system

Abstract

We provide the system. [Solution] Means for collecting user health information, A means of calculating calorie targets based on collected health information, A method for generating a week's worth of low-calorie menus, A means of creating a list of necessary ingredients based on the generated menu, A method for automatically ordering ingredients from partner suppliers, A means of delivering the generated recipe to the user's device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Planning and implementing a low-calorie diet menu with a focus on health in daily life is difficult for many people. In particular, calculating calories based on individual health information and goals, devising a menu containing balanced nutrients, and gathering the necessary ingredients are time-consuming. Moreover, to continuously maintain healthy eating habits, these tasks need to be performed regularly, which is even more laborious. As a result, it becomes difficult for modern people with busy lives to practice healthy eating, and problems arise in maintaining health.

Means for Solving the Problems

[0005] This invention provides a system that collects a user's health information and calculates a calorie target based on that information. Furthermore, it generates a week's worth of low-calorie menus and creates a list of necessary ingredients based on the generated menus. It also includes functions for automatically ordering ingredients from partner suppliers and delivering the generated recipes to the user's terminal. This system allows users to easily implement health-conscious low-calorie meal plans, enabling them to maintain their health on a continuous basis. Specifically, it calculates the basal metabolic rate and total calorie expenditure based on the user's exercise level and health goals, and generates a meal plan tailored to individual needs. It also enhances user convenience by providing reminder notifications for expected arrival dates and cooking procedures.

[0006] "User information" refers to personal information such as the user's age, gender, weight, height, exercise level, and health goals.

[0007] "Calorie target" refers to the total amount of calories a user should consume per day, calculated based on the collected user information.

[0008] A "low-calorie menu" is a one-week meal plan that meets your calorie target while maintaining nutritional balance.

[0009] A "food ingredient list" is a list of ingredients required based on a low-calorie menu.

[0010] A "server" is a central computer that collects user information, calculates calorie targets, generates low-calorie menus, creates ingredient lists, orders ingredients, and distributes recipes.

[0011] A "terminal" is a device used by users to input information, view menus and recipes, and receive notifications.

[0012] A "supplier" is a company that provides ingredients in partnership with the system.

[0013] "Placement" is the process by which the system orders ingredients from suppliers based on an ingredient list.

[0014] A "recipe" is a set of instructions that includes the ingredients needed to prepare a low-calorie meal, cooking steps, cooking time, and calorie information.

[0015] A "reminder notification" is a function that informs users of the expected arrival date of ingredients and cooking instructions.

[0016] "Basal metabolic rate" is the amount of energy consumed at rest, calculated based on the user's gender, age, weight, and height.

[0017] "Total calorie expenditure" refers to the total daily energy expenditure calculated based on the user's basal metabolic rate and activity level. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the language used in the following description will be explained.

[0021] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the 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.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0032] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] Basic configuration

[0040] This system collects users' health information, calculates calorie targets based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device, enabling users to easily and consistently practice healthy eating.

[0041] Server-side processing

[0042] 1. User Information Collection

[0043] The user uses their device to input their age, gender, weight, height, exercise level, health goals, etc. The device then sends this information to the server.

[0044] 2. Calculating your calorie target

[0045] The server calculates the basal metabolic rate (BMR) based on the user's health information. For example, it calculates it using the Mifflin-St. Jeor equation as follows:

[0046] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[0047] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[0048] Next, calculate your total daily exercise expenditure (TDEE) based on your activity level. Example:

[0049] Low activity level: TDEE = BMR 1.2

[0050] Moderate exercise level: TDEE = BMR 1.55

[0051] High activity level: TDEE = BMR 1.9

[0052] Finally, set a target calorie intake according to the user's health goals. Example:

[0053] Health goal is weight loss: Target calories = TDEE - 500

[0054] Maintaining health goals: Target calories = TDEE

[0055] Health goal is weight gain: Target calories = TDEE + 500

[0056] 3. Creating low-calorie menus

[0057] The server generates a week's worth of low-calorie menus based on your target calorie intake. The generated menus are designed to include a balanced range of nutrients.

[0058] 4. Create a list of ingredients and place orders with suppliers.

[0059] The server creates a list of necessary ingredients based on the generated menu. It checks the supplier's inventory and automatically places orders for the required ingredients.

[0060] 5. Recipe generation and distribution to users

[0061] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[0062] Terminal-side processing

[0063] 1. Provide a user interface.

[0064] The terminal provides an interface that allows the user to input necessary information. It also has the function to display delivered menus and recipes.

[0065] 2. Notification function

[0066] The terminal displays reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions sent from the server.

[0067] User actions

[0068] 1. Information Entry

[0069] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[0070] 2. Check the menu and recipes.

[0071] The user checks a week's worth of menus and recipes displayed on the device and cooks according to the recipes.

[0072] 3. Receiving the ingredients

[0073] The user receives the ingredients on the scheduled date notified by the server.

[0074] Implementation of specific examples

[0075] Initial setup

[0076] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[0077] Calorie setting

[0078] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[0079] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[0080] The target calorie intake for maintaining weight is 2567.19 kcal.

[0081] Menu Generation

[0082] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[0083] Automated ingredient procurement

[0084] Create a list of necessary ingredients and automatically place orders with suppliers.

[0085] Recipe distribution

[0086] The recipe, based on the completed menu, will be sent to the user's device for review.

[0087] This makes it easy for users to consistently follow a low-calorie, balanced diet.

[0088] The following describes the processing flow.

[0089] Step 1:

[0090] The user accesses the system's registration screen using their device and enters personal information such as age, gender, weight, height, exercise level, and health goals.

[0091] Step 2:

[0092] The device sends the personal information entered by the user to the server.

[0093] Step 3:

[0094] The server registers the received user information in the database.

[0095] Step 4:

[0096] The server calculates the basal metabolic rate (BMR) for each user. The calculation method is based on gender, age, weight, and height.

[0097] Step 5:

[0098] The server calculates total daily exercise expenditure (TDEE) based on the exercise level. The calculation is performed using a coefficient that corresponds to the exercise level.

[0099] Step 6:

[0100] The server sets target calories according to the user's health goals (weight loss, maintenance, or weight gain).

[0101] Step 7:

[0102] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[0103] Step 8:

[0104] The server creates a list of necessary ingredients based on the generated menu.

[0105] Step 9:

[0106] The server checks the inventory information of food suppliers and places orders for the necessary ingredients.

[0107] Step 10:

[0108] The server creates detailed recipes based on a week's worth of menus. The recipes include required ingredients, cooking instructions, cooking time, and calorie information.

[0109] Step 11:

[0110] The server delivers the generated recipe to the user's device.

[0111] Step 12:

[0112] The device displays the received recipes and menus to the user.

[0113] Step 13:

[0114] The device sends reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions.

[0115] Step 14:

[0116] The user cooks according to the recipe displayed on their device.

[0117] Step 15:

[0118] Users will continue to practice healthy eating by receiving new recipes and menus every week.

[0119] (Example 1)

[0120] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0121] Maintaining a healthy diet is not easy in today's busy lifestyle. In particular, calculating appropriate calorie intake based on individual health conditions and lifestyles, and planning meals accordingly, requires expertise, time, and effort, making it a burden for many users. Furthermore, sourcing appropriate ingredients and managing recipes have become significant challenges in daily life. As a result, many users find it difficult to maintain a healthy diet.

[0122] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0123] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus based on the target calories, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from suppliers, and means for delivering the generated recipes to the user's terminal. This makes it possible for the user to easily plan and practice appropriate calorie intake according to their health condition, and to continuously maintain a balanced diet.

[0124] "Means of collecting user health information" refers to devices, systems, or software that allow users to input and collect information such as their age, gender, weight, height, exercise level, and health goals.

[0125] "Means for calculating calorie targets" refers to a device, system, or software that calculates a user's basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on collected user health information, and determines an appropriate calorie target based on these.

[0126] "Means for generating a week's worth of low-calorie meals" refers to a device, system, or software that automatically generates a week's worth of meal plans containing balanced nutrients based on a set calorie target.

[0127] "Means for creating an ingredient list" refers to a device, system, or software that lists the necessary ingredients and their quantities based on a generated menu.

[0128] "Means of automatically ordering ingredients from suppliers" refers to a device, system, or software that checks the inventory information of partner suppliers based on a created ingredient list and automatically orders the necessary ingredients.

[0129] "Means for delivering generated recipes to the user's terminal" refers to a device, system, or software that sends a recipe to the user's terminal, including necessary ingredients, cooking procedures, cooking time, and calorie information, based on a generated week's worth of menus.

[0130] "Means for calculating basal metabolic rate and total calorie expenditure based on exercise level and health goals" refers to a device, system, or software for calculating basal metabolic rate (BMR) and total calorie expenditure (TDEE) based on a user's exercise level and health goals.

[0131] "Means including necessary ingredients, cooking procedures, cooking time, and calorie information" refers to a device, system, or software that manages and provides the ingredients required for a generated recipe, specific cooking procedures, cooking time, and calorie information for each recipe.

[0132] This invention is a system that automatically collects a user's health information, calculates their calorie target, and generates a low-calorie menu based on that target. Furthermore, it automatically orders the necessary ingredients from suppliers and delivers the generated recipe to the user's terminal, making it easy for the user to practice healthy eating.

[0133] Server-side functionality

[0134] User information collection

[0135] The server receives health information such as age, gender, weight, height, exercise level, and health goals entered by the user on the device and stores it in a database. To do this, the server uses the HTTPS protocol to securely receive data from the device.

[0136] Calculating calorie targets

[0137] The server calculates the basal metabolic rate (BMR) based on the received health information. Specifically, it uses the Mifflin-St. Jeor equation and calculates it as follows:

[0138] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[0139] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[0140] Furthermore, the total daily exercise expenditure (TDEE) is calculated based on the exercise level. For example, if the exercise level is "moderate," it is calculated as follows:

[0141] TDEE = BMR 1.55

[0142] Finally, a target calorie intake is set based on the user's health goals. For example, if the goal is to maintain weight, the target calorie intake would be TDEE (Total Daily Excess).

[0143] Creating low-calorie menus

[0144] The server uses a generative AI model to generate a week's worth of low-calorie menus based on target calories. An example of a specific prompt message is as follows:

[0145] "Please generate a one-week low-calorie menu for a 30-year-old male, weighing 70kg, 175cm tall, with a moderate fitness level, and a target calorie intake of 2567.19 kcal."

[0146] The generated menu contains a balanced set of nutrients.

[0147] Creating a list of ingredients and placing orders with suppliers.

[0148] The server creates a list of necessary ingredients based on the generated menu. Then, it uses the supplier's API to check inventory information and automatically places orders for the ingredients.

[0149] Recipe generation and distribution to users

[0150] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[0151] Device-side functions

[0152] User interface provision

[0153] The device provides an interface for users to input information. It displays fields for entering age, gender, weight, height, exercise level, and health goals in a form. It also has the functionality to display delivered menus and recipes.

[0154] Notification function

[0155] The device notifies the user via pop-up or push notifications about the expected arrival date of ingredients sent from the server and reminders about cooking instructions.

[0156] User behavior

[0157] Information entry

[0158] The user launches the terminal app and follows the instructions to enter their age, gender, weight, height, exercise level, and health goals. After reviewing the entered information, they press the submit button to send the information to the server.

[0159] Check the menu and recipes.

[0160] The user checks a week's worth of menus and recipes displayed on their device. They then cook according to the recipes, checking the necessary ingredients and cooking steps.

[0161] Receiving groceries

[0162] The user checks the expected arrival date of the ingredients notified by the supplier and receives the ingredients on that day. They then check the received ingredients and cook according to the recipe.

[0163] This system allows users to easily plan and implement appropriate calorie intake tailored to their health condition, enabling them to maintain a balanced diet on a regular basis.

