences, and diet goals. Furthermore, dietary advice requires users to source and cook ingredients themselves, which is time-consuming and makes it difficult to achieve sustained diet success. Furthermore, the lack of sustained support utilizing data on users' past failures also contributes to the low success rate of dieting.

[1587] 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.

[1588] In this invention, the server includes means for using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, and means for proposing optimal meal menus based on information such as the user's body type, physical strength, health condition, food preferences, and diet goals, and ordering meals through a food delivery service. This allows users to be provided with individually optimized training and dietary advice, and further reduces the hassle of using the food delivery service, enabling them to achieve sustainable diet success.

[1589] "VR technology" is a technology that provides users with virtual reality, allowing them to experience a virtual environment that is different from the real world through their eyes and ears.

[1590] "Generative AI" is a technology that uses artificial intelligence to analyze user data and generate individually optimized training and dietary advice.

[1591] "Personalized training" means providing the optimal training menu based on individual information such as the user's body type, physical strength, and health condition.

[1592] "Dietary guidance" means proposing an appropriate meal menu taking into consideration the user's food preferences, health condition, diet goals, etc.

[1593] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health, and are nutritionally balanced.

[1594] "Training equipment" refers to equipment for exercise and fitness that supports users in improving their physical strength and muscle training.

[1595] A "food delivery service" is a service that delivers meals ordered by users to their homes, saving users time and effort.

[1596] "User's body type" refers to the shape and size of the user's body, which serves as the basis for creating diet and training plans.

[1597] "Physical strength" refers to the user's physical endurance and muscle strength, and is an important factor when deciding on a training menu.

[1598] "Health status" refers to the user's current health condition, which should be taken into consideration when providing training and dietary advice.

[1599] "Dietary preferences" refers to the foods and eating style that a user prefers, and is important information when providing personalized dietary advice.

[1600] "Diet goals" refer to the weight and body shape goals that a user wishes to achieve, and serve as a basis for creating training and dietary guidance plans.

[1601] As an embodiment of the present invention, the following system is constructed.

[1602] First, the server provides a means using VR technology. Specifically, by wearing a VR device, users can train in a virtual reality environment. This VR device provides a realistic training environment through visual and auditory senses, enhancing the user's sense of immersion.

[1603] Next, the server has a means to provide personalized training and dietary advice using generative AI. The generative AI generates optimal training and dietary menus based on information such as the user's body type, physical strength, health condition, food preferences, and diet goals. This is done by using OpenAI's API to analyze the user's individual data and provide the optimal plan.

[1604] Furthermore, the server has a means for selling special nutritional foods and training equipment, which allows users to easily purchase the nutritional foods and training equipment they need.

[1605] The server also suggests optimal meal menus based on the user's body type, physical strength, health condition, food preferences, and diet goals, and provides a means to order meals through a food delivery service, allowing users to easily receive healthy meals at home.

[1606] Specifically, the system first obtains user information, including data entered by the user via a smartphone or computer. Next, it sends prompts to a generative AI model, which then generates optimal training and meal plans. The following is an example of a prompt:

[1607] User's body type: Normal

[1608] User's stamina: Medium

[1609] Patient's health: Good

[1610] User's food preference: Japanese food

[1611] User's diet goal: weight loss

[1612] Use this information to create the optimal training and meal plan for you.

[1613] The generated menu is displayed to the user through a VR device, and the user can follow the menu to train and order meals using a food delivery service based on the menu.

[1614] This system allows users to receive individually optimized training and dietary advice, and also eliminates the hassle through food delivery services, making it possible for them to achieve sustainable diet success.

[1615] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1616] Step 1:

[1617] The user uses a smartphone or PC to input personal information such as body type, physical strength, health status, food preferences, and diet goals. This information is sent to the server. The input data includes the user's height, weight, exercise habits, and food preferences. The server receives this data and stores it in a database.

