System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving body information and a goal input by a user; means for generating a meal plan and an exercise plan based on the body information and the goal; means for providing the generated meal plan and exercise plan to the user; means for inputting a meal and exercise record; means for storing and analyzing the input meal and exercise record; means for updating the meal plan and the exercise plan based on the analysis result; and means for collecting seasonal ingredient information and reflecting the information in the meal plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, the rise in obesity and lifestyle-related diseases has made healthy eating and weight management increasingly important. However, many people struggle to create individualized diet and exercise plans. As a result, implementing effective diets has become a challenge. Furthermore, there is a lack of personalized support tailored to individual eating habits and exercise levels, making it often difficult to maintain motivation. This invention aims to solve these problems and support effective and sustainable dieting. [Means for solving the problem]

[0005] The present invention provides a system including: a means for receiving physical information and goals input by a user; a means for generating a meal plan and an exercise plan based on the physical information and goals; a means for the user to input a meal and exercise record; a means for saving and analyzing the input meal and exercise record; a means for updating the meal plan and exercise plan based on the analysis results; and a means for collecting seasonal food information and reflecting it in the meal plan. The system may also include a means for linking with the user's healthcare app and acquiring the user's weight and exercise data from the healthcare app. This allows for the provision of a personalized diet plan tailored to the user's individual needs, enabling effective weight management and maintaining the user's motivation.

[0006] "User" refers to a person who uses the system to input physical information and goals and follow the provided meal and exercise plans.

[0007] "Physical information" refers to basic physical information such as age, sex, height, and weight entered by the user.

[0008] "Goal" refers to a diet goal such as the weight the user wants to achieve or the set period of time.

[0009] "Meal Plan" refers to a personalized meal plan that is generated based on a user's physical information and goals.

[0010] An "exercise plan" refers to an individual exercise content that is generated based on the user's physical information and goals.

[0011] A "healthcare app" refers to a software application that records and manages a user's weight and exercise data.

[0012] "Generative AI model" refers to a program that uses artificial intelligence algorithms to generate optimal meal and exercise plans based on a user's physical information and goals.

[0013] "Database" means the data management system used to store and manage User's physical information, goals, diet and exercise records.

[0014] "Analysis" refers to the process of evaluating the user's progress based on the entered food and exercise records and adjusting the plan as needed.

[0015] "Seasonal food information" refers to information about the best ingredients for each season, and is used in meal plans.

[0016] "Record" refers to information that a user inputs and saves about their daily diet and exercise. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a system for supporting dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals. Specific embodiments of this system are described below.

[0039] System configuration and operation

[0040] This system consists of a user, a terminal, a server, and a generative AI model. The role and operation of each component are explained below.

[0041] 1. Enter your user information

[0042] The user accesses the app using a device and first enters their basic physical information (age, gender, height, weight, etc.) and diet goals (e.g., target weight and target period). The entered information is then sent from the device to the server.

[0043] 2. Save information and generate plans

[0044] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal meal and exercise plan. This plan generation process is customized based on the user's individual needs.

[0045] Examples:

[0046] For example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg,

[0047] The generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, then suggests optimal meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise regimens (30 minutes of jogging three times a week).

[0048] 3. Presenting the plan

[0049] The meal plan and exercise plan generated by the server are sent to the terminal, which displays these plans in a user-friendly format, and the user follows the proposed plan to follow their daily diet and exercise routine.

[0050] 4. Food and exercise tracking

[0051] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[0052] Examples:

[0053] For example, if a user has yogurt and fruit for breakfast, the details of the meal (yogurt, fruit) and calorie information are entered into the terminal and sent to the server. Similarly, if a user goes jogging, the amount of exercise is recorded.

[0054] 5. Analyze records and adjust plans

[0055] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[0056] 6. Providing information on seasonal ingredients

[0057] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[0058] Examples:

[0059] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (for example, sweet potato soup and stir-fried mushrooms) are suggested.

[0060] Linking with Healthcare App

[0061] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. This allows for more accurate and detailed data to be used to customize the plan. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[0062] This invention allows users to follow individually customized meal and exercise plans, enabling them to achieve an effective diet. Regular progress assessments and plan adjustments help users maintain their motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[0063] The processing flow will be explained below.

[0064] Specific steps of the program's processing

[0065] 1. Enter and save user information

[0066] Step 1:

[0067] The user uses the device to input their physical information (age, sex, height, weight) and diet goals (target weight, target period) into the app.

[0068] Step 2:

[0069] The terminal checks the entered information and converts it into JSON format.

[0070] Step 3:

[0071] The terminal sends the converted JSON data to the server.

[0072] Step 4:

[0073] The server parses the received JSON data and stores it in a database.

[0074] 2. Generate meal and exercise plans

[0075] Step 5:

[0076] The server retrieves the user's physical information and goals from a database.

[0077] Step 6:

[0078] The server inputs the acquired data into a generative AI model to generate optimal meal and exercise plans for the user.

[0079] Step 7:

[0080] The server converts the generated plan into JSON format and sends it to the terminal.

[0081] Step 8:

[0082] The terminal displays the received plan data in a format that is easy for the user to view.

[0083] 3. Daily diet and exercise records

[0084] Step 9:

[0085] The user inputs the details of their daily meals and the amount of exercise they do into the recording screen of the terminal.

[0086] Step 10:

[0087] The terminal checks the entered records and converts them into JSON format.

[0088] Step 11:

[0089] The terminal sends the converted JSON data to the server.

[0090] Step 12:

[0091] The server parses the received recording data and stores it in a database.

[0092] 4. Regularly analyze and adjust your plan

[0093] Step 13:

[0094] The server periodically retrieves the user's diet and exercise record data from the database.

[0095] Step 14:

[0096] The server analyzes the acquired data and evaluates the user's progress.

[0097] Step 15:

[0098] The server again uses the generative AI model to update the meal and exercise plans as needed.

[0099] Step 16:

[0100] The server converts the updated plan into JSON format and sends it to the device.

[0101] Step 17:

[0102] The terminal notifies the user of the updated plan.

[0103] 5. Providing information on seasonal ingredients

[0104] Step 18:

[0105] The server collects seasonal food information from external information sources.

[0106] Step 19:

[0107] The server stores the collected information on seasonal ingredients in a database.

[0108] Step 20:

[0109] The server uses the stored seasonal ingredient information to reflect seasonal ingredients in meal plans based on a generative AI model.

[0110] Step 21:

[0111] The server generates recipes using seasonal ingredients and sends them to the terminal.

[0112] Step 22:

[0113] The terminal proposes the generated recipe to the user.

[0114] Linking with Healthcare App

[0115] Step 23:

[0116] The server receives weight and exercise data from the user's healthcare app.

[0117] Step 24:

[0118] The server stores the received data in a database.

[0119] Step 25:

[0120] The server applies the additional stored data to the generative AI model to generate a highly accurate plan.

[0121] Through the above process, users can implement individually customized meal and exercise plans, and regular progress assessments and plan updates are provided to support effective dieting.

[0122] Example 1

[0123] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0124] Conventional diet support systems often provide uniform meal and exercise plans, which lack sufficient customization based on the user's individual physical information and diet goals. Furthermore, because they do not utilize seasonal ingredients or the latest data from external sources, it is difficult to provide a plan that users can continue without getting bored. Furthermore, they lack appropriate feedback based on the user's detailed activity data, such as data obtained from linked health management software.

[0125] 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.

[0126] In this invention, the server includes means for receiving physical information and goals input by a user, means for using a generative model to generate a meal plan and an exercise plan based on the physical information and goals, means for the user to input a meal and exercise record, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for collecting seasonal food information and reflecting it in the meal plan, and means for updating the generative model using data obtained from an external information source. This makes it possible to provide customized plans according to the individual needs of users, create menus that never get boring using seasonal ingredients, and provide precise feedback based on detailed data.

[0127] "Physical information" refers to basic physical data such as the user's age, sex, height, weight, etc.

[0128] The "goal" is a specific target value for dieting set by the user, such as a target weight or a target period.

[0129] A "generative model" is an algorithm or program that uses artificial intelligence to generate optimal meal and exercise plans based on a user's physical information and goals.

[0130] A "meal plan" is a meal plan or menu suggested to suit a user's individual physical information and goals.

[0131] An "exercise plan" is a suggested exercise schedule or exercises based on the user's individual physical information and goals.

[0132] "Records" are information entered by the user regarding the details of daily meals and the amount of exercise.

[0133] "Analysis" is the process of evaluating a user's progress based on the stored records and updating their meal and exercise plans as needed.

[0134] "Seasonal food information" is information about fresh ingredients that are considered to be optimal for each season.

[0135] "External information sources" refers to any data source obtained from outside the system, including food databases and health management software, for example.

[0136] "Health management software" is an application that manages health data such as a user's weight and exercise.

[0137] This invention is a system that supports dieting by proposing optimal meal and exercise plans based on the user's individual physical information and diet goals. This system is mainly composed of a user, a terminal, a server, and a generative AI model.

[0138] System configuration and operation

[0139] 1. Enter your user information

[0140] Users access the diet support app using a device such as a smartphone or PC and enter their basic physical information (age, gender, height, weight, etc.) and diet goals (target weight and target period). This information is sent from the device to the server.

[0141] As a specific example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg, the input information is sent to the server and stored in the database.

[0142] Example prompt sentence:

[0143] "Write a program that inputs a user's physical information and diet goals and suggests optimal meal and exercise plans. The user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg. Calculate the user's basal metabolic rate and activity level, and suggest specific meal and exercise menus."

[0144] 2. Save information and generate plans

[0145] The server stores the received user's physical information and goals in a database and generates an optimal meal and exercise plan using a generative AI model, which creates a customized plan based on the user's individual physical information and goals.

[0146] For example, the generative AI model calculates the user's basal metabolic rate and activity level, and then suggests specific meal menus and exercise plans based on that. A specific meal menu example might be yogurt and fruit for breakfast, salad and chicken breast for lunch, and stir-fried vegetables and fish for dinner. The exercise suggestion is 30 minutes of jogging three times a week.

[0147] 3. Presenting the plan

[0148] The generated meal and exercise plans are sent from the server to the device. The device parses the received data and displays it on the user's UI. The meal and exercise plans can be displayed in a list format by day or week, allowing the user to follow the suggested plans for daily diet and exercise.

[0149] 4. Food and exercise tracking

[0150] Users record their daily dietary habits and exercise amounts on their devices and send the information to a server. The entered data is stored in a database. For example, if a user eats yogurt and fruit for breakfast, the dietary habits and calorie information are sent from the device to the server and stored in the database.

[0151] 5. Analyze records and adjust plans

[0152] The server periodically analyzes the stored diet and exercise records to evaluate the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and notified to the user, allowing the user to continue their diet based on the updated plan.

[0153] 6. Providing information on seasonal ingredients

[0154] The server collects seasonal food information from external sources and incorporates it into the generative AI model. This generates recipes using seasonal ingredients and provides them to the user. For example, in autumn, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (e.g., sweet potato soup and stir-fried mushrooms) are suggested.

[0155] Linking with Healthcare App

[0156] This system works with the user's health management software to obtain weight and exercise data, allowing for more accurate and detailed customized plans. The server receives the data from the health management software and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates plans.

[0157] This invention allows users to follow individually customized meal and exercise plans, enabling them to achieve an effective diet. Regular progress assessments and plan adjustments help users maintain their motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1:

[0160] The user accesses the diet support app using a device and inputs their basic physical information and diet goals, including age, gender, height, weight, target weight, and target period. By pressing the "Send" button, the input information is sent from the device to the server.

[0161] Input: Age, gender, height, weight, target weight, target period

[0162] Output: User's physical information and goals received by the server

[0163] Step 2:

[0164] The server receives the user's submitted physical information and goals and stores them in a database, where they are associated with the user ID.

[0165] Input: User information sent from the device

[0166] Output: User information stored in the database

[0167] Step 3:

[0168] The server uses the stored information to provide input data to the generative AI model, which calculates the user's basal metabolic rate and activity level and generates an optimal meal plan and exercise plan.

[0169] Input: Saved user information

[0170] Output: Generated meal and exercise plans

[0171] Step 4:

[0172] The server converts the generated meal plan and exercise plan into a data format such as JSON and sends it to the device, which parses the received data and displays it on the user's UI.

[0173] Input: Generated meal and exercise plans

[0174] Output: Meal and exercise plan displayed on device

[0175] Step 5:

[0176] Users record their daily diet and exercise on their device, which then sends the records to a server and stores them in a database.

[0177] Input: User-logged food and exercise information

[0178] Output: Records stored in the database

[0179] Step 6:

[0180] The server periodically analyzes the stored diet and exercise records and generates new diet and exercise plans using a generative AI model, which are then sent back to the device and notified to the user.

[0181] Input: Stored recordings and generative AI models

[0182] Output: Updated meal and exercise plans

[0183] Step 7:

[0184] The server collects seasonal ingredient information from external sources and reflects it in the generative AI model. For example, if it is autumn, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms will be generated and provided to the user.

[0185] Input: Seasonal food information from external sources

[0186] Output: Meal plan incorporating seasonal ingredients

[0187] (Application example 1)

[0188] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0189] Conventional diet support systems provide meal plans and exercise plans based on the user's physical information and diet goals, but lack support when the user actually purchases ingredients and products in stores. This requires the user to take the time and effort to select the appropriate ingredients and products themselves, which can result in the diet plan not being carried out effectively. Furthermore, there is an issue of increased time and effort required to find the ingredients and products that best suit the user's diet goals, which can decrease the user's motivation.

[0190] 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.

[0191] In this invention, the server includes means for receiving physical information and goals input by the user, means for generating a meal plan and exercise plan based on the physical information and goals, means for providing the generated meal plan and exercise plan to the user, means for the user to input a record of meals and exercise, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for navigating the locations and information of ingredients and products in the store, means for recommending ingredients and products that are optimal for the user's diet goal, and means for collecting seasonal ingredient information and reflecting it in the meal plan. This allows the user to receive appropriate support when selecting ingredients and products in a physical store, enabling them to carry out an effective diet plan.

[0192] "User" refers to an individual who uses the system.

[0193] "Physical information" refers to information about an individual's physical characteristics, such as age, sex, height, and weight.

[0194] "Goal" refers to a diet-related goal that a user wants to achieve (e.g., a target weight or a target period).

[0195] "Meal Plan" refers to specific meal suggestions based on a user's physical information and goals.

[0196] An "exercise plan" refers to specific exercise content suggested based on the user's physical information and goals.

[0197] "Navigation" refers to guiding users to find the products or ingredients they are looking for within a store.

[0198] "Recommendation" refers to suggesting ingredients or products that best suit the user's goals.

[0199] "Saving" refers to storing input data in a storage device such as a database.

[0200] "Analysis" refers to evaluating a user's progress and trends based on collected data.

[0201] "Update" refers to modifying the meal plan and exercise plan based on the analysis results.

[0202] "Information on seasonal ingredients" refers to information on ingredients that are most nutritious and fresh in a particular season.

[0203] "Server" refers to the central device of the system that processes various data such as user information, plan generation, storage, and analysis.

[0204] An embodiment of the present invention is a system for users to receive diet support at a physical store, and this system is composed of a server, a terminal, and a generative AI model. The specific configuration and operation of the system are described in detail below.

[0205] System configuration and operation

[0206] This system mainly provides user information input, plan generation, in-store navigation, and a recommendation system. The role and operation of each component are explained in detail below.

[0207] 1. Enter and save user information

[0208] Users access the app using a smartphone or another device and first enter their basic physical information (age, gender, height, weight) and diet goals (target weight and target period). The entered information is sent from the device to a server and stored in a database.

[0209] 2. Plan generation and provision

[0210] The server uses a generative AI model to generate optimal meal and exercise plans based on the user's physical information and diet goals. This generation process is customized based on the needs of each individual user. The generated meal and exercise plans are then sent to the device and provided to the user.

[0211] 3. In-store navigation

[0212] When users visit a physical store, they can use their smartphone or smart glasses to navigate to the location of the ingredients or products they want within the store, including by tracking their current location in real time and displaying the optimal route.

[0213] 4. Food and product recommendations

[0214] The generative AI model also has the ability to recommend the best ingredients and products in stores based on the user's diet goals, making it easy for users to find the products that are best suited to their diet.

[0215] 5. Recording and analyzing diet and exercise

[0216] Users record their daily diet and exercise routines on their devices. These records are sent to a server and stored in a database. The server periodically analyzes the stored records to assess the user's progress. If necessary, the generative AI model is used again to update the diet and exercise plans.

[0217] 6. Providing information on seasonal ingredients

[0218] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[0219] Specific examples of hardware and software used

[0220] Hardware: Smartphones, smart glasses, servers

[0221] Software: Django (server-side framework), React Native (front-end framework), Hugging Face Transformers (generative AI model)

[0222] Examples:

[0223] For example, when a user wears smart glasses and enters a brick-and-mortar store, they will see information such as "Sweet potatoes are low in calories and in season now" and a suggested recipe for "Sweet Potato Soup."

[0224] "Please enter your user information (age, gender, height, weight) and diet goal (target weight, period)"

[0225] "We will guide you to the best products for your diet based on your current location."

[0226] "Why not try my recommended recipe, sweet potato soup?"

[0227] As described above, the present invention is a system that supports users in effectively carrying out diet plans in physical stores, and is expected to increase user satisfaction and results.

[0228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0229] Step 1:

[0230] A user input physical information and goals are received.

[0231] Input: Information such as age, gender, height, weight, target weight, and target period entered by the user via a device (such as a smartphone).

[0232] Data processing: The terminal converts the input data into an appropriate format for transmission to the server.

[0233] Output: User information is sent to the server and stored.

[0234] Example: A user inputs "30 years old, male, height 180cm, weight 85kg, target weight 75kg, target period 6 months."

[0235] Step 2:

[0236] The server generates a plan using a generative AI model.

[0237] Input: User's physical information and diet goals stored on the server.

[0238] Data calculation: The generative AI model calculates basal metabolic rate and calorie intake based on user information, and generates optimal meal and exercise plans.

[0239] Output: Meal and exercise plans are generated and stored on the server.

[0240] Example: Calculate the user's basal metabolic rate, calculate the amount of calories needed per day, and then suggest menu items such as yogurt for breakfast, salad for lunch, and fish dishes for dinner.

[0241] Step 3:

[0242] The server provides the generated plan to the user.

[0243] Input: Generated meal and exercise plans.

[0244] Data processing: Format the plan into an easy-to-read format and send it to the device.

[0245] Output: The meal and exercise plan is displayed on the user's device.

[0246] Example: A user's smartphone screen displays "Today's menu: Breakfast = yogurt, lunch = salad, dinner = fish dish."

[0247] Step 4:

[0248] A user visits a physical store and begins navigation.

[0249] Input: User's current location (e.g. GPS) and a map of the store.

[0250] Data calculation: Based on the user's current location, the device calculates the shortest route to the desired ingredients or products and displays the route on a map of the store.

[0251] Output: The location and route of the desired ingredients or products are displayed on the user's device.

[0252] Example: The smart glasses display will show navigation such as "Sweet potato section → go straight ahead 20m on the right."

[0253] Step 5:

[0254] The server recommends ingredients and products based on the user's diet goals.

[0255] Input: User's physical information, diet goals, and current location.

[0256] Data computation: Generative AI models recommend ingredients and products that best fit a user's goals and incorporate this information into navigation.

[0257] Output: Recommended product information is displayed on the user's device.

[0258] Example: Smart glasses display the message, "This product is low in calories and perfect for dieting."

[0259] Step 6:

[0260] The user enters a diet and exercise log.

[0261] Input: Daily diet and exercise information entered via the device.

[0262] Data processing: The terminal converts the input data into an appropriate format for transmission to the server.

[0263] Output: Food and exercise records are stored on a server.

[0264] Example: A user inputs "Breakfast: yogurt 200kcal, exercise: 30 minutes of jogging."

[0265] Step 7:

[0266] The server analyzes the records and updates the plan.

[0267] Input: Your saved daily food and exercise logs.

[0268] Data calculation: The server analyzes the user's progress based on the stored records and generates a new meal and exercise plan, again using the generative AI model.

[0269] Output: A new updated meal and exercise plan is generated and sent to the user device.

[0270] Example: If the user is making good progress towards their goal, a new exercise plan is suggested: "Increase jogging distance."

[0271] Step 8:

[0272] The server reflects seasonal food information.

[0273] Input: Seasonal food information from external sources.

[0274] Data processing: The server applies this information to the generative AI model to generate an optimized meal plan.

[0275] Output: A meal plan using seasonal ingredients is sent to the user's device.

[0276] Example: In the fall, "sweet potato soup" and "stir-fried mushrooms" are suggested.

[0277] The specific operation has been explained above based on each processing step, and the overall picture of the system that allows users to receive effective diet support at a physical store has been detailed.

[0278] 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.

[0279] The present invention is a system that effectively supports dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals, and by combining this with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0280] System configuration and operation

[0281] This system consists of a user, a terminal, a server, a generative AI model, and an emotion engine. The role and operation of each component are explained below.

[0282] 1. Enter your user information

[0283] The user accesses the app using a device and first enters their basic physical information (age, gender, height, weight, etc.) and diet goals (e.g., target weight and target period). The entered information is then sent from the device to the server.

[0284] 2. Save information and generate plans

[0285] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal meal and exercise plan. This plan generation process is customized based on the user's individual needs.

[0286] Examples:

[0287] For example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg,

[0288] The generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, then suggests optimal meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise regimens (30 minutes of jogging three times a week).

[0289] 3. Introducing the Emotion Engine

[0290] The emotion engine recognizes emotions from user input and sensor data and stores them in a database, allowing the system to understand the user's emotional state in real time and use it to adjust plans.

[0291] Examples:

[0292] If a user's motivation drops while following a plan, the emotion engine will recognize the change in emotion and adjust the diet and exercise plan. The emotion engine also analyzes the user's past emotion history and provides specific advice to increase the user's motivation.