[0164] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0165] Step 1: Enter and collect user information

[0166] Specific user actions: The user launches the app on their device and follows the instructions to enter their age, gender, weight, height, exercise level, and health goals.

[0167] Specific terminal operation: The terminal stores the entered information in temporary memory and prepares to send it to the server.

[0168] Input: User's age, gender, weight, height, fitness level, health goals

[0169] Output: Data packets containing collected user information

[0170] Step 2: Receiving and saving user information

[0171] Specific server operation: The server receives user information data packets sent from the terminal and stores that information in the database.

[0172] Input: Data packet containing user information

[0173] Output: User information stored in the database

[0174] Step 3: Calculating Basal Metabolic Rate (BMR)

[0175] Specific server operation: The server calculates the basal metabolic rate (BMR) from the stored user information. For example, it uses the Mifflin-St. Jeor equation.

[0176] The inputs used are weight, height, age, and gender.

[0177] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[0178] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[0179] Input: Weight, height, age, gender

[0180] Output: Calculated basal metabolic rate (BMR)

[0181] Step 4: Calculate Total Daily Expenditure (TDEE)

[0182] Specific server operation: The server calculates total daily exercise expenditure (TDEE) based on the exercise level. For example, if the exercise level is "moderate," it calculates as follows:

[0183] TDEE = BMR 1.55 (for moderate activity level)

[0184] Input: Basal metabolic rate (BMR), exercise level

[0185] Output: Calculated Total Daily Expenditure (TDEE)

[0186] Step 5: Setting a target calorie intake

[0187] Specific server operation: The server sets a target calorie intake based on the user's health goals. For example, if the health goal is weight maintenance, the target calorie intake will be TDEE (Total Daily Excess).

[0188] Input: Total Daily Expenditure (TDEE), Health Goals

[0189] Output: Set target calories

[0190] Step 6: Creating a low-calorie menu

[0191] Server operation: The server uses a generative AI model to generate a week's worth of low-calorie menus based on target calories. Enter an example of a prompt message.

[0192] Example prompt: "Generate a one-week low-calorie menu for a 30-year-old male, weighing 70kg, 175cm tall, with a moderate fitness level and a target calorie intake of 2567.19 kcal."

[0193] Input: Target calories, prompt text

[0194] Output: Generated low-calorie menu for one week

[0195] Step 7: Create and order the ingredients list.

[0196] Specific server operation: Based on the generated menu, the server creates a list of necessary ingredients, checks the supplier's inventory information, and automatically places orders.

[0197] Input: Generated low-calorie menu for one week

[0198] Output: List of required ingredients, ordering information for suppliers

[0199] Step 8: Recipe generation and distribution

[0200] Specific server operation: The server generates recipes based on a week's worth of menus and delivers them to the user's terminal. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[0201] Input: Weekly menu

[0202] Output: Recipe delivered to the device

[0203] (Application Example 1)

[0204] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0205] Many people today find it difficult to plan and maintain healthy eating habits amidst their busy lifestyles. Furthermore, creating specific menus for proper calorie management and maintaining good health is challenging. In particular, the effort required to gather all necessary ingredients at once and maintaining a balanced meal plan are significant obstacles. Therefore, there is a need for a system that calculates calorie targets based on the user's health information and continuously provides low-calorie, balanced menus.

[0206] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0207] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from partner suppliers, means for delivering the generated recipes to the user's terminal, and means for calculating the basal metabolic rate, total calorie expenditure, and target calorie intake based on the user's information, generating a week's worth of low-calorie menus based on these, automatically ordering the necessary ingredients, and delivering the generated recipes to the smartphone. This makes it possible for the user to easily and consistently practice a healthy diet.

[0208] "User health information" refers to information necessary to understand the user's health status, such as age, gender, weight, height, exercise level, and health goals.

[0209] A "calorie target" refers to the amount of calories a user should consume per day, calculated based on their health information.

[0210] A "low-calorie menu" refers to a one-week meal plan that is calculated based on a calorie target and includes a balanced range of nutrients.

[0211] A "food ingredient list" refers to a list of ingredients needed based on the generated low-calorie menu.

[0212] A "supplier" refers to a company that provides the necessary ingredients and works in conjunction with the server to automatically supply those ingredients to the user.

[0213] "Automatic ordering" refers to the system automatically placing orders for necessary ingredients with suppliers without any user intervention.

[0214] A "recipe" refers to a cooking method based on a generated low-calorie menu, including the necessary ingredients, cooking steps, cooking time, and calorie information.

[0215] "Device" refers to information devices used by users, such as smartphones, tablets, and personal computers.

[0216] "Basal metabolic rate (BMR)" refers to a value that calculates the amount of energy a user needs while at rest.

[0217] "Total Daily Energy Expenditure (TDEE)" refers to the total amount of energy needed per day, calculated by taking into account the basal metabolic rate and the level of exercise.

[0218] "Delivering to smartphones" refers to sending generated recipes, menus, and other information from the server to the user's smartphone for display.

[0219] Basic configuration

[0220] This system collects users' health information, calculates calorie targets based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device, enabling users to easily and consistently practice healthy eating.

[0221] Server-side processing

[0222] 1. User Information Collection

[0223] The server receives information such as age, gender, weight, height, exercise level, and health goals entered by the user via their device. This information is necessary to accurately understand the user's health status.

[0224] 2. Calculating your calorie target

[0225] The server calculates the basal metabolic rate (BMR) based on the user's health information. For example, it calculates it using the Mifflin-St. Jeor equation as follows:

[0226] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[0227] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[0228] Next, calculate your total daily exercise expenditure (TDEE) based on your activity level. For example, if your activity level is moderate:

[0229] TDEE = BMR 1.55

[0230] Finally, set a target calorie intake according to the user's health goals. For example, if the goal is to maintain weight:

[0231] Target Calories = TDEE

[0232] 3. Creating low-calorie menus

[0233] The server generates a week's worth of low-calorie menus based on your target calorie intake. The generated menus are designed to include a balanced range of nutrients.

[0234] 4. Create ingredient list and automate ordering.

[0235] The server creates a list of necessary ingredients based on the generated menu. It checks the supplier's inventory and automatically orders the necessary ingredients via API.

[0236] 5. Recipe generation and distribution to users

[0237] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include the necessary ingredients, cooking instructions, cooking time, and calorie information.

[0238] Terminal-side processing

[0239] 1. Provide a user interface.

[0240] The terminal provides an interface that allows the user to input necessary information. It also has the function to display delivered menus and recipes.

[0241] 2. Notification function

[0242] The terminal displays reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions sent from the server.

[0243] User actions

[0244] 1. Information Entry

[0245] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[0246] 2. Check the menu and recipes.

[0247] The user checks a week's worth of menus and recipes displayed on the device and cooks according to the recipes.

[0248] 3. Receiving the ingredients

[0249] The user receives the ingredients on the scheduled date notified by the server.

[0250] Implementation of specific examples

[0251] Initial setup

[0252] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[0253] Calorie setting

[0254] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[0255] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[0256] The target calorie intake for maintaining weight is 2567.19 kcal.

[0257] Menu Generation

[0258] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[0259] Automated ingredient procurement

[0260] Create a list of necessary ingredients and automatically place orders via API.

[0261] Recipe distribution

[0262] The recipe, based on the completed menu, will be sent to the user's device for review.

[0263] Example of a prompt:

[0264] "Development of a food delivery app that calculates basal metabolic rate, total calorie expenditure, and target calorie intake based on user information, generates a week's worth of low-calorie menus based on these calculations, automatically orders the necessary ingredients, and delivers the generated recipes to the user's smartphone."

[0265] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0266] Step 1: Enter and collect user information

[0267] Users use a device to input health information such as their age, gender, weight, height, exercise level, and health goals. The device then sends this input information to a server. This input information serves as the basis for calculating calorie goals and is used to monitor one's health status.

[0268] Step 2: Calculating Basal Metabolic Rate (BMR)

[0269] The server calculates each user's basal metabolic rate (BMR) based on the user's health information received in Step 1. Specifically, it calculates the BMR using the Mifflin-St. Jeor equation. For example, for a male:

[0270] Input: weight, height, age, gender

[0271] Data calculation: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[0272] Output: BMR value

[0273] Step 3: Calculate Total Daily Expenditure (TDEE)

[0274] The server calculates the total daily exercise expenditure (TDEE) by adding the exercise level to the basal metabolic rate (BMR) calculated in step 2. For example, if the exercise level is moderate:

[0275] Input: Basal metabolic rate, exercise level

[0276] Data calculation: TDEE = BMR 1.55

[0277] Output: TDEE value

[0278] Step 4: Setting a target calorie intake

[0279] The server sets a target calorie intake based on the user's health goals (weight loss, maintenance, weight gain, etc.). For example, if the goal is weight maintenance:

[0280] Input: Total calories burned, health goal

[0281] Data calculation: Target calories = TDEE

[0282] Output: Target calorie value

[0283] Step 5: Generation of low-calorie menu

[0284] The server generates a one-week low-calorie menu based on the target calories obtained in Step 4. The generated menu is designed to include balanced nutrients. Specifically, appropriate recipes are selected from the database to compose the menu.

[0285] Input: Target calories

[0286] Data calculation: Execute the menu optimization algorithm

[0287] Output: One-week low-calorie menu

[0288] Step 6: Creation of ingredient list and automatic ordering

[0289] The server creates a list of required ingredients based on the menu generated in Step 5. Then, it automatically orders the ingredients from the partnering suppliers through the API.

[0290] Input: Low-calorie menu

[0291] Data calculation: Create ingredient list, send request to supplier API

[0292] Output: Ingredient list, confirmation of automatic ordering

[0293] Step 7: Generation and distribution of recipes

[0294] The server generates detailed recipes based on the one-week menu. The generated recipes are distributed to the user's terminal, and the user can check the required ingredients, cooking procedures, cooking time, calorie information, etc.

[0295] Input: Low-calorie menu

[0296] Data processing: Execute the recipe generation algorithm.

[0297] Output: Recipe delivery to user terminals

[0298] Example of a prompt:

[0299] "Development of a food delivery app that calculates basal metabolic rate, total calorie expenditure, and target calorie intake based on user information, generates a week's worth of low-calorie menus based on these calculations, automatically orders the necessary ingredients, and delivers the generated recipes to the user's smartphone."

[0300] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0301] Basic configuration

[0302] This system collects the user's health information, calculates a calorie target based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device. Furthermore, it incorporates an emotion engine that recognizes the user's emotions, adjusting the menus and recipes based on those emotions.

[0303] Server-side processing

[0304] 1. User Information Collection

[0305] The user uses their device to input their age, gender, weight, height, exercise level, and health goals. The device then sends this information to the server.

[0306] 2. Calculating your calorie target

[0307] The server calculates the basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on the user's health information, and sets the target calories based on the health goals.

[0308] 3. Generation of low-calorie menu

[0309] The server generates a one-week low-calorie menu based on the target calories, taking into account nutritional balance and meal variations.

[0310] 4. Creation of ingredient list and ordering from suppliers

[0311] The server creates a list of required ingredients based on the generated menu and automatically places an order with the suppliers.

[0312] 5. Recipe generation and delivery to users

[0313] The server creates detailed recipes based on the one-week menu and delivers them to the user's terminal. The recipes include the required ingredients, cooking procedures, cooking time, and calorie information.

[0314] 6. Emotion recognition by emotion engine

[0315] The user inputs emotions using the terminal. The emotion engine recognizes this and records the user's current emotional state.

[0316] 7. Adjustment of menu and recipe

[0317] The server adjusts the menu and recipe based on the recognized emotions of the user. For example, for a user feeling stressed, it proposes a menu using ingredients with a relaxing effect.