[1618] Step 2:

[1619] The server creates prompts for the generative AI model based on the saved user information. The prompts include information about the user's body type, physical strength, health status, food preferences, and diet goals. Specific examples of prompts are as follows:

[1620] User's body type: Normal

[1621] User's stamina: Medium

[1622] Patient's health: Good

[1623] User's food preference: Japanese food

[1624] User's diet goal: weight loss

[1625] Use this information to create the optimal training and meal plan for you.

[1626] Step 3:

[1627] The server sends the prompt to the generative AI model, which then generates the optimal training and meal menus for the user. The generative AI model then analyzes the prompt and generates the optimal training and meal menus for the user. The generated menus are then returned to the server.

[1628] Step 4:

[1629] The server sends the generated training and meal menus to the VR device. The user wears the VR device and trains in a virtual reality environment. The VR device provides a realistic training environment through visual and auditory channels, enhancing the user's sense of immersion.

[1630] Step 5:

[1631] The server orders meals through a food delivery service based on the generated meal menu. The user uses a smartphone or computer to check the proposed meal menu and confirm the order. The server then sends the order information to the food delivery service, and the meal is delivered to the user's home.

[1632] Step 6:

[1633] The user receives the delivered meal and eats according to the suggested meal menu, which allows the user to continue eating healthy while saving time and effort.

[1634] Step 7:

[1635] The server collects the user's training and diet history data and reflects it in the next training session or dietary advice, thereby understanding the user's progress and providing more effective diet support.

[1636] Example 2

[1637] Next, a description will be given of Example 2 of Form 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."

[1638] Conventional diet support systems have had the problem of being unable to provide effective diet support because it is difficult to provide personalized dietary advice and training programs for each user. Furthermore, there have been issues with the inability to properly recommend special nutritional foods and training equipment, resulting in a low success rate for users in dieting.

[1639] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for the user to input personal data using a terminal, a means for the server to receive and store the input data, a means for the server to analyze the data using a generated AI model and generate dietary advice and a training program, a means for the server to recommend special nutritional foods based on the generated dietary advice, a means for the server to recommend training equipment based on the generated training program, and a means for the terminal to display to the user the advice and recommended products received from the server. This makes it possible to provide personalized dietary advice and training programs to the user and to appropriately recommend special nutritional foods and training equipment, thereby improving the user's success rate in dieting.

[1640] "User" refers to an individual who uses the system to input personal data and receive dietary advice and training programs.

[1641] "Terminal" refers to the device used by the User to input personal data and receive and display advice and recommended products from the Server, including, for example, a smartphone or PC.

[1642] "Server" refers to the computer system that stores data received from users, analyzes the data using a generative AI model, and generates dietary advice and training programs.

[1643] A "generative AI model" refers to an artificial intelligence model that takes a user's personal data and prompt text as input and generates appropriate dietary advice and training programs.

[1644] "Dietary guidance" refers to providing advice on appropriate dietary content and nutritional intake methods based on the user's diet goals and health condition.

[1645] A "training program" refers to advice on appropriate exercise content and training methods based on the user's diet goals and exercise habits.

[1646] "Special nutritional foods" refer to foods that are recommended based on the user's dietary advice and are effective for dieting and maintaining health, such as protein bars and low-calorie smoothies.

[1647] "Training equipment" refers to equipment recommended for effective exercise and training based on a user's training program, including, for example, running machines and dumbbell sets.

[1648] "Advice" refers to specific instructions or recommendations regarding dietary guidance or training programs provided by a generative AI model based on a user's personal data.

[1649] "Recommended Products" refers to specialized nutritional foods or training equipment that the generative AI model recommends based on the user's dietary guidance and training program.

[1650] This invention is a diet support system in which a user inputs personal data using a terminal, and a server analyzes the data to provide personalized dietary advice and training programs. Specific embodiments of this system are described below.

[1651] First, the user accesses the system using a device such as a smartphone or PC. Using a dedicated application or web form, the user enters personal data such as height, weight, age, gender, exercise habits, and dietary details. For example, the user might enter "height 160 cm, weight 70 kg, age 30, gender female, exercise twice a week, bread and coffee for breakfast, salad for lunch, and fish and vegetables for dinner."