[0293] 4. Presenting the plan

[0294] The meal plan and exercise plan generated by the server are sent to the terminal, which displays these plans in a user-friendly format, and the user follows the proposed plan to follow their daily diet and exercise routine.

[0295] 5. Food and exercise tracking

[0296] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[0297] Examples:

[0298] For example, if a user has yogurt and fruit for breakfast, the details of the meal (yogurt, fruit) and calorie information are entered into the terminal and sent to the server. Similarly, if a user goes jogging, the amount of exercise is recorded.

[0299] 6. Analyze records and adjust plans

[0300] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[0301] 7. Providing information on seasonal ingredients

[0302] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[0303] Examples:

[0304] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (for example, sweet potato soup and stir-fried mushrooms) are suggested.

[0305] Linking with Healthcare App

[0306] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. This allows for more accurate and detailed data to be used to customize the plan. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[0307] Maintaining motivation with an emotional engine

[0308] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice based on the emotion. For example, if the user feels stressed or anxious while following a plan, the emotion engine reports that information to the server, which then adjusts parts of the plan. The emotion engine also predicts the user's reaction in certain situations based on past emotional history and provides preventative advice.

[0309] Examples:

[0310] If a user tends to feel stressed during a particular training session, the emotion engine can use that information to provide advice on how to relax beforehand or change the exercise plan to a different format.

[0311] This invention allows users to implement individually customized meal plans and exercise plans, and provides support that responds to emotional changes. This allows for effective dieting and health management, and can continuously increase the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, and the user can continue without getting bored.

[0312] The processing flow will be explained below.

[0313] Specific steps of the program's processing (including the emotion engine)

[0314] 1. Enter and save user information

[0315] Step 1:

[0316] The user uses the device to input their physical information (age, sex, height, weight) and diet goals (target weight, target period) into the app.

[0317] Step 2:

[0318] The terminal checks the entered information and converts it into JSON format.

[0319] Step 3:

[0320] The terminal sends the converted JSON data to the server.

[0321] Step 4:

[0322] The server parses the received JSON data and stores it in a database.

[0323] 2. Generate meal and exercise plans

[0324] Step 5:

[0325] The server retrieves the user's physical information and goals from a database.

[0326] Step 6:

[0327] The server inputs the acquired data into a generative AI model to generate optimal meal and exercise plans for the user.

[0328] Step 7:

[0329] The server converts the generated plan into JSON format and sends it to the terminal.

[0330] Step 8:

[0331] The terminal displays the received plan data in a format that is easy for the user to view.

[0332] 3. Introducing the Emotion Engine

[0333] Step 9:

[0334] The user inputs their daily emotional state into the terminal, which then transmits this information to the emotion engine.

[0335] Step 10:

[0336] The device collects data from built-in or external sensors (e.g., heart rate and facial expression recognition data) and sends it to the emotion engine.

[0337] Step 11:

[0338] The emotion engine analyzes the received data and recognizes the user's emotional state.

[0339] Step 12:

[0340] The emotion engine sends the analysis results to the server.

[0341] 4. Presenting the plan

[0342] Step 13:

[0343] The meal plan and exercise plan generated by the server are sent to the terminal.

[0344] Step 14:

[0345] The terminal displays these plans in a user-friendly format.

[0346] Step 15:

[0347] The user follows the suggested diet and exercise plan daily.

[0348] 5. Food and exercise tracking

[0349] Step 16:

[0350] The user inputs the details of their daily meals and the amount of exercise they do into the recording screen of the terminal.

[0351] Step 17:

[0352] The terminal checks the entered records and converts them into JSON format.

[0353] Step 18:

[0354] The terminal sends the converted JSON data to the server.

[0355] Step 19:

[0356] The server parses the received recording data and stores it in a database.

[0357] 6. Analyze records and adjust plans

[0358] Step 20:

[0359] The server periodically retrieves the user's diet and exercise record data from the database.

[0360] Step 21:

[0361] The server integrates the acquired data with the emotion engine data, analyzes it, and evaluates the user's progress.

[0362] Step 22:

[0363] The server uses the generative AI model to update the meal and exercise plans as needed.

[0364] Step 23:

[0365] The server converts the updated plan into JSON format and sends it to the device.

[0366] Step 24:

[0367] The terminal notifies the user of the updated plan.

[0368] 7. Providing information on seasonal ingredients

[0369] Step 25:

[0370] The server collects seasonal food information from external information sources.

[0371] Step 26:

[0372] The server stores the collected information on seasonal ingredients in a database.

[0373] Step 27:

[0374] The server uses the stored seasonal ingredient information to reflect seasonal ingredients in meal plans based on a generative AI model.

[0375] Step 28:

[0376] The server generates recipes using seasonal ingredients and sends them to the terminal.

[0377] Step 29:

[0378] The terminal proposes the generated recipe to the user.

[0379] Linking with Healthcare App

[0380] Step 30:

[0381] The server receives weight and exercise data from the user's healthcare app.

[0382] Step 31:

[0383] The server stores the received data in a database.

[0384] Step 32:

[0385] The server applies the additional stored data to the generative AI model to generate a highly accurate plan.

[0386] Maintaining motivation with an emotional engine

[0387] Step 33:

[0388] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice according to their emotions.

[0389] Step 34:

[0390] Based on past emotional history, the server predicts the user's reaction in specific situations and provides preventative advice.

[0391] The above process allows users to implement individually customized meal and exercise plans. It also provides support that responds to emotional changes, enabling effective dieting and health management, and sustainably increasing the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[0392] Example 2

[0393] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0394] Conventional diet support systems have difficulty providing effective support due to their difficulty in highly customizing diet plans based on the user's individual physical information and emotional state. Furthermore, the user's information is fixed, making it difficult to provide flexible plans that respond to changing emotions and seasonal changes. The present invention aims to solve these problems by providing optimal plans that respond to the user's individual needs and emotional state, thereby improving the effectiveness of dieting.

[0395] 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 means for receiving physical information and goals input by the user, means for using a generative model to generate a meal plan and an exercise plan based on the physical information and goals, means for providing the generated meal plan and exercise plan to the user, means for the user to input a meal and exercise record, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for recognizing the user's emotional state and adjusting the plan based on this, and means for collecting seasonal food information and reflecting this in the meal plan. This enables effective diet support while responding to the user's individual physical information and emotional state.

[0396] "User" refers to an individual who uses the system to input physical information and diet goals and receives meal and exercise plans.

[0397] "Physical information" refers to basic physical information about the user, such as age, gender, height, and weight.

[0398] "Goal" refers to a specific goal for dieting that the user wants to achieve, such as a target weight or a target period.

[0399] "Meal Plan" refers to a specific daily meal plan suggested based on a user's physical information and goals.

[0400] An "exercise plan" refers to a plan that specifically outlines the exercise content for one day, proposed based on the user's physical information and goals.

[0401] "Generative model" refers to an AI model that automatically generates optimal meal and exercise plans based on a user's physical information and goals.

[0402] "Saving" refers to the act of recording and storing input data in a storage device such as a database.

[0403] "Analysis" refers to analyzing stored data and assessing a user's progress and trends.

[0404] "Emotional state" refers to various emotional states that users experience in their daily lives, and specifically includes motivation, stress, happiness, etc.

[0405] "Seasonal food information" refers to information about the freshest and most nutritious food items on the market each season.

[0406] A "healthcare app" refers to application software for managing data related to a user's body and exercise.

[0407] "Adjusting your plan" refers to the process of reviewing and optimizing your existing meal and exercise plans based on your progress and emotional state.

[0408] This invention is a system that effectively supports dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals, and by combining this with an emotion engine that recognizes the user's emotions. This system is composed of a user, a device, a server, a generative AI model, and an emotion engine. The role and operation of each component are explained below.

[0409] Entering user information

[0410] Users access the app using a device such as a smartphone or tablet. They enter basic physical information such as age, gender, height, and weight, as well as their diet goals (e.g., target weight and target period). The entered information is sent from the device to the server.

[0411] Save information and generate plans

[0412] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal diet and exercise plan. This generative AI model uses, for example, OpenAI's GPT model. The generation process is customized based on the user's individual needs.

[0413] Examples:

[0414] For example, if a user is a 35-year-old male with a height of 175 cm and a weight of 80 kg, with a target weight of 70 kg, the generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, and then suggests specific meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise options (e.g., 30 minutes of jogging three times a week).

[0415] Example prompts for input to a generative AI model:

[0416] User Information:

[0417] Age: 35

[0418] Gender: Male

[0419] Height: 175cm

[0420] Weight: 80kg

[0421] Target weight: 70kg

[0422] Prompts for input to generative AI models:

[0423] Consider your basal metabolic rate and activity level and generate optimal meal and exercise plans based on the following information:

[0424] Age: 35

[0425] Gender: Male

[0426] Height: 175cm

[0427] Weight: 80kg

[0428] Target weight: 70kg

[0429] Introducing the Emotion Engine

[0430] The emotion engine recognizes emotions from user input data and sensor data (e.g., smartwatches and fitness trackers) and stores the data in a database to understand the user's emotional state in real time. The emotion engine uses, for example, Affectiva's emotion recognition engine. The server adjusts the plan based on this.

[0431] Examples:

[0432] If a user's motivation drops while following a plan, the emotion engine will recognize the change and adjust their diet or exercise plan, or provide advice to boost their motivation.

[0433] Presenting the plan

[0434] The meal plan and exercise plan generated by the server are sent to the terminal, which displays the plan to the user in a visually easy-to-understand format. The user then follows the proposed plan to follow their daily diet and exercise routine.

[0435] Food and exercise tracking

[0436] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[0437] Examples:

[0438] If a user has yogurt and fruit for breakfast, the device will input the meal details (yogurt, fruit) and calorie information and send it to the server. Similarly, if a user goes jogging, the amount of exercise will be recorded.

[0439] Analyze records and adjust plans

[0440] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[0441] Providing information on seasonal ingredients

[0442] The server collects information on seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[0443] Examples:

[0444] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (e.g., sweet potato soup and stir-fried mushrooms) are suggested.

[0445] Linking with Healthcare App

[0446] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[0447] Maintaining motivation with an emotional engine

[0448] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice according to their emotions. It predicts the user's emotional changes in specific situations and recommends measures to increase motivation in advance.

[0449] Examples:

[0450] If a user is prone to feeling stressed during a particular training session, the emotion engine can predict this in advance and provide advice on how to relax or change the exercise plan.

[0451] This invention allows users to implement individually customized meal and exercise plans and provides support that responds to emotional changes. This also allows for effective dieting and health management, and can continuously increase the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, and the user can continue without getting bored.

[0452] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0453] Step 1:

[0454] The user accesses the app using a device and enters their physical information (e.g., age, gender, height, weight) and diet goals (e.g., target weight and target period). The information entered is then entered into a form and sent to the server by pressing the submit button. User profile data is generated based on the input and passed to the server.

[0455] Specific input example:

[0456] User input information:

[0457] Age: 30

[0458] Gender: Female

[0459] Height: 165cm

[0460] Weight: 70kg

[0461] Target weight: 60kg

[0462] Server input:

[0463] User profile data (age, gender, height, weight, goal weight)

[0464] Server output:

[0465] Profile save confirmation message

[0466] Step 2:

[0467] The server stores the received user information in a database, which is then input into a generative AI model to generate meal and exercise plans.

[0468] Specific input example:

[0469] Server input:

[0470] User profile data (age, gender, height, weight, goal weight)

[0471] Server output:

[0472] Save to database

[0473] Specific behavior:

[0474] The server connects to the database and executes a SQL query to save the user information. After the save is complete, a save success message is generated.

[0475] Step 3:

[0476] The server generates prompt sentences based on the stored user information and sends the prompt sentences to the generative AI model, which then generates optimal meal and exercise plans for the user based on the prompt sentences.

[0477] Specific input example:

[0478] Prompt for the generative AI model:

[0479] User Information:

[0480] Age: 30

[0481] Gender: Female

[0482] Height: 165cm

[0483] Weight: 70kg

[0484] Target weight: 60kg

[0485] Prompts for input to generative AI models:

[0486] Consider your basal metabolic rate and activity level and generate optimal meal and exercise plans based on the following information:

[0487] Age: 30

[0488] Gender: Female

[0489] Height: 165cm

[0490] Weight: 70kg

[0491] Target weight: 60kg

[0492] Server input:

[0493] Prompt statement

[0494] Output of the generative AI model:

[0495] Meal and exercise plans (e.g., yogurt and fruit for breakfast, salad and chicken for lunch, fish and stir-fried vegetables for dinner)

[0496] Specific behavior:

[0497] The server sends the generated prompt text to the generative AI model, which then performs calculations and data analysis to generate and return a meal plan and exercise plan.

[0498] Step 4:

[0499] The server sends the meal and exercise plans derived from the generative AI model to the device, which displays these plans in a user-friendly format.

[0500] Specific input example:

[0501] Server input:

[0502] Meal and exercise plans

[0503] Server output:

[0504] Sending plan data to the device

[0505] Type in the terminal:

[0506] Plan data received on your device

[0507] Terminal output:

[0508] User interface (displaying meal and exercise plans)

[0509] Specific behavior:

[0510] The server transmits the plan data to the terminal, which receives it and displays it on the user interface.

[0511] Step 5:

[0512] Users record their daily diet and exercise on their devices, and the recorded data is sent to a server.

[0513] Specific input example:

[0514] User input:

[0515] Food log (e.g., yogurt and fruit for breakfast)

[0516] Exercise record (e.g. 30 minutes of jogging)

[0517] Type in the terminal:

[0518] Food and exercise record data

[0519] Terminal output:

[0520] Sending recorded data to the server

[0521] Server input:

[0522] Food and exercise record data

[0523] Specific behavior:

[0524] The user enters their diet and exercise record data into the app and presses the save button, sending the data to the server.

[0525] Step 6:

[0526] The server stores the received food and exercise records in a database, and then analyzes this data to assess the user's progress.

[0527] Specific input example:

[0528] Server input:

[0529] Food and exercise record data

[0530] Server output:

[0531] Data saving operations

[0532] Progress Assessment Report

[0533] Specific behavior:

[0534] The server stores the food and exercise records in a database and analyzes them with an analytical algorithm to assess progress.

[0535] Step 7:

[0536] Based on the progress assessment results, the server re-uses the generative AI model to update the meal and exercise plans if necessary, and the new plans are sent to the device and notified to the user.

[0537] Specific input example:

[0538] Server input:

[0539] Progress evaluation results

[0540] Server output:

[0541] Updated meal and exercise plans

[0542] Type in the terminal:

[0543] New plan data

[0544] Terminal output:

[0545] View and notify renewal plans

[0546] Specific behavior:

[0547] The server uses the progress data to generate a new plan using the generative AI model and sends the result back to the device, which updates its user interface to display the new plan.

[0548] Step 8:

[0549] The server collects information on seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[0550] Specific input example:

[0551] Server input:

[0552] Seasonal food data from external sources

[0553] Server output:

[0554] Updated Recipe Plans

[0555] Type in the terminal:

[0556] New Recipe Plan

[0557] Terminal output:

[0558] Displaying recipes using seasonal ingredients

[0559] Specific behavior:

[0560] The server retrieves seasonal food data from an external API, applies it to the generative AI model, and generates a new recipe plan. The plan is then sent to the device and displayed to the user.

[0561] Step 9:

[0562] The server periodically obtains weight and exercise data from the user's healthcare app, which is then stored in a database and reflected in the generative AI model.

[0563] Specific input example:

[0564] Server input:

[0565] Weight and exercise data from the Health app

[0566] Server output:

[0567] Updated database

[0568] Specific behavior:

[0569] The server retrieves user data from the healthcare app's API and stores it in a database.

[0570] Step 10:

[0571] The emotion engine recognizes the user's emotional state in real time based on sensor data and input data, and sends the results to the server, which then generates feedback and advice based on that information and provides it to the user.

[0572] Specific input example:

[0573] Emotion Engine Input:

[0574] Sensor data and user emotion input data

[0575] Emotion engine output:

[0576] Emotion analysis results

[0577] Server input:

[0578] Emotion analysis results

[0579] Server output:

[0580] Feedback and Advice

[0581] Specific behavior:

[0582] The emotion engine analyzes the user's emotions, and the server generates feedback and advice based on that information and sends it to the device.

[0583] (Application example 2)

[0584] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0585] In today's busy lifestyles, effective support for personal health management and dieting requires providing plans customized to each user's physical information and emotional state. However, conventional systems have difficulty adjusting meal and exercise plans to accommodate emotional changes, and they lack the information to provide specific ingredients for quickly implementing the generated plans. This creates challenges for maintaining user motivation and sticking to the plan.

[0586] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving physical information and goals input by the user, means for generating a meal plan and an exercise plan based on the physical information and goals, and means for adjusting the meal plan and the exercise plan based on emotional data. This makes it possible to provide a customized plan tailored to each user's individual physical information and emotional state. In addition, by providing means for delivering ingredients based on the generated meal plan, the user can receive support in quickly implementing the plan, which is expected to maintain motivation and promote health management.

[0587] The "means for receiving user-entered physical information and goals" refers to an interface through which a user inputs their basic physical data (age, gender, height, weight, etc.) and the health goals they wish to achieve (e.g., target weight and target period), and the system receives this information.

[0588] The "means for generating a meal plan and exercise plan based on the physical information and goals" refers to an algorithm or software for generating an optimal meal and exercise plan based on the user's input physical information and health goals.

[0589] The "means for providing the generated meal plan and exercise plan to the user" refers to a device or application for presenting the meal plan and exercise plan generated by the system to the user in the form of visual or text information.

[0590] "Means for recognizing user emotions and acquiring emotional data" refers to sensors and analytical software that monitor user emotions in real time and collect that data.

[0591] The "means for adjusting meal plans and exercise plans based on said emotional data" is an algorithm or generative model for optimizing or modifying existing meal plans and exercise plans based on collected emotional data.

[0592] The "means for the user to input records of meals and exercise" refers to an interface that allows the user to input the details of daily meals and the amount of exercise into an application or device.

[0593] The "means for storing and analyzing the entered diet and exercise records" refers to software or hardware for storing the diet and exercise records entered by the user in a database for later analysis.

[0594] The "means for updating the diet and exercise plans based on the analysis results and emotional data" refers to an algorithm for analyzing the stored data and emotional data and updating the diet and exercise plans based thereon.

[0595] The "means for collecting seasonal food information and reflecting it in the meal plan" is a data acquisition system for externally collecting seasonal food information and reflecting it in the meal plan that is generated.

[0596] The "means for delivering ingredients based on the generated meal plan" is a delivery system for quickly delivering ingredients required for the generated meal plan to the user.

[0597] MODE FOR CARRYING OUT THE INVENTION

[0598] This invention is a system that proposes optimal meal and exercise plans based on a user's individual physical information, diet goals, and emotional state, and even delivers ingredients to help them carry out the plans. The main components of this system include a server, a terminal, a generative AI model, and an emotion engine.

[0599] 1. Enter and save user information

[0600] Device:

[0601] Users enter their physical information (age, sex, height, weight, etc.) and goals (e.g., target weight and target period) through an application on their smartphone or tablet.

[0602] server:

[0603] It receives information sent from the device and stores it in a database, which creates a base for various data that will be needed later.

[0604] 2. Creating a menu

[0605] server:

[0606] Based on the saved user information, a generative AI model is used to generate meal and exercise plans. The generated plans are customized based on the user's basal metabolic rate and activity level. For example, for a 35-year-old man who is 175 cm tall, weighs 80 kg, and has a target weight of 70 kg, the generative AI model calculates the user's basal metabolic rate and determines the required calorie intake. It then suggests appropriate meal plans (yogurt and fruit for breakfast, salad and chicken breast for lunch, and stir-fried vegetables and fish for dinner) and exercise options (30 minutes of jogging three times a week).

[0607] 3. Introducing the Emotion Engine

[0608] Device:

[0609] It uses the smartphone's camera, microphone, and other sensors to recognize and monitor the user's emotions in real time and obtain emotional data.

[0610] server:

[0611] Using the captured emotion data, the generative AI model can readjust meal and exercise plans as needed, for example, by changing the meal plan to one that promotes relaxation if it detects that the user is feeling stressed while following the plan.

[0612] 4. Plan presentation and ingredients provided

[0613] Device:

[0614] The optimal meal plan and exercise plan sent from the server are presented to the user.

[0615] server:

[0616] The system arranges for the delivery of the necessary ingredients based on the generated meal plan, and provides the appropriate ingredients to the user through a delivery service.

[0617] 5. Food and exercise tracking

[0618] Device:

[0619] The user inputs the details of their daily diet and the amount of exercise they do. For example, if they eat yogurt and fruit for breakfast, the details and calorie information are entered. Similarly, the amount of exercise they do is also recorded.

[0620] server:

[0621] Receives the entered data and stores it in the database.

[0622] 6. Analyze records and adjust plans

[0623] server:

[0624] The stored data is periodically analyzed to assess the user's progress, and if necessary, the generative AI model is used to regenerate meal and exercise plans and present updated plans to the user.

[0625] 7. Providing information on seasonal ingredients

[0626] server:

[0627] This system collects seasonal food information from external sources and incorporates it into the generative AI model. This makes it possible to provide users with menus that utilize seasonal ingredients. For example, in autumn, it suggests recipes using sweet potatoes and mushrooms (sweet potato soup and stir-fried mushrooms).

[0628] Examples and prompts

[0629] For example, if a user feels stressed while working towards their weight goal, the smartphone camera can analyze the user's facial expressions to detect the stress.The emotion engine then adjusts the meal plan to include foods with a high relaxation effect (such as low-fat fish or green tea), and delivers these ingredients to the user via a delivery service.

[0630] Example prompt sentence:

[0631] "We have found that a 35-year-old male user is following a diet plan, but is feeling stressed. Regenerate the optimal meal plan for this situation and suggest meal menus that will have a relaxing effect. Output the action to deliver the suggested meal menu to the user."