[0318] 8. Utilization of past emotion data

[0319] The server analyzes past sentiment data to optimize the menu for the following week. It learns the user's sentiment patterns and provides the most suitable menu and recipes.

[0320] Terminal-side processing

[0321] 1. Provide a user interface.

[0322] The device provides an interface that allows users to input necessary information. It also has functions for receiving delivered menus and recipes, and for inputting emotions.

[0323] 2. Notification function

[0324] The terminal displays reminder notifications from the server regarding the expected arrival date of ingredients and cooking instructions, as well as requests for emotional input.

[0325] User actions

[0326] 1. Information Entry

[0327] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[0328] 2. Emotional Input

[0329] Users periodically input their emotional state into the device. The emotion engine recognizes this data and sends it to the server.

[0330] 3. Check the menu and recipes.

[0331] The user checks the menu and recipes for the week displayed on the device and cooks according to the recipes.

[0332] 4. Receiving the ingredients

[0333] The user receives the ingredients on the scheduled date notified by the server.

[0334] 5. Receive and adjust next week's menu.

[0335] At the end of each week, users receive a menu optimized for the following week based on their emotional data.

[0336] Implementation of specific examples

[0337] Initial setup

[0338] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[0339] Calorie setting

[0340] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[0341] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[0342] The target calorie intake for maintaining weight is 2567.19 kcal.

[0343] Menu Generation

[0344] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[0345] Emotional input and adjustment

[0346] If a user is experiencing work-related stress, the emotion engine recognizes this and provides a menu containing ingredients effective in reducing stress. Furthermore, the menu for the following week is optimized based on the user's emotional history.

[0347] Automated ingredient procurement

[0348] Create a list of necessary ingredients and automatically place orders with suppliers.

[0349] Recipe distribution

[0350] The recipe, based on the completed menu, will be sent to the user's device for review.

[0351] In this way, users can easily implement a consistent low-calorie diet based on their health information and emotional state.

[0352] The following describes the processing flow.

[0353] Step 1:

[0354] The user accesses the system's registration screen using their device and enters personal information such as age, gender, weight, height, exercise level, and health goals.

[0355] Step 2:

[0356] The terminal sends the user's entered personal information to the server.

[0357] Step 3:

[0358] The server registers the received user information in the database.

[0359] Step 4:

[0360] The server calculates the basal metabolic rate (BMR) for each user. The calculation method is based on gender, age, weight, and height.

[0361] Step 5:

[0362] The server calculates total daily exercise expenditure (TDEE) based on the exercise level. The calculation is performed using a coefficient that corresponds to the exercise level.

[0363] Step 6:

[0364] The server sets target calories according to the user's health goals (weight loss, maintenance, or weight gain).

[0365] Step 7:

[0366] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[0367] Step 8:

[0368] The server creates a list of necessary ingredients based on the generated menu.

[0369] Step 9:

[0370] The server checks the inventory information of food suppliers and places orders for the necessary ingredients.

[0371] Step 10:

[0372] The server creates detailed recipes based on a week's worth of menus. The recipes include required ingredients, cooking instructions, cooking time, and calorie information.

[0373] Step 11:

[0374] The server delivers the generated recipe to the user's device.

[0375] Step 12:

[0376] The device displays the received recipes and menus to the user.

[0377] Step 13:

[0378] The device sends reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions.

[0379] Step 14:

[0380] The user cooks according to the recipe displayed on their device.

[0381] Step 15:

[0382] Users periodically input their emotional state into the device. For example, their emotional state might be "feeling stressed" or "feeling relaxed."

[0383] Step 16:

[0384] The device sends the entered emotional state information to the server.

[0385] Step 17:

[0386] The server uses an emotion engine to analyze and record the user's emotional state.

[0387] Step 18:

[0388] The server adjusts menus and recipes based on the user's emotional state. For example, if a user is feeling stressed, it will offer a menu using ingredients that have a relaxing effect.

[0389] Step 19:

[0390] The server analyzes past sentiment data to optimize the menu for the following week. It learns the user's sentiment patterns and provides the most suitable menu and recipes.

[0391] Step 20:

[0392] The server delivers the next week's menu and adjusted recipes to the user's device.

[0393] Step 21:

[0394] Users receive new menus and recipes every week, encouraging them to consistently practice healthy eating.

[0395] (Example 2)

[0396] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0397] In modern society, individual health management is a crucial issue, and improving dietary habits is particularly essential for maintaining and promoting health. However, it is difficult for many people to design and follow an optimal meal plan based on their own health condition. Furthermore, preparing the necessary ingredients and implementing appropriate recipes in a busy daily life requires considerable time and effort. Moreover, the influence of a user's emotional state on food choices cannot be ignored. This invention aims to solve these problems and provide individual users with an effective and easy-to-use means of health management.

[0398] The identification processing performed by the identification 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 collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from suppliers, means for delivering the generated recipes to the user's terminal, and means for collecting emotional information and adjusting the menus and recipes based on it. As a result, the user can easily obtain meal menus optimized for their individual health condition and efficiently prepare the necessary ingredients and execute the recipes. In addition, the provision of appropriate menus according to the user's emotional state is realized, promoting comprehensive health management.

[0399] "User health information" refers to information about the user's health status and lifestyle, such as the user's age, gender, weight, height, exercise level, and health goals.

[0400] A "calorie target" is the appropriate amount of energy a user should consume per day to achieve their health goals.

[0401] A "low-calorie menu" is a balanced meal plan for one week, tailored to the user's calorie goals.

[0402] The "list of required ingredients" is a list of ingredients needed to execute a given menu, based on the generated menu.

[0403] A "supplier" is a company or organization that is responsible for providing users with the necessary ingredients.

[0404] A "recipe" is information that describes the detailed steps and necessary ingredients for cooking a specific dish.

[0405] "Emotional information" refers to data that represents the user's emotional state, and this is used to adjust menus and recipes.

[0406] An "emotion engine" is a system that recognizes the user's emotional input and determines their current emotional state.

[0407] Basal metabolic rate (BMR) is an indicator that shows the amount of energy a user consumes while at rest.

[0408] Total Daily Energy Expenditure (TDEE) is an indicator that shows the total amount of energy a user consumes in a day, and is calculated by adding the energy consumed through exercise and daily activities to the basal metabolic rate.

[0409] A "generative AI model" is an artificial intelligence model that automatically generates menus and recipes based on user input.

[0410] This invention is a system that calculates a calorie target based on the user's health and emotional information, and generates and delivers a week's worth of low-calorie menus. The following hardware and software are used to implement this invention.

[0411] The server and terminals exchange data via the internet. The server runs on the Linux® operating system and executes Python programs. The server uses MySQL® as its database and utilizes common AI models for generating AI models.

[0412] Users input health and emotional information using their devices (e.g., smartphones, tablets, PCs). The device software is developed using the React Native framework and provides notification functionality using Firebase Cloud Messaging.

[0413] The server receives health information sent by the user and calculates the calorie target using the Python NumPy library. For example, for a 30-year-old male, weighing 70kg, 175cm tall, and with an exercise level of "medium," the calculation would be as follows:

[0414] Basal metabolic rate (BMR): 10 70 + 6.25 175 - 5 30 + 5 = 1656.25 kcal

[0415] Total Daily Energy Expenditure (TDEE): BMR 1.55 = 1656.25 1.55 ≈ 2567.19 kcal

[0416] After calculation, the server uses a generating AI model (e.g., OpenAI's GPT-3) to send a prompt message like the following to generate a week's worth of low-calorie menus:

[0417] "The user's target calorie intake is 2567 kcal. Please generate a balanced 1-week meal plan."

[0418] Based on the generated menu, the server uses the Pandas library to create a list of necessary ingredients and automatically places orders for them. It uses the AWS API to place orders with suppliers and notifies the terminal of the order confirmation.

[0419] Detailed recipes are generated by sending the following prompt to the generative AI model:

[0420] "Please generate a detailed recipe based on this menu."

[0421] The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information, which is sent to the device in JSON format and displayed on the user interface.

[0422] Furthermore, when emotional information is entered by the user, the server analyzes it using Microsoft® Azure® Emotion API and saves the results to a database. This system adjusts menus and recipes based on this emotional information. If the user enters that they are feeling stressed, the server instructs the generating AI model to "adjust the menu to one with a relaxing effect."

[0423] Based on past sentiment data, the server optimizes the menu for the following week. The server analyzes past sentiment data using Scikit-learn's machine learning algorithm and prompts the generative AI model to "generate the optimal menu based on the user's sentiment patterns."

[0424] In this way, the system can automatically suggest and implement the optimal meal plan based on the user's health and emotional information.

[0425] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0426] Step 1:

[0427] Users input health information using their devices. Specifically, they enter their age, gender, weight, height, exercise level, and health goals on an input screen. The entered data is sent to the server in JSON format.

[0428] Input: Data entered by the user, including age, gender, weight, height, exercise level, and health goals.

[0429] Output: User health information data in JSON format is sent to the server.

[0430] Step 2:

[0431] The server uses the NumPy library in Python to calculate the calorie target based on the received health information data.

[0432] Input: Health information data in JSON format submitted in Step 1.

[0433] Output: Basal metabolic rate (BMR) and total daily energy expenditure (TDEE) values.

[0434] As a concrete example, perform the following calculation:

[0435] Basal metabolic rate (BMR): 10 Weight (kg) + 6.25 Height (cm) - 5 Age (years) + 5 (for males)

[0436] Total Daily Expenditure (TDEE): BMR (Body Mass Index)

[0437] Step 3:

[0438] The server uses a generative AI model to send the following prompt to generate a week's worth of low-calorie menus: "The user's target calorie intake is 2567 kcal. Please generate a balanced week's worth of menus."

[0439] Input: Basal metabolic rate (BMR) and total daily energy expenditure (TDEE).

[0440] Output: A week's worth of low-calorie menus generated.

[0441] As a concrete example, a prompt message is sent to a generative AI model, and the generated text-formatted menu is converted to JSON format and saved.

[0442] Step 4:

[0443] The server creates a list of required ingredients based on the generated menu. It uses the Pandas library to generate a dataframe and list the required ingredients.

[0444] Input: A week's worth of low-calorie menus generated by a generative AI model.

[0445] Output: List of required ingredients.

[0446] As a concrete example, the ingredient information extracted from the menu is listed and saved in JSON format.

[0447] Step 5:

[0448] The server uses the AWS API to automatically place orders with suppliers based on the required ingredient list. It then notifies the terminal of the order confirmation.

[0449] Input: List of required ingredients.

[0450] Output: Order confirmation notification to the supplier.

[0451] As a concrete example, a POST request is sent to an AWS API endpoint, and an acknowledgment is received from the supplier.

[0452] Step 6:

[0453] The server sends a prompt to the generating AI model to generate a detailed recipe: "Generate a detailed recipe based on this menu."

[0454] Input: A week's worth of low-calorie menus.

[0455] Output: Detailed recipe.

[0456] The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information, and is sent to the device in JSON format.

[0457] Step 7:

[0458] The user inputs emotional information through their device. They select their current emotional state on the emotional input screen, and the data is sent to the server.

[0459] Input: User sentiment information.

[0460] Output: Sentiment information data in JSON format is sent to the server.

[0461] Step 8:

[0462] The server analyzes emotional information using the Microsoft Azure Emotion API and stores the results in a database.

[0463] Input: Sentiment information data in JSON format.

[0464] Output: Analyzed emotional state data.

[0465] As a concrete example, emotion data is sent to the Emotion API, the analysis results are obtained, and the data is saved in a database.

[0466] Step 9:

[0467] The server sends a prompt to the AI ​​model based on past sentiment data to optimize the menu for the following week: "Generate the optimal menu based on the user's sentiment patterns."

[0468] Input: Analyzed historical sentiment data.