[1652] The device then sends the entered data to a server, which stores it using a relational database management system (RDBMS) such as MySQL or PostgreSQL. For example, it executes the following SQL query: INSERT INTO user_data (height, weight, age, gender, exercise_habits, diet) VALUES (160, 70, 30, 'female', '2 times a week', 'bread and coffee for breakfast, salad for lunch, fish and vegetables for dinner');

[1653] The server uses data analysis software such as Python or R to analyze the stored user data. The server uses a generative AI model (e.g., GPT-4) to generate optimal dietary advice and training programs for the user's diet goals. The generative AI model receives a prompt as input and generates appropriate advice. For example, the following prompt is input into the generative AI model:

[1654] "I'm a 30-year-old woman who weighs 70 kg and is 160 cm tall. I exercise twice a week. My goal is to lose 5 kg in three months. Please provide me with the optimal dietary advice and training program."

[1655] The generative AI model generates dietary advice based on this prompt. For example, it generates advice such as, "We recommend eating high-protein foods for breakfast and including plenty of vegetables at lunch and dinner. It is also recommended to choose nuts or yogurt as snacks." It also recommends, "We recommend purchasing protein bars and low-calorie smoothies."

[1656] Similarly, a generative AI model can generate a training program, such as "Perform 30 minutes of aerobic exercise (e.g., jogging or cycling) three times a week, and add strength training twice a week," and further recommend "We recommend purchasing a treadmill and a set of dumbbells."

[1657] Finally, the server sends the generated advice and recommended products to the device. The device then displays the advice and recommended products to the user using the application's dashboard or notification function. For example, the device may display specific advice and recommended products along with a message such as, "A dietary guide and training program based on your diet goals has been generated. Details are below."

[1658] In this way, users can receive personalized dietary advice and training programs, and receive appropriate recommendations for special nutritional foods and training equipment. This system can improve the user's success rate in dieting.

[1659] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1660] Step 1:

[1661] Users access the system using a terminal and enter their personal data. Specifically, they enter information such as height, weight, age, gender, exercise habits, and diet through a dedicated application or web form. For example, they might enter "height 160 cm, weight 70 kg, age 30, gender female, exercise twice a week, bread and coffee for breakfast, salad for lunch, and fish and vegetables for dinner." The input data is saved on the terminal in JSON format or as form data.

[1662] Step 2:

[1663] The device sends the entered data to the server. Specifically, it uses an HTTP POST request to send user data to the server. For example, it sends JSON-formatted data to the endpoint " / submitUserData". The server stores the received data in a relational database management system (RDBMS) such as MySQL or PostgreSQL. For example, it executes the following SQL query: "INSERT INTO user_data (height, weight, age, gender, exercise_habits, diet) VALUES (160, 70, 30, 'female', '2 times a week', 'bread and coffee for breakfast, salad for lunch, fish and vegetables for dinner');".

[1664] Step 3:

[1665] The server analyzes the stored user data. Specifically, it uses data analysis software such as Python or R to calculate the user's BMI (Body Mass Index) and calorie consumption. For example, to calculate BMI, it uses the formula "BMI = weight / (height / 100)^2." The analysis results are used to generate prompt sentences to input into the generative AI model.

[1666] Step 4:

[1667] The server uses a generative AI model (e.g., GPT-4) to generate optimal dietary advice and training programs for the user's diet goals. Specifically, a prompt sentence is input into the generative AI model, which then generates appropriate advice. For example, the following prompt sentence is input into the generative AI model: "I am a 30-year-old woman, weighing 70 kg, standing 160 cm tall, and exercising twice a week. My diet goal is to lose 5 kg in three months. Please provide me with the optimal dietary advice and training program." The generative AI model then generates dietary advice and training programs based on this prompt sentence.

[1668] Step 5:

[1669] The server recommends specific nutritional foods based on the generated dietary advice. Specifically, it selects nutritional foods suitable for the user based on the dietary advice provided by the generative AI model. For example, it may give advice such as "We recommend that you eat high-protein foods for breakfast and include plenty of vegetables for lunch and dinner. It is also recommended that you choose nuts or yogurt as snacks," along with a recommendation such as "We recommend that you purchase protein bars and low-calorie smoothies."