[0632] The invention includes specific software (such as generative AI models and emotion engines) used for all data processing and analysis, and also integrates an efficient delivery system to facilitate the execution of the meal plan.

[0633] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0634] Step 1:

[0635] Entering user information

[0636] Device: The user enters their physical information (age, gender, height, weight) and diet goals (target weight, target period) through a dedicated mobile application.

[0637] Input: Physical information and goals manually entered by the user.

[0638] Output: The input information is sent to the server in real time.

[0639] Step 2:

[0640] Retention of Information

[0641] Server: Stores the received user information in a database. This step lays the foundation for centrally managing all input data.

[0642] Input: User-entered physical information and goals.

[0643] Output: Saved user information data.

[0644] Step 3:

[0645] Generate a plan

[0646] Server: Uses a generative AI model to generate optimal meal and exercise plans based on the user's individual physical information and goals. The generation process calculates basal metabolic rate and activity level to determine the required calorie intake.

[0647] Input: Saved user information data.

[0648] Output: Optimal diet and exercise plan.

[0649] Step 4:

[0650] Presenting the plan

[0651] Device: The meal plan and exercise plan sent from the server are presented to the user on the device. The user can visually check the proposed plan through the application.

[0652] Input: Meal and exercise plan from server.

[0653] Output: Show plan.

[0654] Step 5:

[0655] Emotion recognition

[0656] On-device: The smartphone's camera, microphone, and sensors are used to capture the user's emotional data in real time. The emotion engine analyzes this data to identify the user's emotional state.

[0657] Input: Data required for emotion recognition, such as camera footage and audio data.

[0658] Output: The user's emotional state.

[0659] Step 6:

[0660] Adjusting the plan

[0661] Server: Based on the acquired emotional data, the system adjusts the diet and exercise plans as needed. The generative AI model analyzes the emotional data and makes adjustments to reduce stress and increase motivation.

[0662] Input: Emotional data and existing diet and exercise plans.

[0663] Output: A tailored diet and exercise plan.

[0664] Step 7:

[0665] Food delivery

[0666] Server: Collects the necessary ingredients based on the adjusted and generated meal plan and delivers them to the user via a delivery service. This uses the delivery service's API.

[0667] Enter: your tailored meal plan.

[0668] Output: Grocery delivery to the user's home.

[0669] Step 8:

[0670] Food and exercise tracking

[0671] Device: The user enters their daily diet and exercise information into the app.

[0672] Input: Food and exercise information manually entered by the user.

[0673] Output: The entered recorded data is sent to the server and saved.

[0674] Step 9:

[0675] Analyze records and readjust plans

[0676] Server: Periodically analyzes the user's stored food and exercise logs to assess the user's progress and, if necessary, regenerates the meal and exercise plans using generative AI models.

[0677] Input: Stored recorded data and analysis results.

[0678] Output: An updated diet and exercise plan.

[0679] Step 10:

[0680] Reflecting seasonal food information

[0681] Server: Collects seasonal ingredients from external sources and incorporates them into meal plans. This process generates recipes that are easy to use and prioritize health.

[0682] Input: Seasonal food data collected from external sources.

[0683] Output: Meal plans using seasonal ingredients.

[0684] In this way, the system provides a customized plan according to the user's physical information and emotional state and is equipped with a series of technical means to support the execution of the plan.

[0685] 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.

[0686] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0687] 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.

[0688] [Second embodiment]

[0689] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0690] 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.

[0691] 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).

[0692] 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.

[0693] 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.

[0694] 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).

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0700] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0701] The present invention is a system for supporting dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals. Specific embodiments of this system are described below.

[0702] System configuration and operation

[0703] This system consists of a user, a terminal, a server, and a generative AI model. The role and operation of each component are explained below.

[0704] 1. Enter your user information

[0705] The user accesses the app using a device and first enters their basic physical information (age, gender, height, weight, etc.) and diet goals (e.g., target weight and target period). The entered information is then sent from the device to the server.

[0706] 2. Save information and generate plans

[0707] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal meal and exercise plan. This plan generation process is customized based on the user's individual needs.

[0708] Examples:

[0709] For example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg,

[0710] The generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, then suggests optimal meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise regimens (30 minutes of jogging three times a week).

[0711] 3. Presenting the plan

[0712] The meal plan and exercise plan generated by the server are sent to the terminal, which displays these plans in a user-friendly format, and the user follows the proposed plan to follow their daily diet and exercise routine.

[0713] 4. Food and exercise tracking

[0714] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[0715] Examples:

[0716] For example, if a user has yogurt and fruit for breakfast, the details of the meal (yogurt, fruit) and calorie information are entered into the terminal and sent to the server. Similarly, if a user goes jogging, the amount of exercise is recorded.

[0717] 5. Analyze records and adjust plans

[0718] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[0719] 6. Providing information on seasonal ingredients

[0720] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[0721] Examples:

[0722] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (for example, sweet potato soup and stir-fried mushrooms) are suggested.

[0723] Linking with Healthcare App

[0724] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. This allows for more accurate and detailed data to be used to customize the plan. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[0725] This invention allows users to follow individually customized meal and exercise plans, enabling them to achieve an effective diet. Regular progress assessments and plan adjustments help users maintain their motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[0726] The processing flow will be explained below.

[0727] Specific steps of the program's processing

[0728] 1. Enter and save user information

[0729] Step 1:

[0730] The user uses the device to input their physical information (age, sex, height, weight) and diet goals (target weight, target period) into the app.

[0731] Step 2:

[0732] The terminal checks the entered information and converts it into JSON format.

[0733] Step 3:

[0734] The terminal sends the converted JSON data to the server.

[0735] Step 4:

[0736] The server parses the received JSON data and stores it in a database.

[0737] 2. Generate meal and exercise plans

[0738] Step 5:

[0739] The server retrieves the user's physical information and goals from a database.

[0740] Step 6:

[0741] The server inputs the acquired data into a generative AI model to generate optimal meal and exercise plans for the user.

[0742] Step 7:

[0743] The server converts the generated plan into JSON format and sends it to the terminal.

[0744] Step 8:

[0745] The terminal displays the received plan data in a format that is easy for the user to view.

[0746] 3. Daily diet and exercise records

[0747] Step 9:

[0748] The user inputs the details of their daily meals and the amount of exercise they do into the recording screen of the terminal.

[0749] Step 10:

[0750] The terminal checks the entered records and converts them into JSON format.

[0751] Step 11:

[0752] The terminal sends the converted JSON data to the server.

[0753] Step 12:

[0754] The server parses the received recording data and stores it in a database.

[0755] 4. Regularly analyze and adjust your plan

[0756] Step 13:

[0757] The server periodically retrieves the user's diet and exercise record data from the database.

[0758] Step 14:

[0759] The server analyzes the acquired data and evaluates the user's progress.

[0760] Step 15:

[0761] The server again uses the generative AI model to update the meal and exercise plans as needed.

[0762] Step 16:

[0763] The server converts the updated plan into JSON format and sends it to the device.

[0764] Step 17:

[0765] The terminal notifies the user of the updated plan.

[0766] 5. Providing information on seasonal ingredients

[0767] Step 18:

[0768] The server collects seasonal food information from external information sources.

[0769] Step 19:

[0770] The server stores the collected information on seasonal ingredients in a database.

[0771] Step 20:

[0772] The server uses the stored seasonal ingredient information to reflect seasonal ingredients in meal plans based on a generative AI model.

[0773] Step 21:

[0774] The server generates recipes using seasonal ingredients and sends them to the terminal.

[0775] Step 22:

[0776] The terminal proposes the generated recipe to the user.

[0777] Linking with Healthcare App

[0778] Step 23:

[0779] The server receives weight and exercise data from the user's healthcare app.

[0780] Step 24:

[0781] The server stores the received data in a database.

[0782] Step 25:

[0783] The server applies the additional stored data to the generative AI model to generate a highly accurate plan.

[0784] Through the above process, users can implement individually customized meal and exercise plans, and regular progress assessments and plan updates are provided to support effective dieting.

[0785] Example 1

[0786] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0787] Conventional diet support systems often provide uniform meal and exercise plans, which lack sufficient customization based on the user's individual physical information and diet goals. Furthermore, because they do not utilize seasonal ingredients or the latest data from external sources, it is difficult to provide a plan that users can continue without getting bored. Furthermore, they lack appropriate feedback based on the user's detailed activity data, such as data obtained from linked health management software.

[0788] 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.

[0789] In this invention, the server includes means for receiving physical information and goals input by a user, means for using a generative model to generate a meal plan and an exercise plan based on the physical information and goals, means for the user to input a meal and exercise record, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for collecting seasonal food information and reflecting it in the meal plan, and means for updating the generative model using data obtained from an external information source. This makes it possible to provide customized plans according to the individual needs of users, create menus that never get boring using seasonal ingredients, and provide precise feedback based on detailed data.

[0790] "Physical information" refers to basic physical data such as the user's age, sex, height, weight, etc.

[0791] The "goal" is a specific target value for dieting set by the user, such as a target weight or a target period.

[0792] A "generative model" is an algorithm or program that uses artificial intelligence to generate optimal meal and exercise plans based on a user's physical information and goals.

[0793] A "meal plan" is a meal plan or menu suggested to suit a user's individual physical information and goals.

[0794] An "exercise plan" is a suggested exercise schedule or exercises based on the user's individual physical information and goals.

[0795] "Records" are information entered by the user regarding the details of daily meals and the amount of exercise.

[0796] "Analysis" is the process of evaluating a user's progress based on the stored records and updating their meal and exercise plans as needed.

[0797] "Seasonal food information" is information about fresh ingredients that are considered to be optimal for each season.

[0798] "External information sources" refers to any data source obtained from outside the system, including food databases and health management software, for example.

[0799] "Health management software" is an application that manages health data such as a user's weight and exercise.

[0800] This invention is a system that supports dieting by proposing optimal meal and exercise plans based on the user's individual physical information and diet goals. This system is mainly composed of a user, a terminal, a server, and a generative AI model.

[0801] System configuration and operation

[0802] 1. Enter your user information

[0803] Users access the diet support app using a device such as a smartphone or PC and enter their basic physical information (age, gender, height, weight, etc.) and diet goals (target weight and target period). This information is sent from the device to the server.

[0804] As a specific example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg, the input information is sent to the server and stored in the database.

[0805] Example prompt sentence:

[0806] "Write a program that inputs a user's physical information and diet goals and suggests optimal meal and exercise plans. The user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg. Calculate the user's basal metabolic rate and activity level, and suggest specific meal and exercise menus."

[0807] 2. Save information and generate plans

[0808] The server stores the received user's physical information and goals in a database and generates an optimal meal and exercise plan using a generative AI model, which creates a customized plan based on the user's individual physical information and goals.

[0809] For example, the generative AI model calculates the user's basal metabolic rate and activity level, and then suggests specific meal menus and exercise plans based on that. A specific meal menu example might be yogurt and fruit for breakfast, salad and chicken breast for lunch, and stir-fried vegetables and fish for dinner. The exercise suggestion is 30 minutes of jogging three times a week.

[0810] 3. Presenting the plan

[0811] The generated meal and exercise plans are sent from the server to the device. The device parses the received data and displays it on the user's UI. The meal and exercise plans can be displayed in a list format by day or week, allowing the user to follow the suggested plans for daily diet and exercise.

[0812] 4. Food and exercise tracking

[0813] Users record their daily dietary habits and exercise amounts on their devices and send the information to a server. The entered data is stored in a database. For example, if a user eats yogurt and fruit for breakfast, the dietary habits and calorie information are sent from the device to the server and stored in the database.

[0814] 5. Analyze records and adjust plans

[0815] The server periodically analyzes the stored diet and exercise records to evaluate the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and notified to the user, allowing the user to continue their diet based on the updated plan.

[0816] 6. Providing information on seasonal ingredients

[0817] The server collects seasonal food information from external sources and incorporates it into the generative AI model. This generates recipes using seasonal ingredients and provides them to the user. For example, in autumn, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (e.g., sweet potato soup and stir-fried mushrooms) are suggested.

[0818] Linking with Healthcare App

[0819] This system works with the user's health management software to obtain weight and exercise data, allowing for more accurate and detailed customized plans. The server receives the data from the health management software and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates plans.

[0820] This invention allows users to follow individually customized meal and exercise plans, enabling them to achieve an effective diet. Regular progress assessments and plan adjustments help users maintain their motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[0821] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0822] Step 1:

[0823] The user accesses the diet support app using a device and inputs their basic physical information and diet goals, including age, gender, height, weight, target weight, and target period. By pressing the "Send" button, the input information is sent from the device to the server.

[0824] Input: Age, gender, height, weight, target weight, target period

[0825] Output: User's physical information and goals received by the server

[0826] Step 2:

[0827] The server receives the user's submitted physical information and goals and stores them in a database, where they are associated with the user ID.

[0828] Input: User information sent from the device

[0829] Output: User information stored in the database

[0830] Step 3:

[0831] The server uses the stored information to provide input data to the generative AI model, which calculates the user's basal metabolic rate and activity level and generates an optimal meal plan and exercise plan.

[0832] Input: Saved user information

[0833] Output: Generated meal and exercise plans

[0834] Step 4:

[0835] The server converts the generated meal plan and exercise plan into a data format such as JSON and sends it to the device, which parses the received data and displays it on the user's UI.

[0836] Input: Generated meal and exercise plans

[0837] Output: Meal and exercise plan displayed on device

[0838] Step 5:

[0839] Users record their daily diet and exercise on their device, which then sends the records to a server and stores them in a database.

[0840] Input: User-logged food and exercise information

[0841] Output: Records stored in the database

[0842] Step 6:

[0843] The server periodically analyzes the stored diet and exercise records and generates new diet and exercise plans using a generative AI model, which are then sent back to the device and notified to the user.

[0844] Input: Stored recordings and generative AI models

[0845] Output: Updated meal and exercise plans

[0846] Step 7:

[0847] The server collects seasonal ingredient information from external sources and reflects it in the generative AI model. For example, if it is autumn, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms will be generated and provided to the user.

[0848] Input: Seasonal food information from external sources

[0849] Output: Meal plan incorporating seasonal ingredients

[0850] (Application example 1)

[0851] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0852] Conventional diet support systems provide meal plans and exercise plans based on the user's physical information and diet goals, but lack support when the user actually purchases ingredients and products in stores. This requires the user to take the time and effort to select the appropriate ingredients and products themselves, which can result in the diet plan not being carried out effectively. Furthermore, there is an issue of increased time and effort required to find the ingredients and products that best suit the user's diet goals, which can decrease the user's motivation.

[0853] 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.

[0854] In this invention, the server includes means for receiving physical information and goals input by the user, means for generating a meal plan and exercise plan based on the physical information and goals, means for providing the generated meal plan and exercise plan to the user, means for the user to input a record of meals and exercise, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for navigating the locations and information of ingredients and products in the store, means for recommending ingredients and products that are optimal for the user's diet goal, and means for collecting seasonal ingredient information and reflecting it in the meal plan. This allows the user to receive appropriate support when selecting ingredients and products in a physical store, enabling them to carry out an effective diet plan.

[0855] "User" refers to an individual who uses the system.

[0856] "Physical information" refers to information about an individual's physical characteristics, such as age, sex, height, and weight.

[0857] "Goal" refers to a diet-related goal that a user wants to achieve (e.g., a target weight or a target period).

[0858] "Meal Plan" refers to specific meal suggestions based on a user's physical information and goals.

[0859] An "exercise plan" refers to specific exercise content suggested based on the user's physical information and goals.

[0860] "Navigation" refers to guiding users to find the products or ingredients they are looking for within a store.

[0861] "Recommendation" refers to suggesting ingredients or products that best suit the user's goals.

[0862] "Saving" refers to storing input data in a storage device such as a database.

[0863] "Analysis" refers to evaluating a user's progress and trends based on collected data.

[0864] "Update" refers to modifying the meal plan and exercise plan based on the analysis results.

[0865] "Information on seasonal ingredients" refers to information on ingredients that are most nutritious and fresh in a particular season.

[0866] "Server" refers to the central device of the system that processes various data such as user information, plan generation, storage, and analysis.

[0867] An embodiment of the present invention is a system for users to receive diet support at a physical store, and this system is composed of a server, a terminal, and a generative AI model. The specific configuration and operation of the system are described in detail below.

[0868] System configuration and operation

[0869] This system mainly provides user information input, plan generation, in-store navigation, and a recommendation system. The role and operation of each component are explained in detail below.

[0870] 1. Enter and save user information

[0871] Users access the app using a smartphone or another device and first enter their basic physical information (age, gender, height, weight) and diet goals (target weight and target period). The entered information is sent from the device to a server and stored in a database.

[0872] 2. Plan generation and provision

[0873] The server uses a generative AI model to generate optimal meal and exercise plans based on the user's physical information and diet goals. This generation process is customized based on the needs of each individual user. The generated meal and exercise plans are then sent to the device and provided to the user.

[0874] 3. In-store navigation

[0875] When users visit a physical store, they can use their smartphone or smart glasses to navigate to the location of the ingredients or products they want within the store, including by tracking their current location in real time and displaying the optimal route.

[0876] 4. Food and product recommendations

[0877] The generative AI model also has the ability to recommend the best ingredients and products in stores based on the user's diet goals, making it easy for users to find the products that are best suited to their diet.

[0878] 5. Recording and analyzing diet and exercise

[0879] Users record their daily diet and exercise routines on their devices. These records are sent to a server and stored in a database. The server periodically analyzes the stored records to assess the user's progress. If necessary, the generative AI model is used again to update the diet and exercise plans.

[0880] 6. Providing information on seasonal ingredients

[0881] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[0882] Specific examples of hardware and software used

[0883] Hardware: Smartphones, smart glasses, servers

[0884] Software: Django (server-side framework), React Native (front-end framework), Hugging Face Transformers (generative AI model)

[0885] Examples:

[0886] For example, when a user wears smart glasses and enters a brick-and-mortar store, they will see information such as "Sweet potatoes are low in calories and in season now" and a suggested recipe for "Sweet Potato Soup."

[0887] "Please enter your user information (age, gender, height, weight) and diet goal (target weight, period)"

[0888] "We will guide you to the best products for your diet based on your current location."

[0889] "Why not try my recommended recipe, sweet potato soup?"

[0890] As described above, the present invention is a system that supports users in effectively carrying out diet plans in physical stores, and is expected to increase user satisfaction and results.

[0891] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0892] Step 1:

[0893] A user input physical information and goals are received.

[0894] Input: Information such as age, gender, height, weight, target weight, and target period entered by the user via a device (such as a smartphone).

[0895] Data processing: The terminal converts the input data into an appropriate format for transmission to the server.

[0896] Output: User information is sent to the server and stored.

[0897] Example: A user inputs "30 years old, male, height 180cm, weight 85kg, target weight 75kg, target period 6 months."

[0898] Step 2:

[0899] The server generates a plan using a generative AI model.

[0900] Input: User's physical information and diet goals stored on the server.

[0901] Data calculation: The generative AI model calculates basal metabolic rate and calorie intake based on user information, and generates optimal meal and exercise plans.

[0902] Output: Meal and exercise plans are generated and stored on the server.

[0903] Example: Calculate the user's basal metabolic rate, calculate the amount of calories needed per day, and then suggest menu items such as yogurt for breakfast, salad for lunch, and fish dishes for dinner.

[0904] Step 3:

[0905] The server provides the generated plan to the user.

[0906] Input: Generated meal and exercise plans.

[0907] Data processing: Format the plan into an easy-to-read format and send it to the device.

[0908] Output: The meal and exercise plan is displayed on the user's device.

[0909] Example: A user's smartphone screen displays "Today's menu: Breakfast = yogurt, lunch = salad, dinner = fish dish."

[0910] Step 4:

[0911] A user visits a physical store and begins navigation.

[0912] Input: User's current location (e.g. GPS) and a map of the store.

[0913] Data calculation: Based on the user's current location, the device calculates the shortest route to the desired ingredients or products and displays the route on a map of the store.

[0914] Output: The location and route of the desired ingredients or products are displayed on the user's device.

[0915] Example: The smart glasses display will show navigation such as "Sweet potato section → go straight ahead 20m on the right."

[0916] Step 5:

[0917] The server recommends ingredients and products based on the user's diet goals.

[0918] Input: User's physical information, diet goals, and current location.

[0919] Data computation: Generative AI models recommend ingredients and products that best fit a user's goals and incorporate this information into navigation.

[0920] Output: Recommended product information is displayed on the user's device.

[0921] Example: Smart glasses display the message, "This product is low in calories and perfect for dieting."

[0922] Step 6:

[0923] The user enters a diet and exercise log.

[0924] Input: Daily diet and exercise information entered via the device.

[0925] Data processing: The terminal converts the input data into an appropriate format for transmission to the server.

[0926] Output: Food and exercise records are stored on a server.

[0927] Example: A user inputs "Breakfast: yogurt 200kcal, exercise: 30 minutes of jogging."

[0928] Step 7:

[0929] The server analyzes the records and updates the plan.

[0930] Input: Your saved daily food and exercise logs.

[0931] Data calculation: The server analyzes the user's progress based on the stored records and generates a new meal and exercise plan, again using the generative AI model.

[0932] Output: A new updated meal and exercise plan is generated and sent to the user device.

[0933] Example: If the user is making good progress towards their goal, a new exercise plan is suggested: "Increase jogging distance."

[0934] Step 8:

[0935] The server reflects seasonal food information.

[0936] Input: Seasonal food information from external sources.

[0937] Data processing: The server applies this information to the generative AI model to generate an optimized meal plan.

[0938] Output: A meal plan using seasonal ingredients is sent to the user's device.

[0939] Example: In the fall, "sweet potato soup" and "stir-fried mushrooms" are suggested.