[0469] Output: Optimized menu for next week.

[0470] As a concrete example, past emotional patterns are analyzed using Scikit-learn, and an optimized menu is generated by sending prompt sentences to a generative AI model.

[0471] (Application Example 2)

[0472] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0473] Conventional meal management systems can calculate calories and generate menus based on a user's health information, but they cannot manage meals while considering the user's emotional state. Therefore, it was difficult to provide appropriate menus when users were experiencing stress or fatigue. Furthermore, the lack of a mechanism to optimize meals based on emotional state made it difficult to improve the user's mental satisfaction. Therefore, the present invention aims to provide a system that integrates the management of a user's health information and emotional state to provide more individualized and optimal meal menus.

[0474] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0475] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, and means for generating a week's worth of low-calorie menus. To this end, it further includes means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from partner suppliers, means for delivering the generated recipes to the user's terminal, means for collecting the user's emotional state and adjusting the menus based on this, and means for optimizing the menus for the following week using past emotional data. This makes it possible to provide personalized and optimal meal menus that take the user's emotional state into consideration.

[0476] "User health information" refers to information related to an individual's health status and lifestyle, such as the user's age, gender, weight, height, exercise level, and health goals.

[0477] A "calorie target" refers to the appropriate amount of calories a user should consume daily to achieve a specific health goal, based on their basal metabolic rate and total calorie expenditure.

[0478] A "low-calorie menu" refers to a set of meal plans designed to help users meet their health goals while keeping their calorie intake low.

[0479] A "food ingredient list" refers to a list of specific ingredients needed for cooking, based on the generated menu.

[0480] "Supplier" refers to a business or service that sells or provides food ingredients that users need.

[0481] "Automated ordering" refers to the process by which a system automatically places orders for ingredients with suppliers.

[0482] A "recipe" is a document that describes the detailed steps for preparing a specific dish, including ingredients, cooking instructions, cooking time, and calorie information.

[0483] "Emotional state" refers to the emotions and psychological state that a user is experiencing. For example, it can refer to states such as stress, fatigue, or happiness.

[0484] "Menu adjustment" refers to the process of optimizing or modifying existing menus, taking into account the user's current emotional state.

[0485] "Emotional data" refers to historical information about emotional states collected through user input or emotion recognition systems.

[0486] Modes for carrying out the invention

[0487] Basic configuration

[0488] This system automatically generates and manages meal plans based on the user's health information and emotional state. Specifically, it collects and analyzes the user's health information, sets calorie targets, generates menus, automatically orders ingredients, and adjusts menus to take the user's emotional state into consideration, thereby providing a system that meets the individual needs of the user.

[0489] Server-side processing

[0490] 1. User Information Collection

[0491] Users use their devices to input health information such as their age, gender, weight, height, exercise level, and health goals. This information is transmitted to the server in real time and stored in a database.

[0492] 2. Calculating your calorie target

[0493] The server calculates the basal metabolic rate (BMR) and total daily allowance (TDEE) based on the received health information, and sets individual calorie targets. By using Python and Flask for these calculations, highly accurate results are provided in real time.

[0494] 3. Creating low-calorie menus

[0495] Using a generative AI model, a week's worth of low-calorie menus are generated based on calculated calorie targets. The generated menus take into account nutritional balance and variety of ingredients, contributing to the user's health maintenance.

[0496] 4. Create a list of ingredients and place orders with suppliers.

[0497] The server creates a list of necessary ingredients based on the generated menu. Based on this list, it automatically places orders with partner suppliers. The ingredient list is sent in JSON format, and suppliers procure and deliver the ingredients accordingly.

[0498] 5. Recipe generation and distribution to users

[0499] The server creates a detailed recipe based on the menu and delivers it to the user's device. This recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information. The user then cooks according to the recipe.

[0500] 6. Emotion recognition by an emotion engine

[0501] Users periodically input their emotional state into their device. The emotion engine recognizes this data and sends it to the server. Based on this information, the server stores the user's emotional state in a database and performs analysis.

[0502] 7. Adjusting the menu and recipes

[0503] The server adjusts menus and recipes based on the recognized emotional state. For example, if a user is feeling stressed, it suggests a menu using ingredients with relaxing properties. Python and Flask are used to analyze emotional data and adjust menus in real time.

[0504] 8. Utilizing past emotional data

[0505] The server analyzes past emotional data and learns the user's emotional patterns to optimize the menu for the following week. It uses a generative AI model to provide menus based on emotional patterns.

[0506] Terminal-side processing

[0507] 1. Provide a user interface.

[0508] The device provides an interface that allows users to input necessary information. It also has functions for receiving delivered menus and recipes, and for inputting emotions.

[0509] 2. Notification function

[0510] The terminal displays reminder notifications from the server regarding the expected arrival date of ingredients and cooking instructions, as well as requests for emotional input.

[0511] Implementation of specific examples

[0512] Initial setup

[0513] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[0514] Calorie setting

[0515] The basal metabolic rate (BMR) is 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal. The total daily allowance (TDEE) is BMR 1.55 = 1656.25 / 1.55 ​​≈ 2567.19 kcal. The target calorie intake for weight maintenance is 2567.19 kcal.

[0516] Menu Generation

[0517] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[0518] Emotional input and adjustment

[0519] If a user is experiencing work-related stress, the emotion engine recognizes this and provides a menu containing ingredients effective in reducing stress. Furthermore, the menu for the following week is optimized based on the user's emotional history.

[0520] Example of a prompt

[0521] When generating recipes or menus using a generative AI model, use the following prompts.

[0522] The user is 30 years old, male, weighs 70kg, and is 175cm tall. Their exercise level is moderate, and their goal is to maintain their weight. Please calculate calories and generate a one-week low-calorie meal plan for this user. Also, considering the user's stress levels, please include ingredients that are effective in reducing stress in the meal plan.

[0523] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0524] Step 1:

[0525] Collection of user information

[0526] The terminal displays an interface for the user to enter their health information (age, gender, weight, height, exercise level, and health goals). Once the user enters this information and presses the submit button, the terminal sends the entered information to the server. The entered data is sent to the server in JSON format, and the server stores it in its database.

[0527] Step 2:

[0528] Calculating calorie targets

[0529] The server calculates the basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on the user information received. Specifically, the BMR is calculated using the following formula: BMR = 10 / weight (kg) + 6.25 / height (cm) - 5 / age (years) + gender (5 for males, -161 for females). Next, the TDEE is calculated by multiplying the BMR by the exercise level. After calculation, the server stores the daily target calories in the database.

[0530] Step 3:

[0531] Creating low-calorie menus

[0532] The server uses a generative AI model to generate a week's worth of low-calorie menus based on a set calorie goal. The generative AI model is given the following prompt as input: "The user is 30 years old, male, weighs 70 kg, and is 175 cm tall. Their exercise level is moderate, and their goal is to maintain their weight. For this user, please calculate calories and generate a week's worth of low-calorie menus." Based on the prompt, the generative AI model generates the menus and outputs them to the server. The server saves the generated menus to its database.

[0533] Step 4:

[0534] Creating ingredient lists and automated ordering.

[0535] The server creates a list of necessary ingredients based on the generated menu. Specifically, it analyzes each menu item and lists the required ingredients and their quantities. This list is generated in JSON format, and the server automatically places an order with its partner suppliers. The order data is sent to the supplier's API.

[0536] Step 5:

[0537] Recipe generation and distribution

[0538] The server creates a detailed recipe based on the menu. The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information. This is then delivered to the user's device. The device provides an interface for the user to view the recipe and sends reminders via notifications.

[0539] Step 6:

[0540] Emotional input and recognition

[0541] Users periodically input their emotional state using a device. The entered emotional data is sent from the device to the server. The server analyzes this data using an emotion engine and stores the user's current emotional state in a database.

[0542] Step 7:

[0543] Adjustments to the menu and recipes

[0544] The server adjusts menus and recipes based on the recognized user's emotions. For example, if the user is feeling stressed, it will suggest a menu using ingredients that have a relaxing effect. The server uses a generative AI model to generate new menus and delivers them to the user's device.

[0545] Step 8:

[0546] Utilizing past emotional data

[0547] The server analyzes past emotional data and learns the user's emotional patterns. It then makes suggestions that take these emotional patterns into account for future menu optimization. The server saves the generated optimized menu to a database and delivers it to the user's device.

[0548] In this way, a series of processes involving the server, terminal, and user provides a personalized meal menu based on the user's health information and emotional state.

[0549] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0550] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0551] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0552] [Second Embodiment]

[0553] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0554] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0555] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0556] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0557] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0558] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0559] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0560] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0561] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0562] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0563] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0564] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0565] Basic configuration

[0566] This system collects users' health information, calculates calorie targets based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device, enabling users to easily and consistently practice healthy eating.

[0567] Server-side processing

[0568] 1. User Information Collection

[0569] The user uses their device to input their age, gender, weight, height, exercise level, health goals, etc. The device then sends this information to the server.

[0570] 2. Calculating your calorie target

[0571] The server calculates the basal metabolic rate (BMR) based on the user's health information. For example, it calculates it using the Mifflin-St. Jeor equation as follows:

[0572] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[0573] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[0574] Next, calculate your total daily exercise expenditure (TDEE) based on your activity level. Example:

[0575] Low activity level: TDEE = BMR 1.2

[0576] Moderate exercise level: TDEE = BMR 1.55

[0577] High activity level: TDEE = BMR 1.9

[0578] Finally, set a target calorie intake according to the user's health goals. Example:

[0579] Health goal is weight loss: Target calories = TDEE - 500

[0580] Maintaining health goals: Target calories = TDEE

[0581] Health goal is weight gain: Target calories = TDEE + 500

[0582] 3. Creating low-calorie menus

[0583] The server generates a week's worth of low-calorie menus based on your target calorie intake. The generated menus are designed to include a balanced range of nutrients.

[0584] 4. Create a list of ingredients and place orders with suppliers.

[0585] The server creates a list of necessary ingredients based on the generated menu. It checks the supplier's inventory and automatically places orders for the required ingredients.

[0586] 5. Recipe generation and distribution to users

[0587] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[0588] Terminal-side processing

[0589] 1. Provide a user interface.

[0590] The terminal provides an interface that allows the user to input necessary information. It also has the function to display delivered menus and recipes.

[0591] 2. Notification function

[0592] The terminal displays reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions sent from the server.

[0593] User actions

[0594] 1. Information Entry

[0595] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[0596] 2. Check the menu and recipes.

[0597] The user checks a week's worth of menus and recipes displayed on the device and cooks according to the recipes.

[0598] 3. Receiving the ingredients

[0599] The user receives the ingredients on the scheduled date notified by the server.

[0600] Implementation of specific examples

[0601] Initial setup

[0602] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[0603] Calorie setting

[0604] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[0605] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[0606] The target calorie intake for maintaining weight is 2567.19 kcal.

[0607] Menu Generation

[0608] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[0609] Automated ingredient procurement

[0610] Create a list of necessary ingredients and automatically place orders with suppliers.

[0611] Recipe distribution

[0612] The recipe, based on the completed menu, will be sent to the user's device for review.

[0613] This makes it easy for users to consistently follow a low-calorie, balanced diet.

[0614] The following describes the processing flow.

[0615] Step 1:

[0616] The user accesses the system's registration screen using their device and enters personal information such as age, gender, weight, height, exercise level, and health goals.

[0617] Step 2:

[0618] The device sends the personal information entered by the user to the server.

[0619] Step 3:

[0620] The server registers the received user information in the database.

[0621] Step 4:

[0622] The server calculates the basal metabolic rate (BMR) for each user. The calculation method is based on gender, age, weight, and height.

[0623] Step 5:

[0624] The server calculates total daily exercise expenditure (TDEE) based on the exercise level. The calculation is performed using a coefficient that corresponds to the exercise level.