[1670] Step 6:

[1671] The server recommends training equipment based on the generated training program. Specifically, it selects training equipment suitable for the user based on the training program provided by the generative AI model. For example, it advises, "Perform 30 minutes of aerobic exercise (e.g., jogging or cycling) three times a week, and add strength training twice a week," along with a recommendation that, "We recommend purchasing a treadmill and a set of dumbbells."

[1672] Step 7:

[1673] The server sends the generated advice and recommended products to the device. Specifically, it sends the generated advice and recommended products to the device using an HTTP response. The device displays the received information to the user using the application's dashboard or notification function. For example, it displays the specific advice and recommended products along with a message saying, "A dietary guide and training program based on your diet goals has been generated. Details are as follows."

[1674] (Application example 2)

[1675] Next, a description will be given of Application Example 2 of Form 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."

[1676] Conventional diet support systems have had problems such as difficulty in providing optimal dietary advice and training programs for individual users, and ineffective recommendations of special nutritional foods and training equipment. Furthermore, there is a lack of support for sustained diet success that takes into account the user's past failure data, resulting in a low long-term diet success rate.

[1677] 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.

[1678] In this invention, the server includes a means for collecting user data, a means for analyzing the data using a generation AI to generate optimal dietary advice and training programs, and a means for recommending and encouraging the purchase of special nutritional foods and training equipment based on the programs provided by the generation AI. This allows for the provision of optimal diet plans for individual users and the effective recommendation of special nutritional foods and training equipment. Furthermore, by analyzing the user's past failure data, it is possible to support sustained diet success.

[1679] "User data" refers to information such as the user's diet goals, current physical condition, eating habits, and exercise habits.

[1680] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate optimal dietary advice and training programs.

[1681] "Dietary guidance" refers to suggesting appropriate meal content and meal timing based on the user's health condition and diet goals.

[1682] A "training program" refers to suggesting appropriate exercise content and frequency based on the user's physical strength and diet goals.

[1683] "Nutritional foods" refer to foods specifically designed for weight loss or health maintenance.

[1684] "Training equipment" refers to any equipment or device used to effectively exercise or train.

[1685] "Recommendation" refers to the generative AI suggesting specific nutritional foods or training equipment to users based on the analysis results.

[1686] "Encouraging purchases" refers to making it easy for users to purchase recommended nutritional foods and training equipment.

[1687] "Past failure data" refers to information about the user's past failed diet attempts and the reasons for them.

[1688] "Sustained diet success" refers to a user continuing a diet over a long period of time and achieving their goal.

[1689] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[1690] System Configuration

[1691] 1. Collection of User Data

[1692] Hardware: Smartphone

[1693] Software: Application user interface

[1694] Data: Your diet goals, current physical condition, eating habits, and exercise habits

[1695] 2. Data analysis using generative AI

[1696] Hardware: Cloud Server

[1697] Software: Generative AI models (e.g., GPT-4)

[1698] Data processing: Analyze user data and generate optimal dietary advice and training programs

[1699] 3. Selection of recommended products

[1700] Hardware: Cloud Server

[1701] Software: Product database, recommendation engine

[1702] Data calculation: Selecting special nutritional foods and training equipment based on programs provided by generative AI

[1703] 4. Suggestions for users

[1704] Hardware: Smartphone

[1705] Software: Application user interface

[1706] Data Display: Display of recommended dietary advice, training programs, and related products

[1707] 5. Purchase Process

[1708] Hardware: Smartphone

[1709] Software: Electronic payment system

[1710] Data processing: processing and verifying purchases

[1711] Processing Description

[1712] 1. Collection of User Data:

[1713] Through a smartphone application, users input information such as their diet goals, current physical condition, eating habits, exercise habits, etc. This data is sent to a cloud server and stored for analysis by the generative AI.

[1714] 2. Generative AI data analysis:

[1715] A generative AI model (e.g., GPT-4) on a cloud server analyzes the collected user data and generates optimal dietary and training programs for each individual user, taking into account the user's past failure data.