[0940] The specific operation has been explained above based on each processing step, and the overall picture of the system that allows users to receive effective diet support at a physical store has been detailed.

[0941] 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.

[0942] The present invention is a system that effectively supports dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals, and by combining this with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0943] System configuration and operation

[0944] This system consists of a user, a terminal, a server, a generative AI model, and an emotion engine. The role and operation of each component are explained below.

[0945] 1. Enter your user information

[0946] The user accesses the app using a device and first enters their basic physical information (age, gender, height, weight, etc.) and diet goals (e.g., target weight and target period). The entered information is then sent from the device to the server.

[0947] 2. Save information and generate plans

[0948] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal meal and exercise plan. This plan generation process is customized based on the user's individual needs.

[0949] Examples:

[0950] For example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg,

[0951] The generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, then suggests optimal meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise regimens (30 minutes of jogging three times a week).

[0952] 3. Introducing the Emotion Engine

[0953] The emotion engine recognizes emotions from user input and sensor data and stores them in a database, allowing the system to understand the user's emotional state in real time and use it to adjust plans.

[0954] Examples:

[0955] If a user's motivation drops while following a plan, the emotion engine will recognize the change in emotion and adjust the diet and exercise plan. The emotion engine also analyzes the user's past emotion history and provides specific advice to increase the user's motivation.

[0956] 4. Presenting the plan

[0957] The meal plan and exercise plan generated by the server are sent to the terminal, which displays these plans in a user-friendly format, and the user follows the proposed plan to follow their daily diet and exercise routine.

[0958] 5. Food and exercise tracking

[0959] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[0960] Examples:

[0961] For example, if a user has yogurt and fruit for breakfast, the details of the meal (yogurt, fruit) and calorie information are entered into the terminal and sent to the server. Similarly, if a user goes jogging, the amount of exercise is recorded.

[0962] 6. Analyze records and adjust plans

[0963] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[0964] 7. Providing information on seasonal ingredients

[0965] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[0966] Examples:

[0967] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (for example, sweet potato soup and stir-fried mushrooms) are suggested.

[0968] Linking with Healthcare App

[0969] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. This allows for more accurate and detailed data to be used to customize the plan. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[0970] Maintaining motivation with an emotional engine

[0971] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice based on the emotion. For example, if the user feels stressed or anxious while following a plan, the emotion engine reports that information to the server, which then adjusts parts of the plan. The emotion engine also predicts the user's reaction in certain situations based on past emotional history and provides preventative advice.

[0972] Examples:

[0973] If a user tends to feel stressed during a particular training session, the emotion engine can use that information to provide advice on how to relax beforehand or change the exercise plan to a different format.

[0974] This invention allows users to implement individually customized meal plans and exercise plans, and provides support that responds to emotional changes. This allows for effective dieting and health management, and can continuously increase the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, and the user can continue without getting bored.

[0975] The processing flow will be explained below.

[0976] Specific steps of the program's processing (including the emotion engine)

[0977] 1. Enter and save user information

[0978] Step 1:

[0979] The user uses the device to input their physical information (age, sex, height, weight) and diet goals (target weight, target period) into the app.

[0980] Step 2:

[0981] The terminal checks the entered information and converts it into JSON format.

[0982] Step 3:

[0983] The terminal sends the converted JSON data to the server.

[0984] Step 4:

[0985] The server parses the received JSON data and stores it in a database.

[0986] 2. Generate meal and exercise plans

[0987] Step 5:

[0988] The server retrieves the user's physical information and goals from a database.

[0989] Step 6:

[0990] The server inputs the acquired data into a generative AI model to generate optimal meal and exercise plans for the user.

[0991] Step 7:

[0992] The server converts the generated plan into JSON format and sends it to the terminal.

[0993] Step 8:

[0994] The terminal displays the received plan data in a format that is easy for the user to view.

[0995] 3. Introducing the Emotion Engine

[0996] Step 9:

[0997] The user inputs their daily emotional state into the terminal, which then transmits this information to the emotion engine.

[0998] Step 10:

[0999] The device collects data from built-in or external sensors (e.g., heart rate and facial expression recognition data) and sends it to the emotion engine.

[1000] Step 11:

[1001] The emotion engine analyzes the received data and recognizes the user's emotional state.

[1002] Step 12:

[1003] The emotion engine sends the analysis results to the server.

[1004] 4. Presenting the plan

[1005] Step 13:

[1006] The meal plan and exercise plan generated by the server are sent to the terminal.

[1007] Step 14:

[1008] The terminal displays these plans in a user-friendly format.

[1009] Step 15:

[1010] The user follows the suggested diet and exercise plan daily.

[1011] 5. Food and exercise tracking

[1012] Step 16:

[1013] The user inputs the details of their daily meals and the amount of exercise they do into the recording screen of the terminal.

[1014] Step 17:

[1015] The terminal checks the entered records and converts them into JSON format.

[1016] Step 18:

[1017] The terminal sends the converted JSON data to the server.

[1018] Step 19:

[1019] The server parses the received recording data and stores it in a database.

[1020] 6. Analyze records and adjust plans

[1021] Step 20:

[1022] The server periodically retrieves the user's diet and exercise record data from the database.

[1023] Step 21:

[1024] The server integrates the acquired data with the emotion engine data, analyzes it, and evaluates the user's progress.

[1025] Step 22:

[1026] The server uses the generative AI model to update the meal and exercise plans as needed.

[1027] Step 23:

[1028] The server converts the updated plan into JSON format and sends it to the device.

[1029] Step 24:

[1030] The terminal notifies the user of the updated plan.

[1031] 7. Providing information on seasonal ingredients

[1032] Step 25:

[1033] The server collects seasonal food information from external information sources.

[1034] Step 26:

[1035] The server stores the collected information on seasonal ingredients in a database.

[1036] Step 27:

[1037] The server uses the stored seasonal ingredient information to reflect seasonal ingredients in meal plans based on a generative AI model.

[1038] Step 28:

[1039] The server generates recipes using seasonal ingredients and sends them to the terminal.

[1040] Step 29:

[1041] The terminal proposes the generated recipe to the user.

[1042] Linking with Healthcare App

[1043] Step 30:

[1044] The server receives weight and exercise data from the user's healthcare app.

[1045] Step 31:

[1046] The server stores the received data in a database.

[1047] Step 32:

[1048] The server applies the additional stored data to the generative AI model to generate a highly accurate plan.

[1049] Maintaining motivation with an emotional engine

[1050] Step 33:

[1051] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice according to their emotions.

[1052] Step 34:

[1053] Based on past emotional history, the server predicts the user's reaction in specific situations and provides preventative advice.

[1054] The above process allows users to implement individually customized meal and exercise plans. It also provides support that responds to emotional changes, enabling effective dieting and health management, and sustainably increasing the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[1055] Example 2

[1056] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1057] Conventional diet support systems have difficulty providing effective support due to their difficulty in highly customizing diet plans based on the user's individual physical information and emotional state. Furthermore, the user's information is fixed, making it difficult to provide flexible plans that respond to changing emotions and seasonal changes. The present invention aims to solve these problems by providing optimal plans that respond to the user's individual needs and emotional state, thereby improving the effectiveness of dieting.

[1058] 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 means for receiving physical information and goals input by the user, means for using a generative model to generate a meal plan and an exercise plan based on the physical information and goals, means for providing the generated meal plan and exercise plan to the user, means for the user to input a meal and exercise record, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for recognizing the user's emotional state and adjusting the plan based on this, and means for collecting seasonal food information and reflecting this in the meal plan. This enables effective diet support while responding to the user's individual physical information and emotional state.

[1059] "User" refers to an individual who uses the system to input physical information and diet goals and receives meal and exercise plans.

[1060] "Physical information" refers to basic physical information about the user, such as age, gender, height, and weight.

[1061] "Goal" refers to a specific goal for dieting that the user wants to achieve, such as a target weight or a target period.

[1062] "Meal Plan" refers to a specific daily meal plan suggested based on a user's physical information and goals.

[1063] An "exercise plan" refers to a plan that specifically outlines the exercise content for one day, proposed based on the user's physical information and goals.

[1064] "Generative model" refers to an AI model that automatically generates optimal meal and exercise plans based on a user's physical information and goals.

[1065] "Saving" refers to the act of recording and storing input data in a storage device such as a database.

[1066] "Analysis" refers to analyzing stored data and assessing a user's progress and trends.

[1067] "Emotional state" refers to various emotional states that users experience in their daily lives, and specifically includes motivation, stress, happiness, etc.

[1068] "Seasonal food information" refers to information about the freshest and most nutritious food items on the market each season.

[1069] A "healthcare app" refers to application software for managing data related to a user's body and exercise.

[1070] "Adjusting your plan" refers to the process of reviewing and optimizing your existing meal and exercise plans based on your progress and emotional state.

[1071] This invention is a system that effectively supports dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals, and by combining this with an emotion engine that recognizes the user's emotions. This system is composed of a user, a device, a server, a generative AI model, and an emotion engine. The role and operation of each component are explained below.

[1072] Entering user information

[1073] Users access the app using a device such as a smartphone or tablet. They enter basic physical information such as age, gender, height, and weight, as well as their diet goals (e.g., target weight and target period). The entered information is sent from the device to the server.

[1074] Save information and generate plans

[1075] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal diet and exercise plan. This generative AI model uses, for example, OpenAI's GPT model. The generation process is customized based on the user's individual needs.

[1076] Examples:

[1077] For example, if a user is a 35-year-old male with a height of 175 cm and a weight of 80 kg, with a target weight of 70 kg, the generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, and then suggests specific meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise options (e.g., 30 minutes of jogging three times a week).

[1078] Example prompts for input to a generative AI model:

[1079] User Information:

[1080] Age: 35

[1081] Gender: Male

[1082] Height: 175cm

[1083] Weight: 80kg

[1084] Target weight: 70kg

[1085] Prompts for input to generative AI models:

[1086] Consider your basal metabolic rate and activity level and generate optimal meal and exercise plans based on the following information:

[1087] Age: 35

[1088] Gender: Male

[1089] Height: 175cm

[1090] Weight: 80kg

[1091] Target weight: 70kg

[1092] Introducing the Emotion Engine

[1093] The emotion engine recognizes emotions from user input data and sensor data (e.g., smartwatches and fitness trackers) and stores the data in a database to understand the user's emotional state in real time. The emotion engine uses, for example, Affectiva's emotion recognition engine. The server adjusts the plan based on this.

[1094] Examples:

[1095] If a user's motivation drops while following a plan, the emotion engine will recognize the change and adjust their diet or exercise plan, or provide advice to boost their motivation.

[1096] Presenting the plan

[1097] The meal plan and exercise plan generated by the server are sent to the terminal, which displays the plan to the user in a visually easy-to-understand format. The user then follows the proposed plan to follow their daily diet and exercise routine.

[1098] Food and exercise tracking

[1099] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[1100] Examples:

[1101] If a user has yogurt and fruit for breakfast, the device will input the meal details (yogurt, fruit) and calorie information and send it to the server. Similarly, if a user goes jogging, the amount of exercise will be recorded.

[1102] Analyze records and adjust plans

[1103] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[1104] Providing information on seasonal ingredients

[1105] The server collects information on seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[1106] Examples:

[1107] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (e.g., sweet potato soup and stir-fried mushrooms) are suggested.

[1108] Linking with Healthcare App

[1109] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[1110] Maintaining motivation with an emotional engine

[1111] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice according to their emotions. It predicts the user's emotional changes in specific situations and recommends measures to increase motivation in advance.

[1112] Examples:

[1113] If a user is prone to feeling stressed during a particular training session, the emotion engine can predict this in advance and provide advice on how to relax or change the exercise plan.

[1114] This invention allows users to implement individually customized meal and exercise plans and provides support that responds to emotional changes. This also allows for effective dieting and health management, and can continuously increase the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, and the user can continue without getting bored.

[1115] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1116] Step 1:

[1117] The user accesses the app using a device and enters their physical information (e.g., age, gender, height, weight) and diet goals (e.g., target weight and target period). The information entered is then entered into a form and sent to the server by pressing the submit button. User profile data is generated based on the input and passed to the server.

[1118] Specific input example:

[1119] User input information:

[1120] Age: 30

[1121] Gender: Female

[1122] Height: 165cm

[1123] Weight: 70kg

[1124] Target weight: 60kg

[1125] Server input:

[1126] User profile data (age, gender, height, weight, goal weight)

[1127] Server output:

[1128] Profile save confirmation message

[1129] Step 2:

[1130] The server stores the received user information in a database, which is then input into a generative AI model to generate meal and exercise plans.

[1131] Specific input example:

[1132] Server input:

[1133] User profile data (age, gender, height, weight, goal weight)

[1134] Server output:

[1135] Save to database

[1136] Specific behavior:

[1137] The server connects to the database and executes a SQL query to save the user information. After the save is complete, a save success message is generated.

[1138] Step 3:

[1139] The server generates prompt sentences based on the stored user information and sends the prompt sentences to the generative AI model, which then generates optimal meal and exercise plans for the user based on the prompt sentences.

[1140] Specific input example:

[1141] Prompt for the generative AI model:

[1142] User Information:

[1143] Age: 30

[1144] Gender: Female

[1145] Height: 165cm

[1146] Weight: 70kg

[1147] Target weight: 60kg

[1148] Prompts for input to generative AI models:

[1149] Consider your basal metabolic rate and activity level and generate optimal meal and exercise plans based on the following information:

[1150] Age: 30

[1151] Gender: Female

[1152] Height: 165cm

[1153] Weight: 70kg

[1154] Target weight: 60kg

[1155] Server input:

[1156] Prompt statement

[1157] Output of the generative AI model:

[1158] Meal and exercise plans (e.g., yogurt and fruit for breakfast, salad and chicken for lunch, fish and stir-fried vegetables for dinner)

[1159] Specific behavior:

[1160] The server sends the generated prompt text to the generative AI model, which then performs calculations and data analysis to generate and return a meal plan and exercise plan.

[1161] Step 4:

[1162] The server sends the meal and exercise plans derived from the generative AI model to the device, which displays these plans in a user-friendly format.

[1163] Specific input example:

[1164] Server input:

[1165] Meal and exercise plans

[1166] Server output:

[1167] Sending plan data to the device

[1168] Type in the terminal:

[1169] Plan data received on your device

[1170] Terminal output:

[1171] User interface (displaying meal and exercise plans)

[1172] Specific behavior:

[1173] The server transmits the plan data to the terminal, which receives it and displays it on the user interface.

[1174] Step 5:

[1175] Users record their daily diet and exercise on their devices, and the recorded data is sent to a server.

[1176] Specific input example:

[1177] User input:

[1178] Food log (e.g., yogurt and fruit for breakfast)

[1179] Exercise record (e.g. 30 minutes of jogging)

[1180] Type in the terminal:

[1181] Food and exercise record data

[1182] Terminal output:

[1183] Sending recorded data to the server

[1184] Server input:

[1185] Food and exercise record data

[1186] Specific behavior:

[1187] The user enters their diet and exercise record data into the app and presses the save button, sending the data to the server.

[1188] Step 6:

[1189] The server stores the received food and exercise records in a database, and then analyzes this data to assess the user's progress.

[1190] Specific input example:

[1191] Server input:

[1192] Food and exercise record data

[1193] Server output:

[1194] Data saving operations

[1195] Progress Assessment Report

[1196] Specific behavior:

[1197] The server stores the food and exercise records in a database and analyzes them with an analytical algorithm to assess progress.

[1198] Step 7:

[1199] Based on the progress assessment results, the server re-uses the generative AI model to update the meal and exercise plans if necessary, and the new plans are sent to the device and notified to the user.

[1200] Specific input example:

[1201] Server input:

[1202] Progress evaluation results

[1203] Server output:

[1204] Updated meal and exercise plans

[1205] Type in the terminal:

[1206] New plan data

[1207] Terminal output:

[1208] View and notify renewal plans

[1209] Specific behavior:

[1210] The server uses the progress data to generate a new plan using the generative AI model and sends the result back to the device, which updates its user interface to display the new plan.

[1211] Step 8:

[1212] The server collects information on seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[1213] Specific input example:

[1214] Server input:

[1215] Seasonal food data from external sources

[1216] Server output:

[1217] Updated Recipe Plans

[1218] Type in the terminal:

[1219] New Recipe Plan

[1220] Terminal output:

[1221] Displaying recipes using seasonal ingredients

[1222] Specific behavior:

[1223] The server retrieves seasonal food data from an external API, applies it to the generative AI model, and generates a new recipe plan. The plan is then sent to the device and displayed to the user.

[1224] Step 9:

[1225] The server periodically obtains weight and exercise data from the user's healthcare app, which is then stored in a database and reflected in the generative AI model.

[1226] Specific input example:

[1227] Server input:

[1228] Weight and exercise data from the Health app

[1229] Server output:

[1230] Updated database

[1231] Specific behavior:

[1232] The server retrieves user data from the healthcare app's API and stores it in a database.

[1233] Step 10:

[1234] The emotion engine recognizes the user's emotional state in real time based on sensor data and input data, and sends the results to the server, which then generates feedback and advice based on that information and provides it to the user.

[1235] Specific input example:

[1236] Emotion Engine Input:

[1237] Sensor data and user emotion input data

[1238] Emotion engine output:

[1239] Emotion analysis results

[1240] Server input:

[1241] Emotion analysis results

[1242] Server output:

[1243] Feedback and Advice

[1244] Specific behavior:

[1245] The emotion engine analyzes the user's emotions, and the server generates feedback and advice based on that information and sends it to the device.

[1246] (Application example 2)

[1247] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1248] In today's busy lifestyles, effective support for personal health management and dieting requires providing plans customized to each user's physical information and emotional state. However, conventional systems have difficulty adjusting meal and exercise plans to accommodate emotional changes, and they lack the information to provide specific ingredients for quickly implementing the generated plans. This creates challenges for maintaining user motivation and sticking to the plan.

[1249] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving physical information and goals input by the user, means for generating a meal plan and an exercise plan based on the physical information and goals, and means for adjusting the meal plan and the exercise plan based on emotional data. This makes it possible to provide a customized plan tailored to each user's individual physical information and emotional state. In addition, by providing means for delivering ingredients based on the generated meal plan, the user can receive support in quickly implementing the plan, which is expected to maintain motivation and promote health management.

[1250] The "means for receiving user-entered physical information and goals" refers to an interface through which a user inputs their basic physical data (age, gender, height, weight, etc.) and the health goals they wish to achieve (e.g., target weight and target period), and the system receives this information.

[1251] The "means for generating a meal plan and exercise plan based on the physical information and goals" refers to an algorithm or software for generating an optimal meal and exercise plan based on the user's input physical information and health goals.

[1252] The "means for providing the generated meal plan and exercise plan to the user" refers to a device or application for presenting the meal plan and exercise plan generated by the system to the user in the form of visual or text information.

[1253] "Means for recognizing user emotions and acquiring emotional data" refers to sensors and analytical software that monitor user emotions in real time and collect that data.

[1254] The "means for adjusting meal plans and exercise plans based on said emotional data" is an algorithm or generative model for optimizing or modifying existing meal plans and exercise plans based on collected emotional data.

[1255] The "means for the user to input records of meals and exercise" refers to an interface that allows the user to input the details of daily meals and the amount of exercise into an application or device.

[1256] The "means for storing and analyzing the entered diet and exercise records" refers to software or hardware for storing the diet and exercise records entered by the user in a database for later analysis.

[1257] The "means for updating the diet and exercise plans based on the analysis results and emotional data" refers to an algorithm for analyzing the stored data and emotional data and updating the diet and exercise plans based thereon.

[1258] The "means for collecting seasonal food information and reflecting it in the meal plan" is a data acquisition system for externally collecting seasonal food information and reflecting it in the meal plan that is generated.

[1259] The "means for delivering ingredients based on the generated meal plan" is a delivery system for quickly delivering ingredients required for the generated meal plan to the user.

[1260] MODE FOR CARRYING OUT THE INVENTION

[1261] This invention is a system that proposes optimal meal and exercise plans based on a user's individual physical information, diet goals, and emotional state, and even delivers ingredients to help them carry out the plans. The main components of this system include a server, a terminal, a generative AI model, and an emotion engine.

[1262] 1. Enter and save user information

[1263] Device:

[1264] Users enter their physical information (age, sex, height, weight, etc.) and goals (e.g., target weight and target period) through an application on their smartphone or tablet.

[1265] server:

[1266] It receives information sent from the device and stores it in a database, which creates a base for various data that will be needed later.

[1267] 2. Creating a menu

[1268] server:

[1269] Based on the saved user information, a generative AI model is used to generate meal and exercise plans. The generated plans are customized based on the user's basal metabolic rate and activity level. For example, for a 35-year-old man who is 175 cm tall, weighs 80 kg, and has a target weight of 70 kg, the generative AI model calculates the user's basal metabolic rate and determines the required calorie intake. It then suggests appropriate meal plans (yogurt and fruit for breakfast, salad and chicken breast for lunch, and stir-fried vegetables and fish for dinner) and exercise options (30 minutes of jogging three times a week).

[1270] 3. Introducing the Emotion Engine

[1271] Device:

[1272] It uses the smartphone's camera, microphone, and other sensors to recognize and monitor the user's emotions in real time and obtain emotional data.

[1273] server:

[1274] Using the captured emotion data, the generative AI model can readjust meal and exercise plans as needed, for example, by changing the meal plan to one that promotes relaxation if it detects that the user is feeling stressed while following the plan.

[1275] 4. Plan presentation and ingredients provided

[1276] Device:

[1277] The optimal meal plan and exercise plan sent from the server are presented to the user.

[1278] server:

[1279] The system arranges for the delivery of the necessary ingredients based on the generated meal plan, and provides the appropriate ingredients to the user through a delivery service.

[1280] 5. Food and exercise tracking

[1281] Device:

[1282] The user inputs the details of their daily diet and the amount of exercise they do. For example, if they eat yogurt and fruit for breakfast, the details and calorie information are entered. Similarly, the amount of exercise they do is also recorded.