[0625] Step 6:

[0626] The server sets target calories according to the user's health goals (weight loss, maintenance, or weight gain).

[0627] Step 7:

[0628] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[0629] Step 8:

[0630] The server creates a list of necessary ingredients based on the generated menu.

[0631] Step 9:

[0632] The server checks the inventory information of food suppliers and places orders for the necessary ingredients.

[0633] Step 10:

[0634] The server creates detailed recipes based on a week's worth of menus. The recipes include required ingredients, cooking instructions, cooking time, and calorie information.

[0635] Step 11:

[0636] The server delivers the generated recipe to the user's device.

[0637] Step 12:

[0638] The device displays the received recipes and menus to the user.

[0639] Step 13:

[0640] The device sends reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions.

[0641] Step 14:

[0642] The user cooks according to the recipe displayed on their device.

[0643] Step 15:

[0644] Users will continue to practice healthy eating by receiving new recipes and menus every week.

[0645] (Example 1)

[0646] Next, we will describe Example 1. 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".

[0647] Maintaining a healthy diet is not easy in today's busy lifestyle. In particular, calculating appropriate calorie intake based on individual health conditions and lifestyles, and planning meals accordingly, requires expertise, time, and effort, making it a burden for many users. Furthermore, sourcing appropriate ingredients and managing recipes have become significant challenges in daily life. As a result, many users find it difficult to maintain a healthy diet.

[0648] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0649] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus based on the target calories, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from suppliers, and means for delivering the generated recipes to the user's terminal. This makes it possible for the user to easily plan and practice appropriate calorie intake according to their health condition, and to continuously maintain a balanced diet.

[0650] "Means of collecting user health information" refers to devices, systems, or software that allow users to input and collect information such as their age, gender, weight, height, exercise level, and health goals.

[0651] "Means for calculating calorie targets" refers to a device, system, or software that calculates a user's basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on collected user health information, and determines an appropriate calorie target based on these.

[0652] "Means for generating a week's worth of low-calorie meals" refers to a device, system, or software that automatically generates a week's worth of meal plans containing balanced nutrients based on a set calorie target.

[0653] "Means for creating an ingredient list" refers to a device, system, or software that lists the necessary ingredients and their quantities based on a generated menu.

[0654] "Means of automatically ordering ingredients from suppliers" refers to a device, system, or software that checks the inventory information of partner suppliers based on a created ingredient list and automatically orders the necessary ingredients.

[0655] "Means for delivering generated recipes to the user's terminal" refers to a device, system, or software that sends a recipe to the user's terminal, including necessary ingredients, cooking procedures, cooking time, and calorie information, based on a generated week's worth of menus.

[0656] "Means for calculating basal metabolic rate and total calorie expenditure based on exercise level and health goals" refers to a device, system, or software for calculating basal metabolic rate (BMR) and total calorie expenditure (TDEE) based on a user's exercise level and health goals.

[0657] "Means including necessary ingredients, cooking procedures, cooking time, and calorie information" refers to a device, system, or software that manages and provides the ingredients required for a generated recipe, specific cooking procedures, cooking time, and calorie information for each recipe.

[0658] This invention is a system that automatically collects a user's health information, calculates their calorie target, and generates a low-calorie menu based on that target. Furthermore, it automatically orders the necessary ingredients from suppliers and delivers the generated recipe to the user's terminal, making it easy for the user to practice healthy eating.

[0659] Server-side functionality

[0660] User information collection

[0661] The server receives health information such as age, gender, weight, height, exercise level, and health goals entered by the user on the device and stores it in a database. To do this, the server uses the HTTPS protocol to securely receive data from the device.

[0662] Calculating calorie targets

[0663] The server calculates the basal metabolic rate (BMR) based on the received health information. Specifically, it uses the Mifflin-St. Jeor equation and calculates it as follows:

[0664] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[0665] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[0666] Furthermore, the total daily exercise expenditure (TDEE) is calculated based on the exercise level. For example, if the exercise level is "moderate," it is calculated as follows:

[0667] TDEE = BMR 1.55

[0668] Finally, a target calorie intake is set based on the user's health goals. For example, if the goal is to maintain weight, the target calorie intake would be TDEE (Total Daily Excess).

[0669] Creating low-calorie menus

[0670] The server uses a generative AI model to generate a week's worth of low-calorie menus based on target calories. An example of a specific prompt message is as follows:

[0671] "Please generate a one-week low-calorie menu for a 30-year-old male, weighing 70kg, 175cm tall, with a moderate fitness level, and a target calorie intake of 2567.19 kcal."

[0672] The generated menu contains a balanced set of nutrients.

[0673] Creating a list of ingredients and placing orders with suppliers.

[0674] The server creates a list of necessary ingredients based on the generated menu. Then, it uses the supplier's API to check inventory information and automatically places orders for the ingredients.

[0675] Recipe generation and distribution to users

[0676] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[0677] Device-side functions

[0678] User interface provided

[0679] The device provides an interface for users to input information. It displays fields for entering age, gender, weight, height, exercise level, and health goals in a form. It also has the functionality to display delivered menus and recipes.

[0680] Notification function

[0681] The device notifies the user via pop-up or push notifications about the expected arrival date of ingredients sent from the server and reminders about cooking instructions.

[0682] User behavior

[0683] Information entry

[0684] The user launches the terminal app and follows the instructions to enter their age, gender, weight, height, exercise level, and health goals. After reviewing the entered information, they press the submit button to send the information to the server.

[0685] Check the menu and recipes.

[0686] The user checks a week's worth of menus and recipes displayed on their device. They then cook according to the recipes, checking the necessary ingredients and cooking steps.

[0687] Receiving groceries

[0688] The user checks the expected arrival date of the ingredients notified by the supplier and receives the ingredients on that day. They then check the received ingredients and cook according to the recipe.

[0689] This system allows users to easily plan and implement appropriate calorie intake tailored to their health condition, enabling them to maintain a balanced diet on a regular basis.

[0690] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0691] Step 1: Enter and collect user information

[0692] Specific user actions: The user launches the app on their device and follows the instructions to enter their age, gender, weight, height, exercise level, and health goals.

[0693] Specific terminal operation: The terminal stores the entered information in temporary memory and prepares to send it to the server.

[0694] Input: User's age, gender, weight, height, fitness level, health goals

[0695] Output: Data packets containing collected user information

[0696] Step 2: Receiving and saving user information

[0697] Specific server operation: The server receives user information data packets sent from the terminal and stores that information in the database.

[0698] Input: Data packet containing user information

[0699] Output: User information stored in the database

[0700] Step 3: Calculating Basal Metabolic Rate (BMR)

[0701] Specific server operation: The server calculates the basal metabolic rate (BMR) from the stored user information. For example, it uses the Mifflin-St. Jeor equation.

[0702] The inputs used are weight, height, age, and gender.

[0703] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[0704] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[0705] Input: Weight, height, age, gender

[0706] Output: Calculated basal metabolic rate (BMR)

[0707] Step 4: Calculate Total Daily Expenditure (TDEE)

[0708] Specific server operation: The server calculates total daily exercise expenditure (TDEE) based on the exercise level. For example, if the exercise level is "moderate," it calculates as follows:

[0709] TDEE = BMR 1.55 (for moderate activity level)

[0710] Input: Basal metabolic rate (BMR), exercise level

[0711] Output: Calculated Total Daily Expenditure (TDEE)

[0712] Step 5: Setting a target calorie intake

[0713] Specific server operation: The server sets a target calorie intake based on the user's health goals. For example, if the health goal is weight maintenance, the target calorie intake will be TDEE (Total Daily Excess).

[0714] Input: Total Daily Expenditure (TDEE), Health Goals

[0715] Output: Set target calories

[0716] Step 6: Creating a low-calorie menu

[0717] Server operation: The server uses a generative AI model to generate a week's worth of low-calorie menus based on target calories. Enter an example of a prompt message.

[0718] Example prompt: "Generate a one-week low-calorie menu for a 30-year-old male, weighing 70kg, 175cm tall, with a moderate fitness level and a target calorie intake of 2567.19 kcal."

[0719] Input: Target calories, prompt text

[0720] Output: Generated low-calorie menu for one week

[0721] Step 7: Create and order the ingredients list.

[0722] Specific server operation: Based on the generated menu, the server creates a list of necessary ingredients, checks the supplier's inventory information, and automatically places orders.

[0723] Input: Generated low-calorie menu for one week

[0724] Output: List of required ingredients, ordering information for suppliers

[0725] Step 8: Recipe generation and distribution

[0726] Specific server operation: The server generates recipes based on a week's worth of menus and delivers them to the user's terminal. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[0727] Input: Weekly menu

[0728] Output: Recipe delivered to the device

[0729] (Application Example 1)

[0730] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0731] Many people today find it difficult to plan and maintain healthy eating habits amidst their busy lifestyles. Furthermore, creating specific menus for proper calorie management and maintaining good health is challenging. In particular, the effort required to gather all necessary ingredients at once and maintaining a balanced meal plan are significant obstacles. Therefore, there is a need for a system that calculates calorie targets based on the user's health information and continuously provides low-calorie, balanced menus.

[0732] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0733] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from partner suppliers, means for delivering the generated recipes to the user's terminal, and means for calculating the basal metabolic rate, total calorie expenditure, and target calorie intake based on the user's information, generating a week's worth of low-calorie menus based on these, automatically ordering the necessary ingredients, and delivering the generated recipes to the smartphone. This makes it possible for the user to easily and consistently practice a healthy diet.

[0734] "User health information" refers to information necessary to understand the user's health status, such as age, gender, weight, height, exercise level, and health goals.

[0735] A "calorie target" refers to the amount of calories a user should consume per day, calculated based on their health information.

[0736] A "low-calorie menu" refers to a one-week meal plan that is calculated based on a calorie target and includes a balanced range of nutrients.

[0737] A "food ingredient list" refers to a list of ingredients needed based on the generated low-calorie menu.

[0738] A "supplier" refers to a company that provides the necessary ingredients and works in conjunction with the server to automatically supply those ingredients to the user.

[0739] "Automatic ordering" refers to the system automatically placing orders for necessary ingredients with suppliers without any user intervention.

[0740] A "recipe" refers to a cooking method based on a generated low-calorie menu, including the necessary ingredients, cooking steps, cooking time, and calorie information.

[0741] "Device" refers to information devices used by users, such as smartphones, tablets, and personal computers.

[0742] "Basal metabolic rate (BMR)" refers to a value that calculates the amount of energy a user needs while at rest.

[0743] "Total Daily Energy Expenditure (TDEE)" refers to the total amount of energy needed per day, calculated by taking into account the basal metabolic rate and the level of exercise.

[0744] "Delivering to smartphones" refers to sending generated recipes, menus, and other information from the server to the user's smartphone for display.

[0745] Basic configuration

[0746] This system collects users' health information, calculates calorie targets based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device, enabling users to easily and consistently practice healthy eating.

[0747] Server-side processing

[0748] 1. User Information Collection

[0749] The server receives information such as age, gender, weight, height, exercise level, and health goals entered by the user via their device. This information is necessary to accurately understand the user's health status.

[0750] 2. Calculating your calorie target

[0751] The server calculates the basal metabolic rate (BMR) based on the user's health information. For example, it calculates it using the Mifflin-St. Jeor equation as follows:

[0752] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[0753] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[0754] Next, calculate your total daily exercise expenditure (TDEE) based on your activity level. For example, if your activity level is moderate:

[0755] TDEE = BMR 1.55

[0756] Finally, set a target calorie intake according to the user's health goals. For example, if the goal is to maintain weight:

[0757] Target Calories = TDEE

[0758] 3. Creating low-calorie menus

[0759] The server generates a week's worth of low-calorie menus based on your target calorie intake. The generated menus are designed to include a balanced range of nutrients.