[1716] 3. Recommended product selection:

[1717] Based on the program provided by the generative AI, a recommendation engine on the cloud server selects special nutritional foods and training equipment, thereby recommending the best products for users.

[1718] 4. Suggestions for users:

[1719] Through a smartphone application, users are provided with personalized dietary advice, training programs, and recommended nutritional foods and training equipment.

[1720] 5. Purchasing process:

[1721] Users can easily purchase recommended nutritional foods and training equipment through the application, with the purchase process handled using an electronic payment system.

[1722] Specific examples

[1723] The user opens the application and enters the following information:

[1724] Diet goal: Lose 5kg

[1725] Current health condition: Healthy

[1726] Eating habits: 3 meals a day, snacks

[1727] Exercise habits: Jogging twice a week

[1728] The generative AI analyzes this data and provides dietary advice and training programs such as:

[1729] Dietary advice: High-protein, low-calorie diet, snacks changed to nuts

[1730] Training program: Add strength training three times a week

[1731] Additionally, the following specialized nutritional and training equipment is recommended:

[1732] Nutritional foods: high protein bars, protein shakes

[1733] Training equipment: dumbbell set, yoga mat

[1734] Prompt Sentence Examples

[1735] "Generate optimal dietary advice and training programs based on the user's diet goals, current physical condition, eating habits, and exercise habits, and recommend nutritional foods and training equipment optimized for them."

[1736] This way, users can easily find and purchase the diet plan and related products that best suit them.

[1737] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1738] Step 1:

[1739] Users open the application on their smartphone and enter information such as their diet goals, current physical condition, eating habits, and exercise habits.

[1740] Input: diet goal, current physical condition, eating habits, exercise habits

[1741] Output: User data

[1742] Specific behavior: The user enters the required information into the application's input form and presses the submit button.

[1743] Step 2:

[1744] The device sends the collected user data to a cloud server.

[1745] Input: User data

[1746] Output: User data stored on cloud servers

[1747] Specific behavior: The application calls an API to send user data to the cloud server.

[1748] Step 3:

[1749] The server uses a generative AI model (e.g., GPT-4) to analyze the collected user data and generate optimal dietary advice and training programs.

[1750] Input: User data

[1751] Output: Dietary advice, training program

[1752] How it works: A generative AI model on a cloud server analyzes user data and generates optimal dietary advice and training programs based on prompts.

[1753] Step 4:

[1754] The server selects special nutritional foods and training equipment based on a program provided by the generated AI.

[1755] Input: dietary advice, training program

[1756] Output: Recommended nutrition, training equipment

[1757] Specific operation: A recommendation engine on a cloud server selects the optimal nutritional foods and training equipment from a product database based on dietary advice and training programs.

[1758] Step 5:

[1759] The server provides recommended dietary advice, training programs, and related products to the user.

[1760] Input: Recommended nutrition, training equipment

[1761] Output: Suggestions to the user

[1762] Specific operation: The cloud server sends the generated suggestions to the smartphone application and displays them to the user.

[1763] Step 6:

[1764] Users purchase recommended nutritional foods and training equipment through the application.

[1765] Input: User's purchase intent

[1766] Output: Purchase confirmation, payment completed

[1767] Specific action: The user presses the purchase button and completes payment through an electronic payment system.

[1768] This way, users can easily find and purchase the diet plan and related products that best suit them.

[1769] Example 3

[1770] Next, a third embodiment of the third embodiment will be described. 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."

[1771] Conventional diet support systems were unable to fully utilize data on users' past failures, making it difficult to support sustained diet success. Furthermore, because they did not provide personalized training or dietary advice, users were likely to repeat the same setbacks. Furthermore, they lacked a mechanism for effectively collecting and analyzing user feedback and updating plans, which led to problems with the effects of dieting not being sustained.

[1772] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1773] In this invention, the server includes means for collecting user data, means for saving and managing the collected data, means for using a generative AI model to analyze the saved data, means for generating personalized training and dietary advice based on the analysis results, means for delivering the generated plan to the user, means for collecting user feedback, and means for analyzing the collected feedback and updating the plan. This makes it possible to utilize data on the user's past failures to support sustained diet success.