[1283] server:

[1284] Receives the entered data and stores it in the database.

[1285] 6. Analyze records and adjust plans

[1286] server:

[1287] The stored data is periodically analyzed to assess the user's progress, and if necessary, the generative AI model is used to regenerate meal and exercise plans and present updated plans to the user.

[1288] 7. Providing information on seasonal ingredients

[1289] server:

[1290] This system collects seasonal food information from external sources and incorporates it into the generative AI model. This makes it possible to provide users with menus that utilize seasonal ingredients. For example, in autumn, it suggests recipes using sweet potatoes and mushrooms (sweet potato soup and stir-fried mushrooms).

[1291] Examples and prompts

[1292] For example, if a user feels stressed while working towards their weight goal, the smartphone camera can analyze the user's facial expressions to detect the stress.The emotion engine then adjusts the meal plan to include foods with a high relaxation effect (such as low-fat fish or green tea), and delivers these ingredients to the user via a delivery service.

[1293] Example prompt sentence:

[1294] "We have found that a 35-year-old male user is following a diet plan, but is feeling stressed. Regenerate the optimal meal plan for this situation and suggest meal menus that will have a relaxing effect. Output the action to deliver the suggested meal menu to the user."

[1295] The invention includes specific software (such as generative AI models and emotion engines) used for all data processing and analysis, and also integrates an efficient delivery system to facilitate the execution of the meal plan.

[1296] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1297] Step 1:

[1298] Entering user information

[1299] Device: The user enters their physical information (age, gender, height, weight) and diet goals (target weight, target period) through a dedicated mobile application.

[1300] Input: Physical information and goals manually entered by the user.

[1301] Output: The input information is sent to the server in real time.

[1302] Step 2:

[1303] Retention of Information

[1304] Server: Stores the received user information in a database. This step lays the foundation for centrally managing all input data.

[1305] Input: User-entered physical information and goals.

[1306] Output: Saved user information data.

[1307] Step 3:

[1308] Generate a plan

[1309] Server: Uses a generative AI model to generate optimal meal and exercise plans based on the user's individual physical information and goals. The generation process calculates basal metabolic rate and activity level to determine the required calorie intake.

[1310] Input: Saved user information data.

[1311] Output: Optimal diet and exercise plan.

[1312] Step 4:

[1313] Presenting the plan

[1314] Device: The meal plan and exercise plan sent from the server are presented to the user on the device. The user can visually check the proposed plan through the application.

[1315] Input: Meal and exercise plan from server.

[1316] Output: Show plan.

[1317] Step 5:

[1318] Emotion recognition

[1319] On-device: The smartphone's camera, microphone, and sensors are used to capture the user's emotional data in real time. The emotion engine analyzes this data to identify the user's emotional state.

[1320] Input: Data required for emotion recognition, such as camera footage and audio data.

[1321] Output: The user's emotional state.

[1322] Step 6:

[1323] Adjusting the plan

[1324] Server: Based on the acquired emotional data, the system adjusts the diet and exercise plans as needed. The generative AI model analyzes the emotional data and makes adjustments to reduce stress and increase motivation.

[1325] Input: Emotional data and existing diet and exercise plans.

[1326] Output: A tailored diet and exercise plan.

[1327] Step 7:

[1328] Food delivery

[1329] Server: Collects the necessary ingredients based on the adjusted and generated meal plan and delivers them to the user via a delivery service. This uses the delivery service's API.

[1330] Enter: your tailored meal plan.

[1331] Output: Grocery delivery to the user's home.

[1332] Step 8:

[1333] Food and exercise tracking

[1334] Device: The user enters their daily diet and exercise information into the app.

[1335] Input: Food and exercise information manually entered by the user.

[1336] Output: The entered recorded data is sent to the server and saved.

[1337] Step 9:

[1338] Analyze records and readjust plans

[1339] Server: Periodically analyzes the user's stored food and exercise logs to assess the user's progress and, if necessary, regenerates the meal and exercise plans using generative AI models.

[1340] Input: Stored recorded data and analysis results.

[1341] Output: An updated diet and exercise plan.

[1342] Step 10:

[1343] Reflecting seasonal food information

[1344] Server: Collects seasonal ingredients from external sources and incorporates them into meal plans. This process generates recipes that are easy to use and prioritize health.

[1345] Input: Seasonal food data collected from external sources.

[1346] Output: Meal plans using seasonal ingredients.

[1347] In this way, the system provides a customized plan according to the user's physical information and emotional state and is equipped with a series of technical means to support the execution of the plan.

[1348] 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.

[1349] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1350] 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.

[1351] [Third embodiment]

[1352] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1353] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1354] 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).

[1355] 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.

[1356] 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.

[1357] 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).

[1358] 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.

[1359] 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.

[1360] 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.

[1361] 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.

[1362] 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.

[1363] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1364] The present invention is a system for supporting dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals. Specific embodiments of this system are described below.

[1365] System configuration and operation

[1366] This system consists of a user, a terminal, a server, and a generative AI model. The role and operation of each component are explained below.

[1367] 1. Enter your user information

[1368] The user accesses the app using a device and first enters their basic physical information (age, gender, height, weight, etc.) and diet goals (e.g., target weight and target period). The entered information is then sent from the device to the server.

[1369] 2. Save information and generate plans

[1370] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal meal and exercise plan. This plan generation process is customized based on the user's individual needs.

[1371] Examples:

[1372] For example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg,

[1373] The generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, then suggests optimal meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise regimens (30 minutes of jogging three times a week).

[1374] 3. Presenting the plan

[1375] The meal plan and exercise plan generated by the server are sent to the terminal, which displays these plans in a user-friendly format, and the user follows the proposed plan to follow their daily diet and exercise routine.

[1376] 4. Food and exercise tracking

[1377] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[1378] Examples:

[1379] For example, if a user has yogurt and fruit for breakfast, the details of the meal (yogurt, fruit) and calorie information are entered into the terminal and sent to the server. Similarly, if a user goes jogging, the amount of exercise is recorded.

[1380] 5. Analyze records and adjust plans

[1381] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[1382] 6. Providing information on seasonal ingredients

[1383] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[1384] Examples:

[1385] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (for example, sweet potato soup and stir-fried mushrooms) are suggested.

[1386] Linking with Healthcare App

[1387] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. This allows for more accurate and detailed data to be used to customize the plan. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[1388] This invention allows users to follow individually customized meal and exercise plans, enabling them to achieve an effective diet. Regular progress assessments and plan adjustments help users maintain their motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[1389] The processing flow will be explained below.

[1390] Specific steps of the program's processing

[1391] 1. Enter and save user information

[1392] Step 1:

[1393] The user uses the device to input their physical information (age, sex, height, weight) and diet goals (target weight, target period) into the app.

[1394] Step 2:

[1395] The terminal checks the entered information and converts it into JSON format.

[1396] Step 3:

[1397] The terminal sends the converted JSON data to the server.

[1398] Step 4:

[1399] The server parses the received JSON data and stores it in a database.

[1400] 2. Generate meal and exercise plans

[1401] Step 5:

[1402] The server retrieves the user's physical information and goals from a database.

[1403] Step 6:

[1404] The server inputs the acquired data into a generative AI model to generate optimal meal and exercise plans for the user.

[1405] Step 7:

[1406] The server converts the generated plan into JSON format and sends it to the terminal.

[1407] Step 8:

[1408] The terminal displays the received plan data in a format that is easy for the user to view.

[1409] 3. Daily diet and exercise records

[1410] Step 9:

[1411] The user inputs the details of their daily meals and the amount of exercise they do into the recording screen of the terminal.

[1412] Step 10:

[1413] The terminal checks the entered records and converts them into JSON format.

[1414] Step 11:

[1415] The terminal sends the converted JSON data to the server.

[1416] Step 12:

[1417] The server parses the received recording data and stores it in a database.

[1418] 4. Regularly analyze and adjust your plan

[1419] Step 13:

[1420] The server periodically retrieves the user's diet and exercise record data from the database.

[1421] Step 14:

[1422] The server analyzes the acquired data and evaluates the user's progress.

[1423] Step 15:

[1424] The server again uses the generative AI model to update the meal and exercise plans as needed.

[1425] Step 16:

[1426] The server converts the updated plan into JSON format and sends it to the device.

[1427] Step 17:

[1428] The terminal notifies the user of the updated plan.

[1429] 5. Providing information on seasonal ingredients

[1430] Step 18:

[1431] The server collects seasonal food information from external information sources.

[1432] Step 19:

[1433] The server stores the collected information on seasonal ingredients in a database.

[1434] Step 20:

[1435] The server uses the stored seasonal ingredient information to reflect seasonal ingredients in meal plans based on a generative AI model.

[1436] Step 21:

[1437] The server generates recipes using seasonal ingredients and sends them to the terminal.

[1438] Step 22:

[1439] The terminal proposes the generated recipe to the user.

[1440] Linking with Healthcare App

[1441] Step 23:

[1442] The server receives weight and exercise data from the user's healthcare app.

[1443] Step 24:

[1444] The server stores the received data in a database.

[1445] Step 25:

[1446] The server applies the additional stored data to the generative AI model to generate a highly accurate plan.

[1447] Through the above process, users can implement individually customized meal and exercise plans, and regular progress assessments and plan updates are provided to support effective dieting.

[1448] Example 1

[1449] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1450] Conventional diet support systems often provide uniform meal and exercise plans, which lack sufficient customization based on the user's individual physical information and diet goals. Furthermore, because they do not utilize seasonal ingredients or the latest data from external sources, it is difficult to provide a plan that users can continue without getting bored. Furthermore, they lack appropriate feedback based on the user's detailed activity data, such as data obtained from linked health management software.

[1451] 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.

[1452] In this invention, the server includes means for receiving physical information and goals input by a user, means for using a generative model to generate a meal plan and an exercise plan based on the physical information and goals, means for the user to input a meal and exercise record, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for collecting seasonal food information and reflecting it in the meal plan, and means for updating the generative model using data obtained from an external information source. This makes it possible to provide customized plans according to the individual needs of users, create menus that never get boring using seasonal ingredients, and provide precise feedback based on detailed data.

[1453] "Physical information" refers to basic physical data such as the user's age, sex, height, weight, etc.

[1454] The "goal" is a specific target value for dieting set by the user, such as a target weight or a target period.

[1455] A "generative model" is an algorithm or program that uses artificial intelligence to generate optimal meal and exercise plans based on a user's physical information and goals.

[1456] A "meal plan" is a meal plan or menu suggested to suit a user's individual physical information and goals.

[1457] An "exercise plan" is a suggested exercise schedule or exercises based on the user's individual physical information and goals.

[1458] "Records" are information entered by the user regarding the details of daily meals and the amount of exercise.

[1459] "Analysis" is the process of evaluating a user's progress based on the stored records and updating their meal and exercise plans as needed.

[1460] "Seasonal food information" is information about fresh ingredients that are considered to be optimal for each season.

[1461] "External information sources" refers to any data source obtained from outside the system, including food databases and health management software, for example.

[1462] "Health management software" is an application that manages health data such as a user's weight and exercise.

[1463] This invention is a system that supports dieting by proposing optimal meal and exercise plans based on the user's individual physical information and diet goals. This system is mainly composed of a user, a terminal, a server, and a generative AI model.

[1464] System configuration and operation

[1465] 1. Enter your user information

[1466] Users access the diet support app using a device such as a smartphone or PC and enter their basic physical information (age, gender, height, weight, etc.) and diet goals (target weight and target period). This information is sent from the device to the server.

[1467] As a specific example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg, the input information is sent to the server and stored in the database.

[1468] Example prompt sentence:

[1469] "Write a program that inputs a user's physical information and diet goals and suggests optimal meal and exercise plans. The user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg. Calculate the user's basal metabolic rate and activity level, and suggest specific meal and exercise menus."

[1470] 2. Save information and generate plans

[1471] The server stores the received user's physical information and goals in a database and generates an optimal meal and exercise plan using a generative AI model, which creates a customized plan based on the user's individual physical information and goals.

[1472] For example, the generative AI model calculates the user's basal metabolic rate and activity level, and then suggests specific meal menus and exercise plans based on that. A specific meal menu example might be yogurt and fruit for breakfast, salad and chicken breast for lunch, and stir-fried vegetables and fish for dinner. The exercise suggestion is 30 minutes of jogging three times a week.

[1473] 3. Presenting the plan

[1474] The generated meal and exercise plans are sent from the server to the device. The device parses the received data and displays it on the user's UI. The meal and exercise plans can be displayed in a list format by day or week, allowing the user to follow the suggested plans for daily diet and exercise.

[1475] 4. Food and exercise tracking

[1476] Users record their daily dietary habits and exercise amounts on their devices and send the information to a server. The entered data is stored in a database. For example, if a user eats yogurt and fruit for breakfast, the dietary habits and calorie information are sent from the device to the server and stored in the database.

[1477] 5. Analyze records and adjust plans

[1478] The server periodically analyzes the stored diet and exercise records to evaluate the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and notified to the user, allowing the user to continue their diet based on the updated plan.

[1479] 6. Providing information on seasonal ingredients

[1480] The server collects seasonal food information from external sources and incorporates it into the generative AI model. This generates recipes using seasonal ingredients and provides them to the user. For example, in autumn, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (e.g., sweet potato soup and stir-fried mushrooms) are suggested.

[1481] Linking with Healthcare App

[1482] This system works with the user's health management software to obtain weight and exercise data, allowing for more accurate and detailed customized plans. The server receives the data from the health management software and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates plans.

[1483] This invention allows users to follow individually customized meal and exercise plans, enabling them to achieve an effective diet. Regular progress assessments and plan adjustments help users maintain their motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[1484] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1485] Step 1:

[1486] The user accesses the diet support app using a device and inputs their basic physical information and diet goals, including age, gender, height, weight, target weight, and target period. By pressing the "Send" button, the input information is sent from the device to the server.

[1487] Input: Age, gender, height, weight, target weight, target period

[1488] Output: User's physical information and goals received by the server

[1489] Step 2:

[1490] The server receives the user's submitted physical information and goals and stores them in a database, where they are associated with the user ID.

[1491] Input: User information sent from the device

[1492] Output: User information stored in the database

[1493] Step 3:

[1494] The server uses the stored information to provide input data to the generative AI model, which calculates the user's basal metabolic rate and activity level and generates an optimal meal plan and exercise plan.

[1495] Input: Saved user information

[1496] Output: Generated meal and exercise plans

[1497] Step 4:

[1498] The server converts the generated meal plan and exercise plan into a data format such as JSON and sends it to the device, which parses the received data and displays it on the user's UI.

[1499] Input: Generated meal and exercise plans

[1500] Output: Meal and exercise plan displayed on device

[1501] Step 5:

[1502] Users record their daily diet and exercise on their device, which then sends the records to a server and stores them in a database.

[1503] Input: User-logged food and exercise information

[1504] Output: Records stored in the database

[1505] Step 6:

[1506] The server periodically analyzes the stored diet and exercise records and generates new diet and exercise plans using a generative AI model, which are then sent back to the device and notified to the user.

[1507] Input: Stored recordings and generative AI models

[1508] Output: Updated meal and exercise plans

[1509] Step 7:

[1510] The server collects seasonal ingredient information from external sources and reflects it in the generative AI model. For example, if it is autumn, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms will be generated and provided to the user.

[1511] Input: Seasonal food information from external sources

[1512] Output: Meal plan incorporating seasonal ingredients

[1513] (Application example 1)

[1514] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1515] Conventional diet support systems provide meal plans and exercise plans based on the user's physical information and diet goals, but lack support when the user actually purchases ingredients and products in stores. This requires the user to take the time and effort to select the appropriate ingredients and products themselves, which can result in the diet plan not being carried out effectively. Furthermore, there is an issue of increased time and effort required to find the ingredients and products that best suit the user's diet goals, which can decrease the user's motivation.

[1516] 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.

[1517] In this invention, the server includes means for receiving physical information and goals input by the user, means for generating a meal plan and exercise plan based on the physical information and goals, means for providing the generated meal plan and exercise plan to the user, means for the user to input a record of meals and exercise, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for navigating the locations and information of ingredients and products in the store, means for recommending ingredients and products that are optimal for the user's diet goal, and means for collecting seasonal ingredient information and reflecting it in the meal plan. This allows the user to receive appropriate support when selecting ingredients and products in a physical store, enabling them to carry out an effective diet plan.

[1518] "User" refers to an individual who uses the system.

[1519] "Physical information" refers to information about an individual's physical characteristics, such as age, sex, height, and weight.

[1520] "Goal" refers to a diet-related goal that a user wants to achieve (e.g., a target weight or a target period).

[1521] "Meal Plan" refers to specific meal suggestions based on a user's physical information and goals.

[1522] An "exercise plan" refers to specific exercise content suggested based on the user's physical information and goals.

[1523] "Navigation" refers to guiding users to find the products or ingredients they are looking for within a store.

[1524] "Recommendation" refers to suggesting ingredients or products that best suit the user's goals.

[1525] "Saving" refers to storing input data in a storage device such as a database.

[1526] "Analysis" refers to evaluating a user's progress and trends based on collected data.

[1527] "Update" refers to modifying the meal plan and exercise plan based on the analysis results.

[1528] "Information on seasonal ingredients" refers to information on ingredients that are most nutritious and fresh in a particular season.

[1529] "Server" refers to the central device of the system that processes various data such as user information, plan generation, storage, and analysis.

[1530] An embodiment of the present invention is a system for users to receive diet support at a physical store, and this system is composed of a server, a terminal, and a generative AI model. The specific configuration and operation of the system are described in detail below.

[1531] System configuration and operation

[1532] This system mainly provides user information input, plan generation, in-store navigation, and a recommendation system. The role and operation of each component are explained in detail below.

[1533] 1. Enter and save user information

[1534] Users access the app using a smartphone or another device and first enter their basic physical information (age, gender, height, weight) and diet goals (target weight and target period). The entered information is sent from the device to a server and stored in a database.

[1535] 2. Plan generation and provision

[1536] The server uses a generative AI model to generate optimal meal and exercise plans based on the user's physical information and diet goals. This generation process is customized based on the needs of each individual user. The generated meal and exercise plans are then sent to the device and provided to the user.

[1537] 3. In-store navigation

[1538] When users visit a physical store, they can use their smartphone or smart glasses to navigate to the location of the ingredients or products they want within the store, including by tracking their current location in real time and displaying the optimal route.

[1539] 4. Food and product recommendations

[1540] The generative AI model also has the ability to recommend the best ingredients and products in stores based on the user's diet goals, making it easy for users to find the products that are best suited to their diet.

[1541] 5. Recording and analyzing diet and exercise

[1542] Users record their daily diet and exercise routines on their devices. These records are sent to a server and stored in a database. The server periodically analyzes the stored records to assess the user's progress. If necessary, the generative AI model is used again to update the diet and exercise plans.

[1543] 6. Providing information on seasonal ingredients

[1544] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[1545] Specific examples of hardware and software used

[1546] Hardware: Smartphones, smart glasses, servers

[1547] Software: Django (server-side framework), React Native (front-end framework), Hugging Face Transformers (generative AI model)

[1548] Examples:

[1549] For example, when a user wears smart glasses and enters a brick-and-mortar store, they will see information such as "Sweet potatoes are low in calories and in season now" and a suggested recipe for "Sweet Potato Soup."

[1550] "Please enter your user information (age, gender, height, weight) and diet goal (target weight, period)"

[1551] "We will guide you to the best products for your diet based on your current location."

[1552] "Why not try my recommended recipe, sweet potato soup?"

[1553] As described above, the present invention is a system that supports users in effectively carrying out diet plans in physical stores, and is expected to increase user satisfaction and results.

[1554] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1555] Step 1:

[1556] A user input physical information and goals are received.

[1557] Input: Information such as age, gender, height, weight, target weight, and target period entered by the user via a device (such as a smartphone).

[1558] Data processing: The terminal converts the input data into an appropriate format for transmission to the server.

[1559] Output: User information is sent to the server and stored.

[1560] Example: A user inputs "30 years old, male, height 180cm, weight 85kg, target weight 75kg, target period 6 months."

[1561] Step 2:

[1562] The server generates a plan using a generative AI model.

[1563] Input: User's physical information and diet goals stored on the server.

[1564] Data calculation: The generative AI model calculates basal metabolic rate and calorie intake based on user information, and generates optimal meal and exercise plans.

[1565] Output: Meal and exercise plans are generated and stored on the server.

[1566] Example: Calculate the user's basal metabolic rate, calculate the amount of calories needed per day, and then suggest menu items such as yogurt for breakfast, salad for lunch, and fish dishes for dinner.

[1567] Step 3:

[1568] The server provides the generated plan to the user.

[1569] Input: Generated meal and exercise plans.

[1570] Data processing: Format the plan into an easy-to-read format and send it to the device.

[1571] Output: The meal and exercise plan is displayed on the user's device.

[1572] Example: A user's smartphone screen displays "Today's menu: Breakfast = yogurt, lunch = salad, dinner = fish dish."

[1573] Step 4:

[1574] A user visits a physical store and begins navigation.

[1575] Input: User's current location (e.g. GPS) and a map of the store.

[1576] Data calculation: Based on the user's current location, the device calculates the shortest route to the desired ingredients or products and displays the route on a map of the store.

[1577] Output: The location and route of the desired ingredients or products are displayed on the user's device.

[1578] Example: The smart glasses display will show navigation such as "Sweet potato section → go straight ahead 20m on the right."

[1579] Step 5:

[1580] The server recommends ingredients and products based on the user's diet goals.

[1581] Input: User's physical information, diet goals, and current location.

[1582] Data computation: Generative AI models recommend ingredients and products that best fit a user's goals and incorporate this information into navigation.

[1583] Output: Recommended product information is displayed on the user's device.