[0760] 4. Create ingredient list and automate ordering.

[0761] The server creates a list of necessary ingredients based on the generated menu. It checks the supplier's inventory and automatically orders the necessary ingredients via API.

[0762] 5. Recipe generation and distribution to users

[0763] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include the necessary ingredients, cooking instructions, cooking time, and calorie information.

[0764] Terminal-side processing

[0765] 1. Provide a user interface.

[0766] The terminal provides an interface that allows the user to input necessary information. It also has the function to display delivered menus and recipes.

[0767] 2. Notification function

[0768] The terminal displays reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions sent from the server.

[0769] User actions

[0770] 1. Information Entry

[0771] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[0772] 2. Check the menu and recipes.

[0773] The user checks a week's worth of menus and recipes displayed on the device and cooks according to the recipes.

[0774] 3. Receiving the ingredients

[0775] The user receives the ingredients on the scheduled date notified by the server.

[0776] Implementation of specific examples

[0777] Initial setup

[0778] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[0779] Calorie setting

[0780] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[0781] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[0782] The target calorie intake for maintaining weight is 2567.19 kcal.

[0783] Menu Generation

[0784] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[0785] Automated ingredient procurement

[0786] Create a list of necessary ingredients and automatically place orders via API.

[0787] Recipe distribution

[0788] The recipe, based on the completed menu, will be sent to the user's device for review.

[0789] Example of a prompt:

[0790] "Development of a food delivery app that calculates basal metabolic rate, total calorie expenditure, and target calorie intake based on user information, generates a week's worth of low-calorie menus based on these calculations, automatically orders the necessary ingredients, and delivers the generated recipes to the user's smartphone."

[0791] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0792] Step 1: Enter and collect user information

[0793] Users use a device to input health information such as their age, gender, weight, height, exercise level, and health goals. The device then sends this input information to a server. This input information serves as the basis for calculating calorie goals and is used to monitor one's health status.

[0794] Step 2: Calculating Basal Metabolic Rate (BMR)

[0795] The server calculates each user's basal metabolic rate (BMR) based on the user's health information received in Step 1. Specifically, it calculates the BMR using the Mifflin-St. Jeor equation. For example, for a male:

[0796] Input: weight, height, age, gender

[0797] Data calculation: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[0798] Output: BMR value

[0799] Step 3: Calculate Total Daily Expenditure (TDEE)

[0800] The server calculates the total daily exercise expenditure (TDEE) by adding the exercise level to the basal metabolic rate (BMR) calculated in step 2. For example, if the exercise level is moderate:

[0801] Input: Basal metabolic rate, exercise level

[0802] Data calculation: TDEE = BMR 1.55

[0803] Output: TDEE value

[0804] Step 4: Setting a target calorie intake

[0805] The server sets a target calorie intake based on the user's health goals (weight loss, maintenance, weight gain, etc.). For example, if the goal is weight maintenance:

[0806] Input: Total calories burned, health goal

[0807] Data calculation: Target calories = TDEE

[0808] Output: Target calorie value

[0809] Step 5: Creating a low-calorie menu

[0810] Based on the target calorie intake obtained in step 4, the server generates a week's worth of low-calorie menus. The generated menus are designed to include a balanced range of nutrients. Specifically, the server selects appropriate recipes from the database and constructs the menu.

[0811] Input: Target calories

[0812] Data calculation: Execute menu optimization algorithm

[0813] Output: A week's worth of low-calorie menus

[0814] Step 6: Create a food list and automate ordering.

[0815] The server creates a list of necessary ingredients based on the menu generated in step 5. It then automatically places orders for the ingredients with partner suppliers via API.

[0816] Input: Low-calorie menu

[0817] Data processing: Create ingredient lists and send requests to supplier APIs.

[0818] Output: Ingredient list, confirmation of automatic ordering

[0819] Step 7: Recipe generation and distribution

[0820] The server generates detailed recipes based on a week's worth of menus. The generated recipes are delivered to the user's device, where the user can check the necessary ingredients, cooking instructions, cooking time, calorie information, and more.

[0821] Input: Low-calorie menu

[0822] Data processing: Execute the recipe generation algorithm.

[0823] Output: Recipe delivery to user terminals

[0824] Example of a prompt:

[0825] "Development of a food delivery app that calculates basal metabolic rate, total calorie expenditure, and target calorie intake based on user information, generates a week's worth of low-calorie menus based on these calculations, automatically orders the necessary ingredients, and delivers the generated recipes to the user's smartphone."

[0826] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0827] Basic configuration

[0828] This system collects the user's health information, calculates a calorie target based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device. Furthermore, it incorporates an emotion engine that recognizes the user's emotions, adjusting the menus and recipes based on those emotions.

[0829] Server-side processing

[0830] 1. User Information Collection

[0831] The user uses their device to input their age, gender, weight, height, exercise level, and health goals. The device then sends this information to the server.

[0832] 2. Calculating your calorie target

[0833] The server calculates the basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on the user's health information, and sets a target calorie intake based on their health goals.

[0834] 3. Creating low-calorie menus

[0835] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[0836] 4. Create a list of ingredients and place orders with suppliers.

[0837] The server creates a list of necessary ingredients based on the generated menu and automatically places orders with suppliers.

[0838] 5. Recipe generation and distribution to users

[0839] The server creates detailed recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[0840] 6. Emotion recognition by an emotion engine

[0841] The user inputs emotions using a device. The emotion engine recognizes this input and records the user's current emotional state.

[0842] 7. Adjusting the menu and recipes

[0843] The server adjusts menus and recipes based on the recognized user's emotions. For example, it suggests a menu using ingredients that promote relaxation for a user who is feeling stressed.

[0844] 8. Utilizing past emotional data

[0845] The server analyzes past sentiment data to optimize the menu for the following week. It learns the user's sentiment patterns and provides the most suitable menu and recipes.

[0846] Terminal-side processing

[0847] 1. Provide a user interface.

[0848] The device provides an interface that allows users to input necessary information. It also has functions for receiving delivered menus and recipes, and for inputting emotions.

[0849] 2. Notification function

[0850] The terminal displays reminder notifications from the server regarding the expected arrival date of ingredients and cooking instructions, as well as requests for emotional input.

[0851] User actions

[0852] 1. Information Entry

[0853] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[0854] 2. Emotional Input

[0855] Users periodically input their emotional state into the device. The emotion engine recognizes this data and sends it to the server.

[0856] 3. Check the menu and recipes.

[0857] The user checks the menu and recipes for the week displayed on the device and cooks according to the recipes.

[0858] 4. Receiving the ingredients

[0859] The user receives the ingredients on the scheduled date notified by the server.

[0860] 5. Receive and adjust next week's menu.

[0861] At the end of each week, users receive a menu optimized for the following week based on their emotional data.

[0862] Implementation of specific examples

[0863] Initial setup

[0864] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[0865] Calorie setting

[0866] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[0867] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[0868] The target calorie intake for maintaining weight is 2567.19 kcal.

[0869] Menu Generation

[0870] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[0871] Emotional input and adjustment

[0872] If a user is experiencing work-related stress, the emotion engine recognizes this and provides a menu containing ingredients effective in reducing stress. Furthermore, the menu for the following week is optimized based on the user's emotional history.

[0873] Automated ingredient procurement

[0874] Create a list of necessary ingredients and automatically place orders with suppliers.

[0875] Recipe distribution

[0876] The recipe, based on the completed menu, will be sent to the user's device for review.

[0877] In this way, users can easily implement a consistent low-calorie diet based on their health information and emotional state.

[0878] The following describes the processing flow.

[0879] Step 1:

[0880] The user accesses the system's registration screen using their device and enters personal information such as age, gender, weight, height, exercise level, and health goals.

[0881] Step 2:

[0882] The terminal sends the user's entered personal information to the server.

[0883] Step 3:

[0884] The server registers the received user information in the database.

[0885] Step 4:

[0886] The server calculates the basal metabolic rate (BMR) for each user. The calculation method is based on gender, age, weight, and height.

[0887] Step 5:

[0888] The server calculates total daily exercise expenditure (TDEE) based on the exercise level. The calculation is performed using a coefficient that corresponds to the exercise level.

[0889] Step 6:

[0890] The server sets target calories according to the user's health goals (weight loss, maintenance, or weight gain).

[0891] Step 7:

[0892] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[0893] Step 8:

[0894] The server creates a list of necessary ingredients based on the generated menu.

[0895] Step 9:

[0896] The server checks the inventory information of food suppliers and places orders for the necessary ingredients.

[0897] Step 10:

[0898] The server creates detailed recipes based on a week's worth of menus. The recipes include required ingredients, cooking instructions, cooking time, and calorie information.

[0899] Step 11:

[0900] The server delivers the generated recipe to the user's device.

[0901] Step 12:

[0902] The device displays the received recipes and menus to the user.

[0903] Step 13:

[0904] The device sends reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions.

[0905] Step 14:

[0906] The user cooks according to the recipe displayed on their device.

[0907] Step 15:

[0908] Users periodically input their emotional state into the device. For example, their emotional state might be "feeling stressed" or "feeling relaxed."

[0909] Step 16:

[0910] The device sends the entered emotional state information to the server.

[0911] Step 17:

[0912] The server uses an emotion engine to analyze and record the user's emotional state.

[0913] Step 18:

[0914] The server adjusts menus and recipes based on the user's emotional state. For example, if a user is feeling stressed, it will offer a menu using ingredients that have a relaxing effect.

[0915] Step 19:

[0916] The server analyzes past sentiment data to optimize the menu for the following week. It learns the user's emotional patterns and provides the most suitable menu and recipes.

[0917] Step 20:

[0918] The server delivers the next week's menu and adjusted recipes to the user's device.

[0919] Step 21:

[0920] Users receive new menus and recipes every week, encouraging them to consistently practice healthy eating.

[0921] (Example 2)

[0922] Next, we will describe Example 2. 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".

[0923] In modern society, individual health management is a crucial issue, and improving dietary habits is particularly essential for maintaining and promoting health. However, it is difficult for many people to design and follow an optimal meal plan based on their own health condition. Furthermore, preparing the necessary ingredients and implementing appropriate recipes in a busy daily life requires considerable time and effort. Moreover, the influence of a user's emotional state on food choices cannot be ignored. This invention aims to solve these problems and provide individual users with an effective and easy-to-use means of health management.

[0924] The identification processing performed by the identification 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 collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from suppliers, means for delivering the generated recipes to the user's terminal, and means for collecting emotional information and adjusting the menus and recipes based on it. As a result, the user can easily obtain meal menus optimized for their individual health condition and efficiently prepare the necessary ingredients and execute the recipes. In addition, the provision of appropriate menus according to the user's emotional state is realized, promoting comprehensive health management.

[0925] "User health information" refers to information about the user's health status and lifestyle, such as the user's age, gender, weight, height, exercise level, and health goals.

[0926] A "calorie target" is the appropriate amount of energy a user should consume per day to achieve their health goals.

[0927] A "low-calorie menu" is a balanced meal plan for one week, tailored to the user's calorie goals.

[0928] The "list of required ingredients" is a list of ingredients needed to execute a given menu, based on the generated menu.

[0929] A "supplier" is a company or organization that is responsible for providing users with the necessary ingredients.

[0930] A "recipe" is information that describes the detailed steps and necessary ingredients for cooking a specific dish.

[0931] "Emotional information" refers to data that represents the user's emotional state, and this is used to adjust menus and recipes.

[0932] An "emotion engine" is a system that recognizes the user's emotional input and determines their current emotional state.

[0933] Basal metabolic rate (BMR) is an indicator that shows the amount of energy a user consumes while at rest.