[1774] "User data" refers to information entered by the user, such as past diet history, failure patterns, current weight, target weight, dietary content, and exercise habits.

[1775] The "means of collection" refers to the system in which users enter data using a smartphone app or web form and send it to a server.

[1776] "Means for storing and managing" refers to a mechanism by which the server stores the data it receives in a database and manages it appropriately.

[1777] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates personalized training and dietary advice.

[1778] The "means of analysis" refers to a mechanism that uses a generative AI model to analyze stored data and identify users' past patterns of failure and success.

[1779] "Personalized training and dietary guidance" refers to individually customized training plans and dietary advice based on the analysis results.

[1780] The "means of delivery" refers to a mechanism for sending the generated training plan and dietary advice from the server to the terminal and notifying the user.

[1781] The "means of collecting feedback" is a system in which users enter their daily progress and new data into their devices and send it to the server.

[1782] The "means of analyzing feedback and updating plans" refers to a mechanism that re-enters the collected feedback into the generative AI model and updates the training plan and dietary advice based on the analysis results.

[1783] This invention is a system that analyzes data on users' past failures and supports their continued success in dieting. Specific embodiments of this system will be described below.

[1784] Collection of User Data

[1785] Using a smartphone app or web form, users enter information such as their past diet history, failure patterns, current weight, goal weight, dietary habits, exercise habits, etc. For example, a user might enter, "In the past, I often ate sweets late at night."

[1786] Data storage and management

[1787] The terminal sends the data entered by the user to the server, which then stores the received data in a database (e.g., MySQL or PostgreSQL) and manages it appropriately.

[1788] Data analysis

[1789] The server inputs the stored data into a generative AI model (for example, OpenAI's GPT-4) and performs analysis. Specifically, it identifies the user's past patterns of failure and success and obtains data-based insights. For example, the analysis may yield results such as, "This user has a tendency to eat sweets late at night."

[1790] Generate a personalized plan

[1791] The server uses the generative AI model to generate optimal training plans and dietary advice for the user. Specifically, it takes into account the user's past data and provides individually customized advice. For example, it generates specific advice such as "Prepare low-calorie snacks to suppress appetite late at night."

[1792] Plan Delivery

[1793] The server sends the generated training plan and dietary advice to the device. The device then displays the received plan to the user. Specifically, the device provides information using the smartphone app's notification function or dashboard. For example, the device may notify the user, "Prepare a low-calorie snack to suppress your appetite late at night."

[1794] Collecting user feedback

[1795] Users input their daily progress and new data (e.g., weight changes and dietary information) into the device. Specifically, they input their daily food and exercise records into the app. For example, they might input, "I had a low-calorie snack late at night today."

[1796] Analyzing feedback and updating plans

[1797] The device sends the new data entered by the user to the server. The server then inputs the received data into the generative AI model again and analyzes it. Based on the analysis results, the training plan and dietary advice are updated and sent again to the device. For example, if the analysis result shows that "low-calorie snacks are effective," the training plan and dietary advice are updated.

[1798] Prompt Sentence Examples

[1799] "Generate optimal training plans and dietary advice for the user based on their past diet history and failure patterns. The user has a tendency to eat sweets late at night. Please provide specific advice taking this failure pattern into consideration."

[1800] In this way, the diet support system utilizes the user's past data to support continuous diet success. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1801] Step 1:

[1802] Users use a smartphone app or web form to enter information such as their past diet history, failure patterns, current weight, target weight, dietary habits, and exercise habits.

[1803] Input: past diet history, failure patterns, current weight, goal weight, dietary content, exercise habits

[1804] Output: User data entered

[1805] Step 2:

[1806] The terminal transmits the data entered by the user to the server.

[1807] Input: User data entered by the user

[1808] Output: User data sent to the server

[1809] Step 3:

[1810] The server stores the received data in a database and manages it appropriately, using a database management system such as MySQL or PostgreSQL.