[1584] Example: Smart glasses display the message, "This product is low in calories and perfect for dieting."

[1585] Step 6:

[1586] The user enters a diet and exercise log.

[1587] Input: Daily diet and exercise information entered via the device.

[1588] Data processing: The terminal converts the input data into an appropriate format for transmission to the server.

[1589] Output: Food and exercise records are stored on a server.

[1590] Example: A user inputs "Breakfast: yogurt 200kcal, exercise: 30 minutes of jogging."

[1591] Step 7:

[1592] The server analyzes the records and updates the plan.

[1593] Input: Your saved daily food and exercise logs.

[1594] Data calculation: The server analyzes the user's progress based on the stored records and generates a new meal and exercise plan, again using the generative AI model.

[1595] Output: A new updated meal and exercise plan is generated and sent to the user device.

[1596] Example: If the user is making good progress towards their goal, a new exercise plan is suggested: "Increase jogging distance."

[1597] Step 8:

[1598] The server reflects seasonal food information.

[1599] Input: Seasonal food information from external sources.

[1600] Data processing: The server applies this information to the generative AI model to generate an optimized meal plan.

[1601] Output: A meal plan using seasonal ingredients is sent to the user's device.

[1602] Example: In the fall, "sweet potato soup" and "stir-fried mushrooms" are suggested.

[1603] The specific operation has been explained above based on each processing step, and the overall picture of the system that allows users to receive effective diet support at a physical store has been detailed.

[1604] 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.

[1605] The present invention is a system that effectively supports dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals, and by combining this with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1606] System configuration and operation

[1607] This system consists of a user, a terminal, a server, a generative AI model, and an emotion engine. The role and operation of each component are explained below.

[1608] 1. Enter your user information

[1609] The user accesses the app using a device and first enters their basic physical information (age, gender, height, weight, etc.) and diet goals (e.g., target weight and target period). The entered information is then sent from the device to the server.

[1610] 2. Save information and generate plans

[1611] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal meal and exercise plan. This plan generation process is customized based on the user's individual needs.

[1612] Examples:

[1613] For example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg,

[1614] The generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, then suggests optimal meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise regimens (30 minutes of jogging three times a week).

[1615] 3. Introducing the Emotion Engine

[1616] The emotion engine recognizes emotions from user input and sensor data and stores them in a database, allowing the system to understand the user's emotional state in real time and use it to adjust plans.

[1617] Examples:

[1618] If a user's motivation drops while following a plan, the emotion engine will recognize the change in emotion and adjust the diet and exercise plan. The emotion engine also analyzes the user's past emotion history and provides specific advice to increase the user's motivation.

[1619] 4. Presenting the plan

[1620] The meal plan and exercise plan generated by the server are sent to the terminal, which displays these plans in a user-friendly format, and the user follows the proposed plan to follow their daily diet and exercise routine.

[1621] 5. Food and exercise tracking

[1622] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[1623] Examples:

[1624] For example, if a user has yogurt and fruit for breakfast, the details of the meal (yogurt, fruit) and calorie information are entered into the terminal and sent to the server. Similarly, if a user goes jogging, the amount of exercise is recorded.

[1625] 6. Analyze records and adjust plans

[1626] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[1627] 7. Providing information on seasonal ingredients

[1628] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[1629] Examples:

[1630] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (for example, sweet potato soup and stir-fried mushrooms) are suggested.

[1631] Linking with Healthcare App

[1632] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. This allows for more accurate and detailed data to be used to customize the plan. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[1633] Maintaining motivation with an emotional engine

[1634] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice based on the emotion. For example, if the user feels stressed or anxious while following a plan, the emotion engine reports that information to the server, which then adjusts parts of the plan. The emotion engine also predicts the user's reaction in certain situations based on past emotional history and provides preventative advice.

[1635] Examples:

[1636] If a user tends to feel stressed during a particular training session, the emotion engine can use that information to provide advice on how to relax beforehand or change the exercise plan to a different format.

[1637] This invention allows users to implement individually customized meal plans and exercise plans, and provides support that responds to emotional changes. This allows for effective dieting and health management, and can continuously increase the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, and the user can continue without getting bored.

[1638] The processing flow will be explained below.

[1639] Specific steps of the program's processing (including the emotion engine)

[1640] 1. Enter and save user information

[1641] Step 1:

[1642] The user uses the device to input their physical information (age, sex, height, weight) and diet goals (target weight, target period) into the app.

[1643] Step 2:

[1644] The terminal checks the entered information and converts it into JSON format.

[1645] Step 3:

[1646] The terminal sends the converted JSON data to the server.

[1647] Step 4:

[1648] The server parses the received JSON data and stores it in a database.

[1649] 2. Generate meal and exercise plans

[1650] Step 5:

[1651] The server retrieves the user's physical information and goals from a database.

[1652] Step 6:

[1653] The server inputs the acquired data into a generative AI model to generate optimal meal and exercise plans for the user.

[1654] Step 7:

[1655] The server converts the generated plan into JSON format and sends it to the terminal.

[1656] Step 8:

[1657] The terminal displays the received plan data in a format that is easy for the user to view.

[1658] 3. Introducing the Emotion Engine

[1659] Step 9:

[1660] The user inputs their daily emotional state into the terminal, which then transmits this information to the emotion engine.

[1661] Step 10:

[1662] The device collects data from built-in or external sensors (e.g., heart rate and facial expression recognition data) and sends it to the emotion engine.

[1663] Step 11:

[1664] The emotion engine analyzes the received data and recognizes the user's emotional state.

[1665] Step 12:

[1666] The emotion engine sends the analysis results to the server.

[1667] 4. Presenting the plan

[1668] Step 13:

[1669] The meal plan and exercise plan generated by the server are sent to the terminal.

[1670] Step 14:

[1671] The terminal displays these plans in a user-friendly format.

[1672] Step 15:

[1673] The user follows the suggested diet and exercise plan daily.

[1674] 5. Food and exercise tracking

[1675] Step 16:

[1676] The user inputs the details of their daily meals and the amount of exercise they do into the recording screen of the terminal.

[1677] Step 17:

[1678] The terminal checks the entered records and converts them into JSON format.

[1679] Step 18:

[1680] The terminal sends the converted JSON data to the server.

[1681] Step 19:

[1682] The server parses the received recording data and stores it in a database.

[1683] 6. Analyze records and adjust plans

[1684] Step 20:

[1685] The server periodically retrieves the user's diet and exercise record data from the database.

[1686] Step 21:

[1687] The server integrates the acquired data with the emotion engine data, analyzes it, and evaluates the user's progress.

[1688] Step 22:

[1689] The server uses the generative AI model to update the meal and exercise plans as needed.

[1690] Step 23:

[1691] The server converts the updated plan into JSON format and sends it to the device.

[1692] Step 24:

[1693] The terminal notifies the user of the updated plan.

[1694] 7. Providing information on seasonal ingredients

[1695] Step 25:

[1696] The server collects seasonal food information from external information sources.

[1697] Step 26:

[1698] The server stores the collected information on seasonal ingredients in a database.

[1699] Step 27:

[1700] The server uses the stored seasonal ingredient information to reflect seasonal ingredients in meal plans based on a generative AI model.

[1701] Step 28:

[1702] The server generates recipes using seasonal ingredients and sends them to the terminal.

[1703] Step 29:

[1704] The terminal proposes the generated recipe to the user.

[1705] Linking with Healthcare App

[1706] Step 30:

[1707] The server receives weight and exercise data from the user's healthcare app.

[1708] Step 31:

[1709] The server stores the received data in a database.

[1710] Step 32:

[1711] The server applies the additional stored data to the generative AI model to generate a highly accurate plan.

[1712] Maintaining motivation with an emotional engine

[1713] Step 33:

[1714] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice according to their emotions.

[1715] Step 34:

[1716] Based on past emotional history, the server predicts the user's reaction in specific situations and provides preventative advice.

[1717] The above process allows users to implement individually customized meal and exercise plans. It also provides support that responds to emotional changes, enabling effective dieting and health management, and sustainably increasing the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[1718] Example 2

[1719] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1720] Conventional diet support systems have difficulty providing effective support due to their difficulty in highly customizing diet plans based on the user's individual physical information and emotional state. Furthermore, the user's information is fixed, making it difficult to provide flexible plans that respond to changing emotions and seasonal changes. The present invention aims to solve these problems by providing optimal plans that respond to the user's individual needs and emotional state, thereby improving the effectiveness of dieting.

[1721] 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 means for receiving physical information and goals input by the user, means for using a generative model to generate a meal plan and an exercise plan based on the physical information and goals, means for providing the generated meal plan and exercise plan to the user, means for the user to input a meal and exercise record, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for recognizing the user's emotional state and adjusting the plan based on this, and means for collecting seasonal food information and reflecting this in the meal plan. This enables effective diet support while responding to the user's individual physical information and emotional state.

[1722] "User" refers to an individual who uses the system to input physical information and diet goals and receives meal and exercise plans.

[1723] "Physical information" refers to basic physical information about the user, such as age, gender, height, and weight.

[1724] "Goal" refers to a specific goal for dieting that the user wants to achieve, such as a target weight or a target period.

[1725] "Meal Plan" refers to a specific daily meal plan suggested based on a user's physical information and goals.

[1726] An "exercise plan" refers to a plan that specifically outlines the exercise content for one day, proposed based on the user's physical information and goals.

[1727] "Generative model" refers to an AI model that automatically generates optimal meal and exercise plans based on a user's physical information and goals.

[1728] "Saving" refers to the act of recording and storing input data in a storage device such as a database.

[1729] "Analysis" refers to analyzing stored data and assessing a user's progress and trends.

[1730] "Emotional state" refers to various emotional states that users experience in their daily lives, and specifically includes motivation, stress, happiness, etc.

[1731] "Seasonal food information" refers to information about the freshest and most nutritious food items on the market each season.

[1732] A "healthcare app" refers to application software for managing data related to a user's body and exercise.

[1733] "Adjusting your plan" refers to the process of reviewing and optimizing your existing meal and exercise plans based on your progress and emotional state.

[1734] This invention is a system that effectively supports dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals, and by combining this with an emotion engine that recognizes the user's emotions. This system is composed of a user, a device, a server, a generative AI model, and an emotion engine. The role and operation of each component are explained below.

[1735] Entering user information

[1736] Users access the app using a device such as a smartphone or tablet. They enter basic physical information such as age, gender, height, and weight, as well as their diet goals (e.g., target weight and target period). The entered information is sent from the device to the server.

[1737] Save information and generate plans

[1738] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal diet and exercise plan. This generative AI model uses, for example, OpenAI's GPT model. The generation process is customized based on the user's individual needs.

[1739] Examples:

[1740] For example, if a user is a 35-year-old male with a height of 175 cm and a weight of 80 kg, with a target weight of 70 kg, the generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, and then suggests specific meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise options (e.g., 30 minutes of jogging three times a week).

[1741] Example prompts for input to a generative AI model:

[1742] User Information:

[1743] Age: 35

[1744] Gender: Male

[1745] Height: 175cm

[1746] Weight: 80kg

[1747] Target weight: 70kg

[1748] Prompts for input to generative AI models:

[1749] Consider your basal metabolic rate and activity level and generate optimal meal and exercise plans based on the following information:

[1750] Age: 35

[1751] Gender: Male

[1752] Height: 175cm

[1753] Weight: 80kg

[1754] Target weight: 70kg

[1755] Introducing the Emotion Engine

[1756] The emotion engine recognizes emotions from user input data and sensor data (e.g., smartwatches and fitness trackers) and stores the data in a database to understand the user's emotional state in real time. The emotion engine uses, for example, Affectiva's emotion recognition engine. The server adjusts the plan based on this.

[1757] Examples:

[1758] If a user's motivation drops while following a plan, the emotion engine will recognize the change and adjust their diet or exercise plan, or provide advice to boost their motivation.

[1759] Presenting the plan

[1760] The meal plan and exercise plan generated by the server are sent to the terminal, which displays the plan to the user in a visually easy-to-understand format. The user then follows the proposed plan to follow their daily diet and exercise routine.

[1761] Food and exercise tracking

[1762] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[1763] Examples:

[1764] If a user has yogurt and fruit for breakfast, the device will input the meal details (yogurt, fruit) and calorie information and send it to the server. Similarly, if a user goes jogging, the amount of exercise will be recorded.

[1765] Analyze records and adjust plans

[1766] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[1767] Providing information on seasonal ingredients

[1768] The server collects information on seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[1769] Examples:

[1770] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (e.g., sweet potato soup and stir-fried mushrooms) are suggested.

[1771] Linking with Healthcare App

[1772] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[1773] Maintaining motivation with an emotional engine

[1774] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice according to their emotions. It predicts the user's emotional changes in specific situations and recommends measures to increase motivation in advance.

[1775] Examples:

[1776] If a user is prone to feeling stressed during a particular training session, the emotion engine can predict this in advance and provide advice on how to relax or change the exercise plan.

[1777] This invention allows users to implement individually customized meal and exercise plans and provides support that responds to emotional changes. This also allows for effective dieting and health management, and can continuously increase the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, and the user can continue without getting bored.

[1778] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1779] Step 1:

[1780] The user accesses the app using a device and enters their physical information (e.g., age, gender, height, weight) and diet goals (e.g., target weight and target period). The information entered is then entered into a form and sent to the server by pressing the submit button. User profile data is generated based on the input and passed to the server.

[1781] Specific input example:

[1782] User input information:

[1783] Age: 30

[1784] Gender: Female

[1785] Height: 165cm

[1786] Weight: 70kg

[1787] Target weight: 60kg

[1788] Server input:

[1789] User profile data (age, gender, height, weight, goal weight)

[1790] Server output:

[1791] Profile save confirmation message

[1792] Step 2:

[1793] The server stores the received user information in a database, which is then input into a generative AI model to generate meal and exercise plans.

[1794] Specific input example:

[1795] Server input:

[1796] User profile data (age, gender, height, weight, goal weight)

[1797] Server output:

[1798] Save to database

[1799] Specific behavior:

[1800] The server connects to the database and executes a SQL query to save the user information. After the save is complete, a save success message is generated.

[1801] Step 3:

[1802] The server generates prompt sentences based on the stored user information and sends the prompt sentences to the generative AI model, which then generates optimal meal and exercise plans for the user based on the prompt sentences.

[1803] Specific input example:

[1804] Prompt for the generative AI model:

[1805] User Information:

[1806] Age: 30

[1807] Gender: Female

[1808] Height: 165cm

[1809] Weight: 70kg

[1810] Target weight: 60kg

[1811] Prompts for input to generative AI models:

[1812] Consider your basal metabolic rate and activity level and generate optimal meal and exercise plans based on the following information:

[1813] Age: 30

[1814] Gender: Female

[1815] Height: 165cm

[1816] Weight: 70kg

[1817] Target weight: 60kg

[1818] Server input:

[1819] Prompt statement

[1820] Output of the generative AI model:

[1821] Meal and exercise plans (e.g., yogurt and fruit for breakfast, salad and chicken for lunch, fish and stir-fried vegetables for dinner)

[1822] Specific behavior:

[1823] The server sends the generated prompt text to the generative AI model, which then performs calculations and data analysis to generate and return a meal plan and exercise plan.

[1824] Step 4:

[1825] The server sends the meal and exercise plans derived from the generative AI model to the device, which displays these plans in a user-friendly format.

[1826] Specific input example:

[1827] Server input:

[1828] Meal and exercise plans

[1829] Server output:

[1830] Sending plan data to the device

[1831] Type in the terminal:

[1832] Plan data received on your device

[1833] Terminal output:

[1834] User interface (displaying meal and exercise plans)

[1835] Specific behavior:

[1836] The server transmits the plan data to the terminal, which receives it and displays it on the user interface.

[1837] Step 5:

[1838] Users record their daily diet and exercise on their devices, and the recorded data is sent to a server.

[1839] Specific input example:

[1840] User input:

[1841] Food log (e.g., yogurt and fruit for breakfast)

[1842] Exercise record (e.g. 30 minutes of jogging)

[1843] Type in the terminal:

[1844] Food and exercise record data

[1845] Terminal output:

[1846] Sending recorded data to the server

[1847] Server input:

[1848] Food and exercise record data

[1849] Specific behavior:

[1850] The user enters their diet and exercise record data into the app and presses the save button, sending the data to the server.

[1851] Step 6:

[1852] The server stores the received food and exercise records in a database, and then analyzes this data to assess the user's progress.

[1853] Specific input example:

[1854] Server input:

[1855] Food and exercise record data

[1856] Server output:

[1857] Data saving operations

[1858] Progress Assessment Report

[1859] Specific behavior:

[1860] The server stores the food and exercise records in a database and analyzes them with an analytical algorithm to assess progress.

[1861] Step 7:

[1862] Based on the progress assessment results, the server re-uses the generative AI model to update the meal and exercise plans if necessary, and the new plans are sent to the device and notified to the user.

[1863] Specific input example:

[1864] Server input:

[1865] Progress evaluation results

[1866] Server output:

[1867] Updated meal and exercise plans

[1868] Type in the terminal:

[1869] New plan data

[1870] Terminal output:

[1871] View and notify renewal plans

[1872] Specific behavior:

[1873] The server uses the progress data to generate a new plan using the generative AI model and sends the result back to the device, which updates its user interface to display the new plan.

[1874] Step 8:

[1875] The server collects information on seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[1876] Specific input example:

[1877] Server input:

[1878] Seasonal food data from external sources

[1879] Server output:

[1880] Updated Recipe Plans

[1881] Type in the terminal:

[1882] New Recipe Plan

[1883] Terminal output:

[1884] Displaying recipes using seasonal ingredients

[1885] Specific behavior:

[1886] The server retrieves seasonal food data from an external API, applies it to the generative AI model, and generates a new recipe plan. The plan is then sent to the device and displayed to the user.

[1887] Step 9:

[1888] The server periodically obtains weight and exercise data from the user's healthcare app, which is then stored in a database and reflected in the generative AI model.

[1889] Specific input example:

[1890] Server input:

[1891] Weight and exercise data from the Health app

[1892] Server output:

[1893] Updated database

[1894] Specific behavior:

[1895] The server retrieves user data from the healthcare app's API and stores it in a database.

[1896] Step 10:

[1897] The emotion engine recognizes the user's emotional state in real time based on sensor data and input data, and sends the results to the server, which then generates feedback and advice based on that information and provides it to the user.

[1898] Specific input example:

[1899] Emotion Engine Input:

[1900] Sensor data and user emotion input data

[1901] Emotion engine output:

[1902] Emotion analysis results

[1903] Server input:

[1904] Emotion analysis results

[1905] Server output:

[1906] Feedback and Advice

[1907] Specific behavior:

[1908] The emotion engine analyzes the user's emotions, and the server generates feedback and advice based on that information and sends it to the device.

[1909] (Application example 2)

[1910] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1911] In today's busy lifestyles, effective support for personal health management and dieting requires providing plans customized to each user's physical information and emotional state. However, conventional systems have difficulty adjusting meal and exercise plans to accommodate emotional changes, and they lack the information to provide specific ingredients for quickly implementing the generated plans. This creates challenges for maintaining user motivation and sticking to the plan.

[1912] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving physical information and goals input by the user, means for generating a meal plan and an exercise plan based on the physical information and goals, and means for adjusting the meal plan and the exercise plan based on emotional data. This makes it possible to provide a customized plan tailored to each user's individual physical information and emotional state. In addition, by providing means for delivering ingredients based on the generated meal plan, the user can receive support in quickly implementing the plan, which is expected to maintain motivation and promote health management.

[1913] The "means for receiving user-entered physical information and goals" refers to an interface through which a user inputs their basic physical data (age, gender, height, weight, etc.) and the health goals they wish to achieve (e.g., target weight and target period), and the system receives this information.

[1914] The "means for generating a meal plan and exercise plan based on the physical information and goals" refers to an algorithm or software for generating an optimal meal and exercise plan based on the user's input physical information and health goals.

[1915] The "means for providing the generated meal plan and exercise plan to the user" refers to a device or application for presenting the meal plan and exercise plan generated by the system to the user in the form of visual or text information.

[1916] "Means for recognizing user emotions and acquiring emotional data" refers to sensors and analytical software that monitor user emotions in real time and collect that data.

[1917] The "means for adjusting meal plans and exercise plans based on said emotional data" is an algorithm or generative model for optimizing or modifying existing meal plans and exercise plans based on collected emotional data.

[1918] The "means for the user to input records of meals and exercise" refers to an interface that allows the user to input the details of daily meals and the amount of exercise into an application or device.

[1919] The "means for storing and analyzing the entered diet and exercise records" refers to software or hardware for storing the diet and exercise records entered by the user in a database for later analysis.

[1920] The "means for updating the diet and exercise plans based on the analysis results and emotional data" refers to an algorithm for analyzing the stored data and emotional data and updating the diet and exercise plans based thereon.

[1921] The "means for collecting seasonal food information and reflecting it in the meal plan" is a data acquisition system for externally collecting seasonal food information and reflecting it in the meal plan that is generated.

[1922] The "means for delivering ingredients based on the generated meal plan" is a delivery system for quickly delivering ingredients required for the generated meal plan to the user.

[1923] MODE FOR CARRYING OUT THE INVENTION

[1924] This invention is a system that proposes optimal meal and exercise plans based on a user's individual physical information, diet goals, and emotional state, and even delivers ingredients to help them carry out the plans. The main components of this system include a server, a terminal, a generative AI model, and an emotion engine.

[1925] 1. Enter and save user information

[1926] Device:

[1927] Users enter their physical information (age, sex, height, weight, etc.) and goals (e.g., target weight and target period) through an application on their smartphone or tablet.

[1928] server:

[1929] It receives information sent from the device and stores it in a database, which creates a base for various data that will be needed later.