[0934] Total Daily Energy Expenditure (TDEE) is an indicator that shows the total amount of energy a user consumes in a day, and is calculated by adding the energy consumed through exercise and daily activities to the basal metabolic rate.

[0935] A "generative AI model" is an artificial intelligence model that automatically generates menus and recipes based on user input.

[0936] This invention is a system that calculates a calorie target based on the user's health and emotional information, and generates and delivers a week's worth of low-calorie menus. The following hardware and software are used to implement this invention.

[0937] The server and terminals exchange data over the internet. The server runs on the Linux operating system and executes Python programs. The server uses MySQL as its database and utilizes common AI models for generating AI models.

[0938] Users input health and emotional information using their devices (e.g., smartphones, tablets, PCs). The device software is developed using the React Native framework and provides notification functionality using Firebase Cloud Messaging.

[0939] The server receives health information sent by the user and calculates the calorie target using the Python NumPy library. For example, for a 30-year-old male, weighing 70kg, 175cm tall, and with an exercise level of "medium," the calculation would be as follows:

[0940] Basal metabolic rate (BMR): 10 70 + 6.25 175 - 5 30 + 5 = 1656.25 kcal

[0941] Total Daily Energy Expenditure (TDEE): BMR 1.55 = 1656.25 1.55 ≈ 2567.19 kcal

[0942] After calculation, the server uses a generating AI model (e.g., OpenAI's GPT-3) to send a prompt message like the following to generate a week's worth of low-calorie menus:

[0943] "The user's target calorie intake is 2567 kcal. Please generate a balanced 1-week meal plan."

[0944] Based on the generated menu, the server uses the Pandas library to create a list of necessary ingredients and automatically places orders for them. It uses the AWS API to place orders with suppliers and notifies the terminal of the order confirmation.

[0945] Detailed recipes are generated by sending the following prompt to the generative AI model:

[0946] "Please generate a detailed recipe based on this menu."

[0947] The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information, which is sent to the device in JSON format and displayed on the user interface.

[0948] Furthermore, when emotional information is entered by the user, the server analyzes it using the Microsoft Azure Emotion API and saves the results to a database. The system adjusts menus and recipes based on this emotional information. If the user enters that they are feeling stressed, the server instructs the generating AI model to "adjust the menu to one with a relaxing effect."

[0949] Based on past sentiment data, the server optimizes the menu for the following week. The server analyzes past sentiment data using Scikit-learn's machine learning algorithm and prompts the generative AI model to "generate the optimal menu based on the user's sentiment patterns."

[0950] In this way, the system can automatically suggest and implement the optimal meal plan based on the user's health and emotional information.

[0951] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0952] Step 1:

[0953] Users input health information using their devices. Specifically, they enter their age, gender, weight, height, exercise level, and health goals on an input screen. The entered data is sent to the server in JSON format.

[0954] Input: Data entered by the user, including age, gender, weight, height, exercise level, and health goals.

[0955] Output: User health information data in JSON format is sent to the server.

[0956] Step 2:

[0957] The server uses the NumPy library in Python to calculate the calorie target based on the received health information data.

[0958] Input: Health information data in JSON format submitted in Step 1.

[0959] Output: Basal metabolic rate (BMR) and total daily energy expenditure (TDEE) values.

[0960] As a concrete example, perform the following calculation:

[0961] Basal metabolic rate (BMR): 10 Weight (kg) + 6.25 Height (cm) - 5 Age (years) + 5 (for males)

[0962] Total Daily Expenditure (TDEE): BMR (Body Mass Index)

[0963] Step 3:

[0964] The server uses a generative AI model to send the following prompt to generate a week's worth of low-calorie menus: "The user's target calorie intake is 2567 kcal. Please generate a balanced week's worth of menus."

[0965] Input: Basal metabolic rate (BMR) and total daily energy expenditure (TDEE).

[0966] Output: A week's worth of low-calorie menus generated.

[0967] As a concrete example, a prompt message is sent to a generative AI model, and the generated text-formatted menu is converted to JSON format and saved.

[0968] Step 4:

[0969] The server creates a list of required ingredients based on the generated menu. It uses the Pandas library to generate a dataframe and list the required ingredients.

[0970] Input: A week's worth of low-calorie menus generated by a generative AI model.

[0971] Output: List of required ingredients.

[0972] As a concrete example, the ingredient information extracted from the menu is listed and saved in JSON format.

[0973] Step 5:

[0974] The server uses the AWS API to automatically place orders with suppliers based on the required ingredient list. It then notifies the terminal of the order confirmation.

[0975] Input: List of required ingredients.

[0976] Output: Order confirmation notification to the supplier.

[0977] As a concrete example, a POST request is sent to an AWS API endpoint, and an acknowledgment is received from the supplier.

[0978] Step 6:

[0979] The server sends a prompt to the generating AI model to generate a detailed recipe: "Generate a detailed recipe based on this menu."

[0980] Input: A week's worth of low-calorie menus.

[0981] Output: Detailed recipe.

[0982] The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information, and is sent to the device in JSON format.

[0983] Step 7:

[0984] The user inputs emotional information through their device. They select their current emotional state on the emotional input screen, and the data is sent to the server.

[0985] Input: User sentiment information.

[0986] Output: Sentiment information data in JSON format is sent to the server.

[0987] Step 8:

[0988] The server analyzes emotional information using the Microsoft Azure Emotion API and stores the results in a database.

[0989] Input: Sentiment information data in JSON format.

[0990] Output: Analyzed emotional state data.

[0991] As a concrete example, emotion data is sent to the Emotion API, the analysis results are obtained, and the data is saved in a database.

[0992] Step 9:

[0993] The server sends a prompt to the AI ​​model based on past sentiment data to optimize the menu for the following week: "Generate the optimal menu based on the user's sentiment patterns."

[0994] Input: Analyzed historical sentiment data.

[0995] Output: Optimized menu for next week.

[0996] As a concrete example, past emotional patterns are analyzed using Scikit-learn, and an optimized menu is generated by sending prompt sentences to a generative AI model.

[0997] (Application Example 2)

[0998] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0999] Conventional meal management systems can calculate calories and generate menus based on a user's health information, but they cannot manage meals while considering the user's emotional state. Therefore, it was difficult to provide appropriate menus when users were experiencing stress or fatigue. Furthermore, the lack of a mechanism to optimize meals based on emotional state made it difficult to improve the user's mental satisfaction. Therefore, the present invention aims to provide a system that integrates the management of a user's health information and emotional state to provide more individualized and optimal meal menus.

[1000] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1001] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, and means for generating a week's worth of low-calorie menus. To this end, it further includes means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from partner suppliers, means for delivering the generated recipes to the user's terminal, means for collecting the user's emotional state and adjusting the menus based on this, and means for optimizing the menus for the following week using past emotional data. This makes it possible to provide personalized and optimal meal menus that take the user's emotional state into consideration.

[1002] "User health information" refers to information related to an individual's health status and lifestyle, such as the user's age, gender, weight, height, exercise level, and health goals.

[1003] A "calorie target" refers to the appropriate amount of calories a user should consume daily to achieve a specific health goal, based on their basal metabolic rate and total calorie expenditure.

[1004] A "low-calorie menu" refers to a set of meal plans designed to help users meet their health goals while keeping their calorie intake low.

[1005] A "food ingredient list" refers to a list of specific ingredients needed for cooking, based on the generated menu.

[1006] "Supplier" refers to a business or service that sells or provides food ingredients that users need.

[1007] "Automated ordering" refers to the process by which a system automatically places orders for ingredients with suppliers.

[1008] A "recipe" is a document that describes the detailed steps for preparing a specific dish, including ingredients, cooking instructions, cooking time, and calorie information.

[1009] "Emotional state" refers to the emotions and psychological state that a user is experiencing. For example, it can refer to states such as stress, fatigue, or happiness.

[1010] "Menu adjustment" refers to the process of optimizing or modifying existing menus, taking into account the user's current emotional state.

[1011] "Emotional data" refers to historical information about emotional states collected through user input or emotion recognition systems.

[1012] Modes for carrying out the invention

[1013] Basic configuration

[1014] This system automatically generates and manages meal plans based on the user's health information and emotional state. Specifically, it collects and analyzes the user's health information, sets calorie targets, generates menus, automatically orders ingredients, and adjusts menus to take the user's emotional state into consideration, thereby providing a system that meets the individual needs of the user.

[1015] Server-side processing

[1016] 1. User Information Collection

[1017] Users use their devices to input health information such as their age, gender, weight, height, exercise level, and health goals. This information is transmitted to the server in real time and stored in a database.

[1018] 2. Calculating your calorie target

[1019] The server calculates the basal metabolic rate (BMR) and total daily allowance (TDEE) based on the received health information, and sets individual calorie targets. By using Python and Flask for these calculations, highly accurate results are provided in real time.

[1020] 3. Creating low-calorie menus

[1021] Using a generative AI model, a week's worth of low-calorie menus are generated based on calculated calorie targets. The generated menus take into account nutritional balance and variety of ingredients, contributing to the user's health maintenance.

[1022] 4. Create a list of ingredients and place orders with suppliers.

[1023] The server creates a list of necessary ingredients based on the generated menu. Based on this list, it automatically places orders with partner suppliers. The ingredient list is sent in JSON format, and suppliers procure and deliver the ingredients accordingly.

[1024] 5. Recipe generation and distribution to users

[1025] The server creates a detailed recipe based on the menu and delivers it to the user's device. This recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information. The user then cooks according to the recipe.

[1026] 6. Emotion recognition by an emotion engine

[1027] Users periodically input their emotional state into their device. The emotion engine recognizes this data and sends it to the server. Based on this information, the server stores the user's emotional state in a database and performs analysis.

[1028] 7. Adjusting the menu and recipes

[1029] The server adjusts menus and recipes based on the recognized emotional state. For example, if a user is feeling stressed, it suggests a menu using ingredients with relaxing properties. Python and Flask are used to analyze emotional data and adjust menus in real time.

[1030] 8. Utilizing past emotional data

[1031] The server analyzes past emotional data and learns the user's emotional patterns to optimize the menu for the following week. It uses a generative AI model to provide menus based on emotional patterns.

[1032] Terminal-side processing

[1033] 1. Provide a user interface.

[1034] The device provides an interface that allows users to input necessary information. It also has functions for receiving delivered menus and recipes, and for inputting emotions.

[1035] 2. Notification function

[1036] The terminal displays reminder notifications from the server regarding the expected arrival date of ingredients and cooking instructions, as well as requests for emotional input.

[1037] Implementation of specific examples

[1038] Initial setup

[1039] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[1040] Calorie setting

[1041] The basal metabolic rate (BMR) is 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal. The total daily allowance (TDEE) is BMR 1.55 = 1656.25 / 1.55 ​​≈ 2567.19 kcal. The target calorie intake for weight maintenance is 2567.19 kcal.

[1042] Menu Generation

[1043] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[1044] Emotional input and adjustment

[1045] If a user is experiencing work-related stress, the emotion engine recognizes this and provides a menu containing ingredients effective in reducing stress. Furthermore, the menu for the following week is optimized based on the user's emotional history.

[1046] Example of a prompt

[1047] When generating recipes or menus using a generative AI model, use the following prompts.

[1048] The user is 30 years old, male, weighs 70kg, and is 175cm tall. Their exercise level is moderate, and their goal is to maintain their weight. Please calculate calories and generate a one-week low-calorie meal plan for this user. Also, considering the user's stress levels, please include ingredients that are effective in reducing stress in the meal plan.

[1049] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1050] Step 1:

[1051] Collection of user information

[1052] The terminal displays an interface for the user to enter their health information (age, gender, weight, height, exercise level, and health goals). Once the user enters this information and presses the submit button, the terminal sends the entered information to the server. The entered data is sent to the server in JSON format, and the server stores it in its database.