[1811] Input: User data sent to the server

[1812] Output: User data stored in the database

[1813] Step 4:

[1814] The server inputs the stored data into a generative AI model (such as OpenAI's GPT-4) for analysis, specifically identifying the user's past patterns of failure and success and deriving insights from the data.

[1815] Input: User data stored in the database

[1816] Output: Analysis results (e.g., "This user tends to eat sweets late at night")

[1817] Step 5:

[1818] The server uses the generative AI model to generate optimal training plans and dietary advice for each user, taking into account the user's past data and providing individually customized advice.

[1819] Input: Analysis results

[1820] Output: Personalized training plan and dietary advice (e.g., "Pack a low-calorie snack to curb late-night cravings")

[1821] Step 6:

[1822] The server sends the generated training plan and dietary advice to the device, which then displays the received plan to the user. Specifically, the information is provided using the smartphone app's notification function and dashboard.

[1823] Input: Personalized training plans and nutrition advice

[1824] Output: A training plan or dietary advice displayed to the user (e.g., "Pack a low-calorie snack to curb your appetite later in the night")

[1825] Step 7:

[1826] The user inputs their daily progress and new data (e.g., weight changes and dietary information) into the device. Specifically, they input their daily diet and exercise records into the app.

[1827] Input: Daily progress and new data (e.g., "I had a low-calorie snack late at night today")

[1828] Output: New data entered

[1829] Step 8:

[1830] The device sends the new data to the server. The server then inputs the received data back into the generative AI model for analysis. Based on the analysis results, the training plan and dietary advice are updated and sent back to the device.

[1831] Input: New data

[1832] Output: Updated training plan and dietary advice (e.g., "Low-calorie snacks are effective")

[1833] In this way, the diet support system utilizes the user's past data to support sustained diet success.

[1834] (Application example 3)

[1835] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1836] Conventional diet support systems do not provide personalized guidance that takes into full consideration the user's past diet history or failure patterns, which means that users often end up repeating the same setbacks. Furthermore, because they do not sell special nutritional foods or training equipment, it is difficult for users to obtain the resources they need in one place. Furthermore, they lack the functionality to suggest optimal meal menus for users, making it difficult to support sustained diet success.

[1837] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, and means for analyzing the user's past diet history and failure patterns and proposing personalized meal menus. This makes it possible to prevent the user from repeating the same failures and support continuous diet success.

[1838] "VR technology" is a technology that uses virtual reality to provide users with a visual and experiential training environment.

[1839] "Generative AI" is an artificial intelligence technology that analyzes a user's past data and generates personalized training and dietary advice.

[1840] "Personalized training and dietary guidance" means providing customized training and dietary plans based on the user's individual needs and past history.

[1841] "Special nutritional foods" are foods specially designed for the purpose of dieting or maintaining health.

[1842] "Training equipment" refers to equipment used by users for physical training.

[1843] "User's past diet history" refers to records and data of diets that the user has undertaken in the past.

[1844] "Failure patterns" refer to the causes and tendencies that led users to abandon or fail diets in the past.

[1845] A "personalized meal menu" is a meal plan customized based on the user's past diet history and failure patterns.

[1846] A system for carrying out this invention is configured as follows: The server includes means for using VR technology, means for providing personalized training and dietary advice using generative AI, means for selling special nutritional foods and training equipment, and means for analyzing a user's past diet history and failure patterns and proposing a personalized meal menu.

[1847] The server first retrieves the user's past diet history and failure patterns from a database. This data...

Claims

1. A means for collecting data regarding a user's body makeup; A means for analyzing the data using a generative AI model and identifying a failure pattern regarding the user's body makeover; A means for generating and proposing at least one of information on a personalized training plan and dietary advice based on the identified failure pattern; means for acquiring an emotional state of the user using an emotion engine and providing feedback based on the acquired emotional state of the user; A system including:

2. The providing means provides feedback to maintain the motivation of the user using the generative AI model based on a prompt sentence corresponding to the emotional state of the user. The system of claim 1 .

3. means for collecting feedback from said users; and a means for analyzing the collected feedback from the user and updating at least one of the information on the training plan and the information on the dietary advice. The system of claim 1 .

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