[1930] 2. Creating a menu

[1931] server:

[1932] Based on the saved user information, a generative AI model is used to generate meal and exercise plans. The generated plans are customized based on the user's basal metabolic rate and activity level. For example, for a 35-year-old man who is 175 cm tall, weighs 80 kg, and has a target weight of 70 kg, the generative AI model calculates the user's basal metabolic rate and determines the required calorie intake. It then suggests appropriate meal plans (yogurt and fruit for breakfast, salad and chicken breast for lunch, and stir-fried vegetables and fish for dinner) and exercise options (30 minutes of jogging three times a week).

[1933] 3. Introducing the Emotion Engine

[1934] Device:

[1935] It uses the smartphone's camera, microphone, and other sensors to recognize and monitor the user's emotions in real time and obtain emotional data.

[1936] server:

[1937] Using the captured emotion data, the generative AI model can readjust meal and exercise plans as needed, for example, by changing the meal plan to one that promotes relaxation if it detects that the user is feeling stressed while following the plan.

[1938] 4. Plan presentation and ingredients provided

[1939] Device:

[1940] The optimal meal plan and exercise plan sent from the server are presented to the user.

[1941] server:

[1942] The system arranges for the delivery of the necessary ingredients based on the generated meal plan, and provides the appropriate ingredients to the user through a delivery service.

[1943] 5. Food and exercise tracking

[1944] Device:

[1945] The user inputs the details of their daily diet and the amount of exercise they do. For example, if they eat yogurt and fruit for breakfast, the details and calorie information are entered. Similarly, the amount of exercise they do is also recorded.

[1946] server:

[1947] Receives the entered data and stores it in the database.

[1948] 6. Analyze records and adjust plans

[1949] server:

[1950] The stored data is periodically analyzed to assess the user's progress, and if necessary, the generative AI model is used to regenerate meal and exercise plans and present updated plans to the user.

[1951] 7. Providing information on seasonal ingredients

[1952] server:

[1953] This system collects seasonal food information from external sources and incorporates it into the generative AI model. This makes it possible to provide users with menus that utilize seasonal ingredients. For example, in autumn, it suggests recipes using sweet potatoes and mushrooms (sweet potato soup and stir-fried mushrooms).

[1954] Examples and prompts

[1955] For example, if a user feels stressed while working towards their weight goal, the smartphone camera can analyze the user's facial expressions to detect the stress.The emotion engine then adjusts the meal plan to include foods with a high relaxation effect (such as low-fat fish or green tea), and delivers these ingredients to the user via a delivery service.

[1956] Example prompt sentence:

[1957] "We have found that a 35-year-old male user is following a diet plan, but is feeling stressed. Regenerate the optimal meal plan for this situation and suggest meal menus that will have a relaxing effect. Output the action to deliver the suggested meal menu to the user."

[1958] The invention includes specific software (such as generative AI models and emotion engines) used for all data processing and analysis, and also integrates an efficient delivery system to facilitate the execution of the meal plan.

[1959] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1960] Step 1:

[1961] Entering user information

[1962] Device: The user enters their physical information (age, gender, height, weight) and diet goals (target weight, target period) through a dedicated mobile application.

[1963] Input: Physical information and goals manually entered by the user.

[1964] Output: The input information is sent to the server in real time.

[1965] Step 2:

[1966] Retention of Information

[1967] Server: Stores the received user information in a database. This step lays the foundation for centrally managing all input data.

[1968] Input: User-entered physical information and goals.

[1969] Output: Saved user information data.

[1970] Step 3:

[1971] Generate a plan

[1972] Server: Uses a generative AI model to generate optimal meal and exercise plans based on the user's individual physical information and goals. The generation process calculates basal metabolic rate and activity level to determine the required calorie intake.

[1973] Input: Saved user information data.

[1974] Output: Optimal diet and exercise plan.

[1975] Step 4:

[1976] Presenting the plan

[1977] Device: The meal plan and exercise plan sent from the server are presented to the user on the device. The user can visually check the proposed plan through the application.

[1978] Input: Meal and exercise plan from server.

[1979] Output: Show plan.

[1980] Step 5:

[1981] Emotion recognition

[1982] On-device: The smartphone's camera, microphone, and sensors are used to capture the user's emotional data in real time. The emotion engine analyzes this data to identify the user's emotional state.

[1983] Input: Data required for emotion recognition, such as camera footage and audio data.

[1984] Output: The user's emotional state.

[1985] Step 6:

[1986] Adjusting the plan

[1987] Server: Based on the acquired emotional data, the system adjusts the diet and exercise plans as needed. The generative AI model analyzes the emotional data and makes adjustments to reduce stress and increase motivation.

[1988] Input: Emotional data and existing diet and exercise plans.

[1989] Output: A tailored diet and exercise plan.

[1990] Step 7:

[1991] Food delivery

[1992] Server: Collects the necessary ingredients based on the adjusted and generated meal plan and delivers them to the user via a delivery service. This uses the delivery service's API.

[1993] Enter: your tailored meal plan.

[1994] Output: Grocery delivery to the user's home.

[1995] Step 8:

[1996] Food and exercise tracking

[1997] Device: The user enters their daily diet and exercise information into the app.

[1998] Input: Food and exercise information manually entered by the user.

[1999] Output: The entered recorded data is sent to the server and saved.

[2000] Step 9:

[2001] Analyze records and readjust plans

[2002] Server: Periodically analyzes the user's stored food and exercise logs to assess the user's progress and, if necessary, regenerates the meal and exercise plans using generative AI models.

[2003] Input: Stored recorded data and analysis results.

[2004] Output: An updated diet and exercise plan.

[2005] Step 10:

[2006] Reflecting seasonal food information

[2007] Server: Collects seasonal ingredients from external sources and incorporates them into meal plans. This process generates recipes that are easy to use and prioritize health.

[2008] Input: Seasonal food data collected from external sources.

[2009] Output: Meal plans using seasonal ingredients.

[2010] In this way, the system provides a customized plan according to the user's physical information and emotional state and is equipped with a series of technical means to support the execution of the plan.

[2011] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2012] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2013] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2014] [Fourth embodiment]

[2015] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2016] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2017] 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).

[2018] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2019] 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.

[2020] 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).

[2021] 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.

[2022] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2023] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2024] 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.

[2025] 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.

[2026] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2027] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2028] The present invention is a system for supporting dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals. Specific embodiments of this system are described below.

[2029] System configuration and operation

[2030] This system consists of a user, a terminal, a server, and a generative AI model. The role and operation of each component are explained below.

[2031] 1. Enter your user information

[2032] The user accesses the app using a device and first enters their basic physical information (age, gender, height, weight, etc.) and diet goals (e.g., target weight and target period). The entered information is then sent from the device to the server.

[2033] 2. Save information and generate plans

[2034] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal meal and exercise plan. This plan generation process is customized based on the user's individual needs.

[2035] Examples:

[2036] For example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg,

[2037] The generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, then suggests optimal meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise regimens (30 minutes of jogging three times a week).

[2038] 3. Presenting the plan

[2039] The meal plan and exercise plan generated by the server are sent to the terminal, which displays these plans in a user-friendly format, and the user follows the proposed plan to follow their daily diet and exercise routine.

[2040] 4. Food and exercise tracking

[2041] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[2042] Examples:

[2043] For example, if a user has yogurt and fruit for breakfast, the details of the meal (yogurt, fruit) and calorie information are entered into the terminal and sent to the server. Similarly, if a user goes jogging, the amount of exercise is recorded.

[2044] 5. Analyze records and adjust plans

[2045] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[2046] 6. Providing information on seasonal ingredients

[2047] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[2048] Examples:

[2049] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (for example, sweet potato soup and stir-fried mushrooms) are suggested.

[2050] Linking with Healthcare App

[2051] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. This allows for more accurate and detailed data to be used to customize the plan. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[2052] This invention allows users to follow individually customized meal and exercise plans, enabling them to achieve an effective diet. Regular progress assessments and plan adjustments help users maintain their motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[2053] The processing flow will be explained below.

[2054] Specific steps of the program's processing

[2055] 1. Enter and save user information

[2056] Step 1:

[2057] The user uses the device to input their physical information (age, sex, height, weight) and diet goals (target weight, target period) into the app.

[2058] Step 2:

[2059] The terminal checks the entered information and converts it into JSON format.

[2060] Step 3:

[2061] The terminal sends the converted JSON data to the server.

[2062] Step 4:

[2063] The server parses the received JSON data and stores it in a database.

[2064] 2. Generate meal and exercise plans

[2065] Step 5:

[2066] The server retrieves the user's physical information and goals from a database.

[2067] Step 6:

[2068] The server inputs the acquired data into a generative AI model to generate optimal meal and exercise plans for the user.

[2069] Step 7:

[2070] The server converts the generated plan into JSON format and sends it to the terminal.

[2071] Step 8:

[2072] The terminal displays the received plan data in a format that is easy for the user to view.

[2073] 3. Daily diet and exercise records

[2074] Step 9:

[2075] The user inputs the details of their daily meals and the amount of exercise they do into the recording screen of the terminal.

[2076] Step 10:

[2077] The terminal checks the entered records and converts them into JSON format.

[2078] Step 11:

[2079] The terminal sends the converted JSON data to the server.

[2080] Step 12:

[2081] The server parses the received recording data and stores it in a database.

[2082] 4. Regularly analyze and adjust your plan

[2083] Step 13:

[2084] The server periodically retrieves the user's diet and exercise record data from the database.

[2085] Step 14:

[2086] The server analyzes the acquired data and evaluates the user's progress.

[2087] Step 15:

[2088] The server again uses the generative AI model to update the meal and exercise plans as needed.

[2089] Step 16:

[2090] The server converts the updated plan into JSON format and sends it to the device.

[2091] Step 17:

[2092] The terminal notifies the user of the updated plan.

[2093] 5. Providing information on seasonal ingredients

[2094] Step 18:

[2095] The server collects seasonal food information from external information sources.

[2096] Step 19:

[2097] The server stores the collected information on seasonal ingredients in a database.

[2098] Step 20:

[2099] The server uses the stored seasonal ingredient information to reflect seasonal ingredients in meal plans based on a generative AI model.

[2100] Step 21:

[2101] The server generates recipes using seasonal ingredients and sends them to the terminal.

[2102] Step 22:

[2103] The terminal proposes the generated recipe to the user.

[2104] Linking with Healthcare App

[2105] Step 23:

[2106] The server receives weight and exercise data from the user's healthcare app.

[2107] Step 24:

[2108] The server stores the received data in a database.

[2109] Step 25:

[2110] The server applies the additional stored data to the generative AI model to generate a highly accurate plan.

[2111] Through the above process, users can implement individually customized meal and exercise plans, and regular progress assessments and plan updates are provided to support effective dieting.

[2112] Example 1

[2113] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2114] Conventional diet support systems often provide uniform meal and exercise plans, which lack sufficient customization based on the user's individual physical information and diet goals. Furthermore, because they do not utilize seasonal ingredients or the latest data from external sources, it is difficult to provide a plan that users can continue without getting bored. Furthermore, they lack appropriate feedback based on the user's detailed activity data, such as data obtained from linked health management software.

[2115] 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.

[2116] In this invention, the server includes means for receiving physical information and goals input by a user, means for using a generative model to generate a meal plan and an exercise plan based on the physical information and goals, means for the user to input a meal and exercise record, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for collecting seasonal food information and reflecting it in the meal plan, and means for updating the generative model using data obtained from an external information source. This makes it possible to provide customized plans according to the individual needs of users, create menus that never get boring using seasonal ingredients, and provide precise feedback based on detailed data.

[2117] "Physical information" refers to basic physical data such as the user's age, sex, height, weight, etc.

[2118] The "goal" is a specific target value for dieting set by the user, such as a target weight or a target period.

[2119] A "generative model" is an algorithm or program that uses artificial intelligence to generate optimal meal and exercise plans based on a user's physical information and goals.

[2120] A "meal plan" is a meal plan or menu suggested to suit a user's individual physical information and goals.

[2121] An "exercise plan" is a suggested exercise schedule or exercises based on the user's individual physical information and goals.

[2122] "Records" are information entered by the user regarding the details of daily meals and the amount of exercise.

[2123] "Analysis" is the process of evaluating a user's progress based on the stored records and updating their meal and exercise plans as needed.

[2124] "Seasonal food information" is information about fresh ingredients that are considered to be optimal for each season.

[2125] "External information sources" refers to any data source obtained from outside the system, including food databases and health management software, for example.

[2126] "Health management software" is an application that manages health data such as a user's weight and exercise.

[2127] This invention is a system that supports dieting by proposing optimal meal and exercise plans based on the user's individual physical information and diet goals. This system is mainly composed of a user, a terminal, a server, and a generative AI model.

[2128] System configuration and operation

[2129] 1. Enter your user information

[2130] Users access the diet support app using a device such as a smartphone or PC and enter their basic physical information (age, gender, height, weight, etc.) and diet goals (target weight and target period). This information is sent from the device to the server.

[2131] As a specific example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg, the input information is sent to the server and stored in the database.

[2132] Example prompt sentence:

[2133] "Write a program that inputs a user's physical information and diet goals and suggests optimal meal and exercise plans. The user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg. Calculate the user's basal metabolic rate and activity level, and suggest specific meal and exercise menus."

[2134] 2. Save information and generate plans

[2135] The server stores the received user's physical information and goals in a database and generates an optimal meal and exercise plan using a generative AI model, which creates a customized plan based on the user's individual physical information and goals.

[2136] For example, the generative AI model calculates the user's basal metabolic rate and activity level, and then suggests specific meal menus and exercise plans based on that. A specific meal menu example might be yogurt and fruit for breakfast, salad and chicken breast for lunch, and stir-fried vegetables and fish for dinner. The exercise suggestion is 30 minutes of jogging three times a week.

[2137] 3. Presenting the plan

[2138] The generated meal and exercise plans are sent from the server to the device. The device parses the received data and displays it on the user's UI. The meal and exercise plans can be displayed in a list format by day or week, allowing the user to follow the suggested plans for daily diet and exercise.

[2139] 4. Food and exercise tracking

[2140] Users record their daily dietary habits and exercise amounts on their devices and send the information to a server. The entered data is stored in a database. For example, if a user eats yogurt and fruit for breakfast, the dietary habits and calorie information are sent from the device to the server and stored in the database.

[2141] 5. Analyze records and adjust plans

[2142] The server periodically analyzes the stored diet and exercise records to evaluate the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and notified to the user, allowing the user to continue their diet based on the updated plan.

[2143] 6. Providing information on seasonal ingredients

[2144] The server collects seasonal food information from external sources and incorporates it into the generative AI model. This generates recipes using seasonal ingredients and provides them to the user. For example, in autumn, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (e.g., sweet potato soup and stir-fried mushrooms) are suggested.

[2145] Linking with Healthcare App

[2146] This system works with the user's health management software to obtain weight and exercise data, allowing for more accurate and detailed customized plans. The server receives the data from the health management software and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates plans.

[2147] This invention allows users to follow individually customized meal and exercise plans, enabling them to achieve an effective diet. Regular progress assessments and plan adjustments help users maintain their motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[2148] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2149] Step 1:

[2150] The user accesses the diet support app using a device and inputs their basic physical information and diet goals, including age, gender, height, weight, target weight, and target period. By pressing the "Send" button, the input information is sent from the device to the server.

[2151] Input: Age, gender, height, weight, target weight, target period

[2152] Output: User's physical information and goals received by the server

[2153] Step 2:

[2154] The server receives the user's submitted physical information and goals and stores them in a database, where they are associated with the user ID.

[2155] Input: User information sent from the device

[2156] Output: User information stored in the database

[2157] Step 3:

[2158] The server uses the stored information to provide input data to the generative AI model, which calculates the user's basal metabolic rate and activity level and generates an optimal meal plan and exercise plan.

[2159] Input: Saved user information

[2160] Output: Generated meal and exercise plans

[2161] Step 4:

[2162] The server converts the generated meal plan and exercise plan into a data format such as JSON and sends it to the device, which parses the received data and displays it on the user's UI.

[2163] Input: Generated meal and exercise plans

[2164] Output: Meal and exercise plan displayed on device

[2165] Step 5:

[2166] Users record their daily diet and exercise on their device, which then sends the records to a server and stores them in a database.

[2167] Input: User-logged food and exercise information

[2168] Output: Records stored in the database

[2169] Step 6:

[2170] The server periodically analyzes the stored diet and exercise records and generates new diet and exercise plans using a generative AI model, which are then sent back to the device and notified to the user.

[2171] Input: Stored recordings and generative AI models

[2172] Output: Updated meal and exercise plans

[2173] Step 7:

[2174] The server collects seasonal ingredient information from external sources and reflects it in the generative AI model. For example, if it is autumn, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms will be generated and provided to the user.

[2175] Input: Seasonal food information from external sources

[2176] Output: Meal plan incorporating seasonal ingredients

[2177] (Application example 1)

[2178] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2179] Conventional diet support systems provide meal plans and exercise plans based on the user's physical information and diet goals, but lack support when the user actually purchases ingredients and products in stores. This requires the user to take the time and effort to select the appropriate ingredients and products themselves, which can result in the diet plan not being carried out effectively. Furthermore, there is an issue of increased time and effort required to find the ingredients and products that best suit the user's diet goals, which can decrease the user's motivation.

[2180] 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.

[2181] In this invention, the server includes means for receiving physical information and goals input by the user, means for generating a meal plan and exercise plan based on the physical information and goals, means for providing the generated meal plan and exercise plan to the user, means for the user to input a record of meals and exercise, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for navigating the locations and information of ingredients and products in the store, means for recommending ingredients and products that are optimal for the user's diet goal, and means for collecting seasonal ingredient information and reflecting it in the meal plan. This allows the user to receive appropriate support when selecting ingredients and products in a physical store, enabling them to carry out an effective diet plan.

[2182] "User" refers to an individual who uses the system.

[2183] "Physical information" refers to information about an individual's physical characteristics, such as age, sex, height, and weight.

[2184] "Goal" refers to a diet-related goal that a user wants to achieve (e.g., a target weight or a target period).

[2185] "Meal Plan" refers to specific meal suggestions based on a user's physical information and goals.

[2186] An "exercise plan" refers to specific exercise content suggested based on the user's physical information and goals.

[2187] "Navigation" refers to guiding users to find the products or ingredients they are looking for within a store.

[2188] "Recommendation" refers to suggesting ingredients or products that best suit the user's goals.

[2189] "Saving" refers to storing input data in a storage device such as a database.

[2190] "Analysis" refers to evaluating a user's progress and trends based on collected data.

[2191] "Update" refers to modifying the meal plan and exercise plan based on the analysis results.

[2192] "Information on seasonal ingredients" refers to information on ingredients that are most nutritious and fresh in a particular season.

[2193] "Server" refers to the central device of the system that processes various data such as user information, plan generation, storage, and analysis.

[2194] An embodiment of the present invention is a system for users to receive diet support at a physical store, and this system is composed of a server, a terminal, and a generative AI model. The specific configuration and operation of the system are described in detail below.

[2195] System configuration and operation

[2196] This system mainly provides user information input, plan generation, in-store navigation, and a recommendation system. The role and operation of each component are explained in detail below.

[2197] 1. Enter and save user information

[2198] Users access the app using a smartphone or another device and first enter their basic physical information (age, gender, height, weight) and diet goals (target weight and target period). The entered information is sent from the device to a server and stored in a database.

[2199] 2. Plan generation and provision

[2200] The server uses a generative AI model to generate optimal meal and exercise plans based on the user's physical information and diet goals. This generation process is customized based on the needs of each individual user. The generated meal and exercise plans are then sent to the device and provided to the user.

[2201] 3. In-store navigation

[2202] When users visit a physical store, they can use their smartphone or smart glasses to navigate to the location of the ingredients or products they want within the store, including by tracking their current location in real time and displaying the optimal route.

[2203] 4. Food and product recommendations

[2204] The generative AI model also has the ability to recommend the best ingredients and products in stores based on the user's diet goals, making it easy for users to find the products that are best suited to their diet.

[2205] 5. Recording and analyzing diet and exercise

[2206] Users record their daily diet and exercise routines on their devices. These records are sent to a server and stored in a database. The server periodically analyzes the stored records to assess the user's progress. If necessary, the generative AI model is used again to update the diet and exercise plans.

[2207] 6. Providing information on seasonal ingredients

[2208] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[2209] Specific examples of hardware and software used

[2210] Hardware: Smartphones, smart glasses, servers

[2211] Software: Django (server-side framework), React Native (front-end framework), Hugging Face Transformers (generative AI model)

[2212] Examples:

[2213] For example, when a user wears smart glasses and enters a brick-and-mortar store, they will see information such as "Sweet potatoes are low in calories and in season now" and a suggested recipe for "Sweet Potato Soup."

[2214] "Please enter your user information (age, gender, height, weight) and diet goal (target weight, period)"

[2215] "We will guide you to the best products for your diet based on your current location."

[2216] "Why not try my recommended recipe, sweet potato soup?"

[2217] As described above, the present invention is a system that supports users in effectively carrying out diet plans in physical stores, and is expected to increase user satisfaction and results.

[2218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2219] Step 1:

[2220] A user input physical information and goals are received.

[2221] Input: Information such as age, gender, height, weight, target weight, and target period entered by the user via a device (such as a smartphone).

[2222] Data processing: The terminal converts the input data into an appropriate format for transmission to the server.

[2223] Output: User information is sent to the server and stored.

[2224] Example: A user inputs "30 years old, male, height 180cm, weight 85kg, target weight 75kg, target period 6 months."

[2225] Step 2:

[2226] The server generates a plan using a generative AI model.

[2227] Input: User's physical information and diet goals stored on the server.

[2228] Data calculation: The generative AI model calculates basal metabolic rate and calorie intake based on user information, and generates optimal meal and exercise plans.

[2229] Output: Meal and exercise plans are generated and stored on the server.

[2230] Example: Calculate the user's basal metabolic rate, calculate the amount of calories needed per day, and then suggest menu items such as yogurt for breakfast, salad for lunch, and fish dishes for dinner.

[2231] Step 3:

[2232] The server provides the generated plan to the user.

[2233] Input: Generated meal and exercise plans.

[2234] Data processing: Format the plan into an easy-to-read format and send it to the device.

[2235] Output: The meal and exercise plan is displayed on the user's device.

[2236] Example: A user's smartphone screen displays "Today's menu: Breakfast = yogurt, lunch = salad, dinner = fish dish."

[2237] Step 4:

[2238] A user visits a physical store and begins navigation.

[2239] Input: User's current location (e.g. GPS) and a map of the store.

[2240] Data calculation: Based on the user's current location, the device calculates the shortest route to the desired ingredients or products and displays the route on a map of the store.

[2241] Output: The location and route of the desired ingredients or products are displayed on the user's device.

[2242] Example: The smart glasses display will show navigation such as "Sweet potato section → go straight ahead 20m on the right."

[2243] Step 5:

[2244] The server recommends ingredients and products based on the user's diet goals.

[2245] Input: User's physical information, diet goals, and current location.

[2246] Data computation: Generative AI models recommend ingredients and products that best fit a user's goals and incorporate this information into navigation.

[2247] Output: Recommended product information is displayed on the user's device.

[2248] Example: Smart glasses display the message, "This product is low in calories and perfect for dieting."

[2249] Step 6:

[2250] The user enters a diet and exercise log.

[2251] Input: Daily diet and exercise information entered via the device.

[2252] Data processing: The terminal converts the input data into an appropriate format for transmission to the server.

[2253] Output: Food and exercise records are stored on a server.

[2254] Example: A user inputs "Breakfast: yogurt 200kcal, exercise: 30 minutes of jogging."

[2255] Step 7:

[2256] The server analyzes the records and updates the plan.

[2257] Input: Your saved daily food and exercise logs.

[2258] Data calculation: The server analyzes the user's progress based on the stored records and generates a new meal and exercise plan, again using the generative AI model.

[2259] Output: A new updated meal and exercise plan is generated and sent to the user device.

[2260] Example: If the user is making good progress towards their goal, a new exercise plan is suggested: "Increase jogging distance."

[2261] Step 8:

[2262] The server reflects seasonal food information.

[2263] Input: Seasonal food information from external sources.

[2264] Data processing: The server applies this information to the generative AI model to generate an optimized meal plan.

[2265] Output: A meal plan using seasonal ingredients is sent to the user's device.

[2266] Example: In the fall, "sweet potato soup" and "stir-fried mushrooms" are suggested.

[2267] The specific operation has been explained above based on each processing step, and the overall picture of the system that allows users to receive effective diet support at a physical store has been detailed.

[2268] 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.

[2269] The present invention is a system that effectively supports dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals, and by combining this with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[2270] System configuration and operation

[2271] This system consists of a user, a terminal, a server, a generative AI model, and an emotion engine. The role and operation of each component are explained below.

[2272] 1. Enter your user information

[2273] The user accesses the app using a device and first enters their basic physical information (age, gender, height, weight, etc.) and diet goals (e.g., target weight and target period). The entered information is then sent from the device to the server.

[2274] 2. Save information and generate plans

[2275] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal meal and exercise plan. This plan generation process is customized based on the user's individual needs.

[2276] Examples:

[2277] For example, if the user is a 35-year-old male, 175 cm tall, weighs 80 kg, and has a target weight of 70 kg,

[2278] The generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, then suggests optimal meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise regimens (30 minutes of jogging three times a week).

[2279] 3. Introducing the Emotion Engine

[2280] The emotion engine recognizes emotions from user input and sensor data and stores them in a database, allowing the system to understand the user's emotional state in real time and use it to adjust plans.

[2281] Examples:

[2282] If a user's motivation drops while following a plan, the emotion engine will recognize the change in emotion and adjust the diet and exercise plan. The emotion engine also analyzes the user's past emotion history and provides specific advice to increase the user's motivation.

[2283] 4. Presenting the plan

[2284] The meal plan and exercise plan generated by the server are sent to the terminal, which displays these plans in a user-friendly format, and the user follows the proposed plan to follow their daily diet and exercise routine.

[2285] 5. Food and exercise tracking

[2286] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[2287] Examples:

[2288] For example, if a user has yogurt and fruit for breakfast, the details of the meal (yogurt, fruit) and calorie information are entered into the terminal and sent to the server. Similarly, if a user goes jogging, the amount of exercise is recorded.

[2289] 6. Analyze records and adjust plans

[2290] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[2291] 7. Providing information on seasonal ingredients

[2292] The server collects information about seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[2293] Examples:

[2294] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (for example, sweet potato soup and stir-fried mushrooms) are suggested.

[2295] Linking with Healthcare App

[2296] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. This allows for more accurate and detailed data to be used to customize the plan. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[2297] Maintaining motivation with an emotional engine

[2298] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice based on the emotion. For example, if the user feels stressed or anxious while following a plan, the emotion engine reports that information to the server, which then adjusts parts of the plan. The emotion engine also predicts the user's reaction in certain situations based on past emotional history and provides preventative advice.

[2299] Examples:

[2300] If a user tends to feel stressed during a particular training session, the emotion engine can use that information to provide advice on how to relax beforehand or change the exercise plan to a different format.

[2301] This invention allows users to implement individually customized meal plans and exercise plans, and provides support that responds to emotional changes. This allows for effective dieting and health management, and can continuously increase the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, and the user can continue without getting bored.

[2302] The processing flow will be explained below.

[2303] Specific steps of the program's processing (including the emotion engine)

[2304] 1. Enter and save user information

[2305] Step 1:

[2306] The user uses the device to input their physical information (age, sex, height, weight) and diet goals (target weight, target period) into the app.

[2307] Step 2:

[2308] The terminal checks the entered information and converts it into JSON format.

[2309] Step 3:

[2310] The terminal sends the converted JSON data to the server.

[2311] Step 4:

[2312] The server parses the received JSON data and stores it in a database.

[2313] 2. Generate meal and exercise plans

[2314] Step 5:

[2315] The server retrieves the user's physical information and goals from a database.

[2316] Step 6:

[2317] The server inputs the acquired data into a generative AI model to generate optimal meal and exercise plans for the user.

[2318] Step 7:

[2319] The server converts the generated plan into JSON format and sends it to the terminal.

[2320] Step 8:

[2321] The terminal displays the received plan data in a format that is easy for the user to view.

[2322] 3. Introducing the Emotion Engine

[2323] Step 9:

[2324] The user inputs their daily emotional state into the terminal, which then transmits this information to the emotion engine.

[2325] Step 10:

[2326] The device collects data from built-in or external sensors (e.g., heart rate and facial expression recognition data) and sends it to the emotion engine.

[2327] Step 11:

[2328] The emotion engine analyzes the received data and recognizes the user's emotional state.

[2329] Step 12:

[2330] The emotion engine sends the analysis results to the server.

[2331] 4. Presenting the plan

[2332] Step 13:

[2333] The meal plan and exercise plan generated by the server are sent to the terminal.

[2334] Step 14:

[2335] The terminal displays these plans in a user-friendly format.

[2336] Step 15:

[2337] The user follows the suggested diet and exercise plan daily.

[2338] 5. Food and exercise tracking

[2339] Step 16:

[2340] The user inputs the details of their daily meals and the amount of exercise they do into the recording screen of the terminal.

[2341] Step 17:

[2342] The terminal checks the entered records and converts them into JSON format.

[2343] Step 18:

[2344] The terminal sends the converted JSON data to the server.

[2345] Step 19:

[2346] The server parses the received recording data and stores it in a database.

[2347] 6. Analyze records and adjust plans

[2348] Step 20:

[2349] The server periodically retrieves the user's diet and exercise record data from the database.

[2350] Step 21:

[2351] The server integrates the acquired data with the emotion engine data, analyzes it, and evaluates the user's progress.

[2352] Step 22:

[2353] The server uses the generative AI model to update the meal and exercise plans as needed.

[2354] Step 23:

[2355] The server converts the updated plan into JSON format and sends it to the device.

[2356] Step 24:

[2357] The terminal notifies the user of the updated plan.

[2358] 7. Providing information on seasonal ingredients

[2359] Step 25:

[2360] The server collects seasonal food information from external information sources.

[2361] Step 26:

[2362] The server stores the collected information on seasonal ingredients in a database.

[2363] Step 27:

[2364] The server uses the stored seasonal ingredient information to reflect seasonal ingredients in meal plans based on a generative AI model.

[2365] Step 28:

[2366] The server generates recipes using seasonal ingredients and sends them to the terminal.

[2367] Step 29:

[2368] The terminal proposes the generated recipe to the user.

[2369] Linking with Healthcare App

[2370] Step 30:

[2371] The server receives weight and exercise data from the user's healthcare app.

[2372] Step 31:

[2373] The server stores the received data in a database.

[2374] Step 32:

[2375] The server applies the additional stored data to the generative AI model to generate a highly accurate plan.

[2376] Maintaining motivation with an emotional engine

[2377] Step 33:

[2378] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice according to their emotions.

[2379] Step 34:

[2380] Based on past emotional history, the server predicts the user's reaction in specific situations and provides preventative advice.

[2381] The above process allows users to implement individually customized meal and exercise plans. It also provides support that responds to emotional changes, enabling effective dieting and health management, and sustainably increasing the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, ensuring that users can continue without getting bored.

[2382] Example 2

[2383] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2384] Conventional diet support systems have difficulty providing effective support due to their difficulty in highly customizing diet plans based on the user's individual physical information and emotional state. Furthermore, the user's information is fixed, making it difficult to provide flexible plans that respond to changing emotions and seasonal changes. The present invention aims to solve these problems by providing optimal plans that respond to the user's individual needs and emotional state, thereby improving the effectiveness of dieting.

[2385] 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 means for receiving physical information and goals input by the user, means for using a generative model to generate a meal plan and an exercise plan based on the physical information and goals, means for providing the generated meal plan and exercise plan to the user, means for the user to input a meal and exercise record, means for saving and analyzing the input meal and exercise record, means for updating the meal plan and exercise plan based on the analysis results, means for recognizing the user's emotional state and adjusting the plan based on this, and means for collecting seasonal food information and reflecting this in the meal plan. This enables effective diet support while responding to the user's individual physical information and emotional state.

[2386] "User" refers to an individual who uses the system to input physical information and diet goals and receives meal and exercise plans.

[2387] "Physical information" refers to basic physical information about the user, such as age, gender, height, and weight.

[2388] "Goal" refers to a specific goal for dieting that the user wants to achieve, such as a target weight or a target period.

[2389] "Meal Plan" refers to a specific daily meal plan suggested based on a user's physical information and goals.

[2390] An "exercise plan" refers to a plan that specifically outlines the exercise content for one day, proposed based on the user's physical information and goals.

[2391] "Generative model" refers to an AI model that automatically generates optimal meal and exercise plans based on a user's physical information and goals.

[2392] "Saving" refers to the act of recording and storing input data in a storage device such as a database.

[2393] "Analysis" refers to analyzing stored data and assessing a user's progress and trends.

[2394] "Emotional state" refers to various emotional states that users experience in their daily lives, and specifically includes motivation, stress, happiness, etc.

[2395] "Seasonal food information" refers to information about the freshest and most nutritious food items on the market each season.

[2396] A "healthcare app" refers to application software for managing data related to a user's body and exercise.

[2397] "Adjusting your plan" refers to the process of reviewing and optimizing your existing meal and exercise plans based on your progress and emotional state.

[2398] This invention is a system that effectively supports dieting by proposing optimal meal and exercise plans based on a user's individual physical information and diet goals, and by combining this with an emotion engine that recognizes the user's emotions. This system is composed of a user, a device, a server, a generative AI model, and an emotion engine. The role and operation of each component are explained below.

[2399] Entering user information

[2400] Users access the app using a device such as a smartphone or tablet. They enter basic physical information such as age, gender, height, and weight, as well as their diet goals (e.g., target weight and target period). The entered information is sent from the device to the server.

[2401] Save information and generate plans

[2402] The server receives the user's submitted physical information and goals and stores them in a database. Based on the stored information, a generative AI model generates an optimal diet and exercise plan. This generative AI model uses, for example, OpenAI's GPT model. The generation process is customized based on the user's individual needs.

[2403] Examples:

[2404] For example, if a user is a 35-year-old male with a height of 175 cm and a weight of 80 kg, with a target weight of 70 kg, the generative AI model calculates the user's basal metabolic rate and activity level to determine the required calorie intake, and then suggests specific meal plans (e.g., yogurt and fruit for breakfast, salad and chicken breast for lunch, stir-fried vegetables and fish for dinner) and exercise options (e.g., 30 minutes of jogging three times a week).

[2405] Example prompts for input to a generative AI model:

[2406] User Information:

[2407] Age: 35

[2408] Gender: Male

[2409] Height: 175cm

[2410] Weight: 80kg

[2411] Target weight: 70kg

[2412] Prompts for input to generative AI models:

[2413] Consider your basal metabolic rate and activity level and generate optimal meal and exercise plans based on the following information:

[2414] Age: 35

[2415] Gender: Male

[2416] Height: 175cm

[2417] Weight: 80kg

[2418] Target weight: 70kg

[2419] Introducing the Emotion Engine

[2420] The emotion engine recognizes emotions from user input data and sensor data (e.g., smartwatches and fitness trackers) and stores the data in a database to understand the user's emotional state in real time. The emotion engine uses, for example, Affectiva's emotion recognition engine. The server adjusts the plan based on this.

[2421] Examples:

[2422] If a user's motivation drops while following a plan, the emotion engine will recognize the change and adjust their diet or exercise plan, or provide advice to boost their motivation.

[2423] Presenting the plan

[2424] The meal plan and exercise plan generated by the server are sent to the terminal, which displays the plan to the user in a visually easy-to-understand format. The user then follows the proposed plan to follow their daily diet and exercise routine.

[2425] Food and exercise tracking

[2426] Users record their daily diet and exercise on their devices, which then send the records to a server and store them in a database.

[2427] Examples:

[2428] If a user has yogurt and fruit for breakfast, the device will input the meal details (yogurt, fruit) and calorie information and send it to the server. Similarly, if a user goes jogging, the amount of exercise will be recorded.

[2429] Analyze records and adjust plans

[2430] The server periodically analyzes the stored diet and exercise records to assess the user's progress. If necessary, it re-uses the generative AI model to update the diet and exercise plan. The new plan is again sent to the device and the user is notified.

[2431] Providing information on seasonal ingredients

[2432] The server collects information on seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[2433] Examples:

[2434] During the autumn season, healthy recipes using seasonal ingredients such as sweet potatoes and mushrooms (e.g., sweet potato soup and stir-fried mushrooms) are suggested.

[2435] Linking with Healthcare App

[2436] This system works in conjunction with the user's healthcare app to obtain weight and exercise data. The server receives the data from the healthcare app and stores it in a database. Based on this, the generative AI model performs more precise analysis and generates a plan.

[2437] Maintaining motivation with an emotional engine

[2438] The emotion engine monitors the user's emotional changes in real time and provides feedback and advice according to their emotions. It predicts the user's emotional changes in specific situations and recommends measures to increase motivation in advance.

[2439] Examples:

[2440] If a user is prone to feeling stressed during a particular training session, the emotion engine can predict this in advance and provide advice on how to relax or change the exercise plan.

[2441] This invention allows users to implement individually customized meal and exercise plans and provides support that responds to emotional changes. This also allows for effective dieting and health management, and can continuously increase the user's motivation. Furthermore, by utilizing seasonal ingredients, nutritionally balanced meals are provided according to the season, and the user can continue without getting bored.

[2442] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2443] Step 1:

[2444] The user accesses the app using a device and enters their physical information (e.g., age, gender, height, weight) and diet goals (e.g., target weight and target period). The information entered is then entered into a form and sent to the server by pressing the submit button. User profile data is generated based on the input and passed to the server.

[2445] Specific input example:

[2446] User input information:

[2447] Age: 30

[2448] Gender: Female

[2449] Height: 165cm

[2450] Weight: 70kg

[2451] Target weight: 60kg

[2452] Server input:

[2453] User profile data (age, gender, height, weight, goal weight)

[2454] Server output:

[2455] Profile save confirmation message

[2456] Step 2:

[2457] The server stores the received user information in a database, which is then input into a generative AI model to generate meal and exercise plans.

[2458] Specific input example:

[2459] Server input:

[2460] User profile data (age, gender, height, weight, goal weight)

[2461] Server output:

[2462] Save to database

[2463] Specific behavior:

[2464] The server connects to the database and executes a SQL query to save the user information. After the save is complete, a save success message is generated.

[2465] Step 3:

[2466] The server generates prompt sentences based on the stored user information and sends the prompt sentences to the generative AI model, which then generates optimal meal and exercise plans for the user based on the prompt sentences.

[2467] Specific input example:

[2468] Prompt for the generative AI model:

[2469] User Information:

[2470] Age: 30

[2471] Gender: Female

[2472] Height: 165cm

[2473] Weight: 70kg

[2474] Target weight: 60kg

[2475] Prompts for input to generative AI models:

[2476] Consider your basal metabolic rate and activity level and generate optimal meal and exercise plans based on the following information:

[2477] Age: 30

[2478] Gender: Female

[2479] Height: 165cm

[2480] Weight: 70kg

[2481] Target weight: 60kg

[2482] Server input:

[2483] Prompt statement

[2484] Output of the generative AI model:

[2485] Meal and exercise plans (e.g., yogurt and fruit for breakfast, salad and chicken for lunch, fish and stir-fried vegetables for dinner)

[2486] Specific behavior:

[2487] The server sends the generated prompt text to the generative AI model, which then performs calculations and data analysis to generate and return a meal plan and exercise plan.

[2488] Step 4:

[2489] The server sends the meal and exercise plans derived from the generative AI model to the device, which displays these plans in a user-friendly format.

[2490] Specific input example:

[2491] Server input:

[2492] Meal and exercise plans

[2493] Server output:

[2494] Sending plan data to the device

[2495] Type in the terminal:

[2496] Plan data received on your device

[2497] Terminal output:

[2498] User interface (displaying meal and exercise plans)

[2499] Specific behavior:

[2500] The server transmits the plan data to the terminal, which receives it and displays it on the user interface.

[2501] Step 5:

[2502] Users record their daily diet and exercise on their devices, and the recorded data is sent to a server.

[2503] Specific input example:

[2504] User input:

[2505] Food log (e.g., yogurt and fruit for breakfast)

[2506] Exercise record (e.g. 30 minutes of jogging)

[2507] Type in the terminal:

[2508] Food and exercise record data

[2509] Terminal output:

[2510] Sending recorded data to the server

[2511] Server input:

[2512] Food and exercise record data

[2513] Specific behavior:

[2514] The user enters their diet and exercise record data into the app and presses the save button, sending the data to the server.

[2515] Step 6:

[2516] The server stores the received food and exercise records in a database, and then analyzes this data to assess the user's progress.

[2517] Specific input example:

[2518] Server input:

[2519] Food and exercise record data

[2520] Server output:

[2521] Data saving operations

[2522] Progress Assessment Report

[2523] Specific behavior:

[2524] The server stores the food and exercise records in a database and analyzes them with an analytical algorithm to assess progress.

[2525] Step 7:

[2526] Based on the progress assessment results, the server re-uses the generative AI model to update the meal and exercise plans if necessary, and the new plans are sent to the device and notified to the user.

[2527] Specific input example:

[2528] Server input:

[2529] Progress evaluation results

[2530] Server output:

[2531] Updated meal and exercise plans

[2532] Type in the terminal:

[2533] New plan data

[2534] Terminal output:

[2535] View and notify renewal plans

[2536] Specific behavior:

[2537] The server uses the progress data to generate a new plan using the generative AI model and sends the result back to the device, which updates its user interface to display the new plan.

[2538] Step 8:

[2539] The server collects information on seasonal ingredients from external sources and reflects it in the generative AI model, which then generates recipes using seasonal ingredients and provides them to users.

[2540] Specific input example:

[2541] Server input:

[2542] Seasonal food data from external sources

[2543] Server output:

[2544] Updated Recipe Plans

[2545] Type in the terminal:

[2546] New Recipe Plan

[2547] Terminal output:

[2548] Displaying recipes using seasonal ingredients

[2549] Specific behavior:

[2550] The server retrieves seasonal food data from an external API, applies it to the generative AI model, and generates a new recipe plan. The plan is then sent to the device and displayed to the user.

[2551] Step 9:

[2552] The server periodically obtains weight and exercise data from the user's healthcare app, which is then stored in a database and reflected in the generative AI model.

[2553] Specific input example:

[2554] Server input:

[2555] Weight and exercise data from the Health app

[2556] Server output:

[2557] Updated database

[2558] Specific behavior:

[2559] The server retrieves user data from the healthcare app's API and stores it in a database.

[2560] Step 10:

[2561] The emotion engine recognizes the user's emotional state in real time based on sensor data and input data, and sends the results to the server, which then generates feedback and advice based on that information and provides it to the user.

[2562] Specific input example:

[2563] Emotion Engine Input:

[2564] Sensor data and user emotion input data

[2565] Emotion engine output:

[2566] Emotion analysis results

[2567] Server input:

[2568] Emotion analysis results

[2569] ...

Claims

1. means for receiving user-input physical information and goals; means for generating a meal plan and an exercise plan based on the physical information and goals; means for providing the generated meal and exercise plan to the user; a means for the user to input a diet and exercise log; means for storing and analyzing the entered dietary and exercise records; means for updating the meal plan and exercise plan based on the analysis results; The system includes a means for collecting information on seasonal ingredients and reflecting this information in the meal plan.

2. The system according to claim 1 , further comprising means for linking with a healthcare app of the user and acquiring weight and exercise data of the user from the healthcare app.

3. The system of claim 1 further comprising means for evaluating the user's progress based on the stored records and the analysis results.

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