[1053] Step 2:

[1054] Calculating calorie targets

[1055] The server calculates the basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on the user information received. Specifically, the BMR is calculated using the following formula: BMR = 10 / weight (kg) + 6.25 / height (cm) - 5 / age (years) + gender (5 for males, -161 for females). Next, the TDEE is calculated by multiplying the BMR by the exercise level. After calculation, the server stores the daily target calories in the database.

[1056] Step 3:

[1057] Creating low-calorie menus

[1058] The server uses a generative AI model to generate a week's worth of low-calorie menus based on a set calorie goal. The generative AI model is given the following prompt as input: "The user is 30 years old, male, weighs 70 kg, and is 175 cm tall. Their exercise level is moderate, and their goal is to maintain their weight. For this user, please calculate calories and generate a week's worth of low-calorie menus." Based on the prompt, the generative AI model generates the menus and outputs them to the server. The server saves the generated menus to its database.

[1059] Step 4:

[1060] Creating ingredient lists and automated ordering.

[1061] The server creates a list of necessary ingredients based on the generated menu. Specifically, it analyzes each menu item and lists the required ingredients and their quantities. This list is generated in JSON format, and the server automatically places an order with its partner suppliers. The order data is sent to the supplier's API.

[1062] Step 5:

[1063] Recipe generation and distribution

[1064] The server creates a detailed recipe based on the menu. The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information. This is then delivered to the user's device. The device provides an interface for the user to view the recipe and sends reminders via notifications.

[1065] Step 6:

[1066] Emotional input and recognition

[1067] Users periodically input their emotional state using a device. The entered emotional data is sent from the device to the server. The server analyzes this data using an emotion engine and stores the user's current emotional state in a database.

[1068] Step 7:

[1069] Adjustments to the menu and recipes

[1070] The server adjusts menus and recipes based on the recognized user's emotions. For example, if the user is feeling stressed, it will suggest a menu using ingredients that have a relaxing effect. The server uses a generative AI model to generate new menus and delivers them to the user's device.

[1071] Step 8:

[1072] Utilizing past emotional data

[1073] The server analyzes past emotional data and learns the user's emotional patterns. It then makes suggestions that take these emotional patterns into account for future menu optimization. The server saves the generated optimized menu to a database and delivers it to the user's device.

[1074] In this way, a series of processes involving the server, terminal, and user provides a personalized meal menu based on the user's health information and emotional state.

[1075] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1076] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1077] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1078] [Third Embodiment]

[1079] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1080] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1081] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1082] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1083] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1084] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1085] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1086] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1087] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1088] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1089] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1090] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1091] Basic configuration

[1092] This system collects users' health information, calculates calorie targets based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device, enabling users to easily and consistently practice healthy eating.

[1093] Server-side processing

[1094] 1. User Information Collection

[1095] The user uses their device to input their age, gender, weight, height, exercise level, health goals, etc. The device then sends this information to the server.

[1096] 2. Calculating your calorie target

[1097] The server calculates the basal metabolic rate (BMR) based on the user's health information. For example, it calculates it using the Mifflin-St. Jeor equation as follows:

[1098] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[1099] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[1100] Next, calculate your total daily exercise expenditure (TDEE) based on your activity level. Example:

[1101] Low activity level: TDEE = BMR 1.2

[1102] Moderate exercise level: TDEE = BMR 1.55

[1103] High activity level: TDEE = BMR 1.9

[1104] Finally, set a target calorie intake according to the user's health goals. Example:

[1105] Health goal is weight loss: Target calories = TDEE - 500

[1106] Maintaining health goals: Target calories = TDEE

[1107] Health goal is weight gain: Target calories = TDEE + 500

[1108] 3. Creating low-calorie menus

[1109] The server generates a week's worth of low-calorie menus based on your target calorie intake. The generated menus are designed to include a balanced range of nutrients.

[1110] 4. Create a list of ingredients and place orders with suppliers.

[1111] The server creates a list of necessary ingredients based on the generated menu. It checks the supplier's inventory and automatically places orders for the required ingredients.

[1112] 5. Recipe generation and distribution to users

[1113] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[1114] Terminal-side processing

[1115] 1. Provide a user interface.

[1116] The terminal provides an interface that allows the user to input necessary information. It also has the function to display delivered menus and recipes.

[1117] 2. Notification function

[1118] The terminal displays reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions sent from the server.

[1119] User actions

[1120] 1. Information Entry

[1121] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[1122] 2. Check the menu and recipes.

[1123] The user checks a week's worth of menus and recipes displayed on the device and cooks according to the recipes.

[1124] 3. Receiving the ingredients

[1125] The user receives the ingredients on the scheduled date notified by the server.

[1126] Implementation of specific examples

[1127] Initial setup

[1128] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[1129] Calorie setting

[1130] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[1131] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[1132] The target calorie intake for maintaining weight is 2567.19 kcal.

[1133] Menu Generation

[1134] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[1135] Automated ingredient procurement

[1136] Create a list of necessary ingredients and automatically place orders with suppliers.

[1137] Recipe distribution

[1138] The recipe, based on the completed menu, will be sent to the user's device for review.

[1139] This makes it easy for users to consistently follow a low-calorie, balanced diet.

[1140] The following describes the processing flow.

[1141] Step 1:

[1142] The user accesses the system's registration screen using their device and enters personal information such as age, gender, weight, height, exercise level, and health goals.

[1143] Step 2:

[1144] The device sends the personal information entered by the user to the server.

[1145] Step 3:

[1146] The server registers the received user information in the database.

[1147] Step 4:

[1148] The server calculates the basal metabolic rate (BMR) for each user. The calculation method is based on gender, age, weight, and height.

[1149] Step 5:

[1150] The server calculates total daily exercise expenditure (TDEE) based on the exercise level. The calculation is performed using a coefficient that corresponds to the exercise level.

[1151] Step 6:

[1152] The server sets target calories according to the user's health goals (weight loss, maintenance, or weight gain).

[1153] Step 7:

[1154] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[1155] Step 8:

[1156] The server creates a list of necessary ingredients based on the generated menu.

[1157] Step 9:

[1158] The server checks the inventory information of food suppliers and places orders for the necessary ingredients.

[1159] Step 10:

[1160] The server creates detailed recipes based on a week's worth of menus. The recipes include required ingredients, cooking instructions, cooking time, and calorie information.

[1161] Step 11:

[1162] The server delivers the generated recipe to the user's device.

[1163] Step 12:

[1164] The device displays the received recipes and menus to the user.

[1165] Step 13:

[1166] The device sends reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions.

[1167] Step 14:

[1168] The user cooks according to the recipe displayed on their device.

[1169] Step 15:

[1170] Users will continue to practice healthy eating by receiving new recipes and menus every week.

[1171] (Example 1)

[1172] Next, we will describe Example 1. 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."

[1173] Maintaining a healthy diet is not easy in today's busy lifestyle. In particular, calculating appropriate calorie intake based on individual health conditions and lifestyles, and planning meals accordingly, requires expertise, time, and effort, making it a burden for many users. Furthermore, sourcing appropriate ingredients and managing recipes have become significant challenges in daily life. As a result, many users find it difficult to maintain a healthy diet.

[1174] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1175] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus based on the target calories, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from suppliers, and means for delivering the generated recipes to the user's terminal. This makes it possible for the user to easily plan and practice appropriate calorie intake according to their health condition, and to continuously maintain a balanced diet.

[1176] "Means of collecting user health information" refers to devices, systems, or software that allow users to input and collect information such as their age, gender, weight, height, exercise level, and health goals.

[1177] "Means for calculating calorie targets" refers to a device, system, or software that calculates a user's basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on collected user health information, and determines an appropriate calorie target based on these.

[1178] "Means for generating a week's worth of low-calorie meals" refers to a device, system, or software that automatically generates a week's worth of meal plans containing balanced nutrients based on a set calorie target.

[1179] "Means for creating an ingredient list" refers to a device, system, or software that lists the necessary ingredients and their quantities based on a generated menu.

[1180] "Means of automatically ordering ingredients from suppliers" refers to a device, system, or software that checks the inventory information of partner suppliers based on a created ingredient list and automatically orders the necessary ingredients.

[1181] "Means for delivering generated recipes to the user's terminal" refers to a device, system, or software that sends a recipe to the user's terminal, including necessary ingredients, cooking procedures, cooking time, and calorie information, based on a generated week's worth of menus.

[1182] "Means for calculating basal metabolic rate and total calorie expenditure based on exercise level and health goals" refers to a device, system, or software for calculating basal metabolic rate (BMR) and total calorie expenditure (TDEE) based on a user's exercise level and health goals.

[1183] "Means including necessary ingredients, cooking procedures, cooking time, and calorie information" refers to a device, system, or software that manages and provides the ingredients required for a generated recipe, specific cooking procedures, cooking time, and calorie information for each recipe.

[1184] This invention is a system that automatically collects a user's health information, calculates their calorie target, and generates a low-calorie menu based on that target. Furthermore, it automatically orders the necessary ingredients from suppliers and delivers the generated recipe to the user's terminal, making it easy for the user to practice healthy eating.

[1185] Server-side functionality

[1186] User information collection

[1187] The server receives health information such as age, gender, weight, height, exercise level, and health goals entered by the user on the device and stores it in a database. To do this, the server uses the HTTPS protocol to securely receive data from the device.

[1188] Calculating calorie targets

[1189] The server calculates the basal metabolic rate (BMR) based on the received health information. Specifically, it uses the Mifflin-St. Jeor equation and calculates it as follows:

[1190] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[1191] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[1192] Furthermore, the total daily exercise expenditure (TDEE) is calculated based on the exercise level. For example, if the exercise level is "moderate," it is calculated as follows:

[1193] TDEE = BMR 1.55

[1194] Finally, a target calorie intake is set based on the user's health goals. For example, if the goal is to maintain weight, the target calorie intake would be TDEE (Total Daily Excess).

[1195] Creating low-calorie menus

[1196] The server uses a generative AI model to generate a week's worth of low-calorie menus based on target calories. An example of a specific prompt message is as follows:

[1197] "Please generate a one-week low-calorie menu for a 30-year-old male, weighing 70kg, 175cm tall, with a moderate fitness level, and a target calorie intake of 2567.19 kcal."

[1198] The generated menu contains a balanced set of nutrients.

[1199] Creating a list of ingredients and placing orders with suppliers.

[1200] The server creates a list of necessary ingredients based on the generated menu. Then, it uses the supplier's API to check inventory information and automatically places orders for the ingredients.

[1201] Recipe generation and distribution to users

[1202] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[1203] Device-side functions

[1204] User interface provided

[1205] The device provides an interface for users to input information. It displays fields for entering age, gender, weight, height, exercise level, and health goals in a form. It also has the functionality to display delivered menus and recipes.

[1206] Notification function

[1207] The device notifies the user via pop-up or push notifications about the expected arrival date of ingredients sent from the server and reminders about cooking instructions.

[1208] User behavior

[1209] Information entry

[1210] The user launches the terminal app and follows the instructions to enter their age, gender, weight, height, exercise level, and health goals. After reviewing the entered information, they press the submit button to send the information to the server.

[1211] Check the menu and recipes.

[1212] The user checks a week's worth of menus and recipes displayed on their device. They then cook according to the recipes, checking the necessary ingredients and cooking steps.

[1213] Receiving groceries

[1214] The user checks the expected arrival date of the ingredients notified by the supplier and receives the ingredients on that day. They then check the received ingredients and cook according to the recipe.

[1215] This system allows users to easily plan and implement appropriate calorie intake tailored to their health condition, enabling them to maintain a balanced diet on a regular basis.

[1216] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1217] Step 1: Enter and collect user information

[1218] Specific user actions: The user launches the app on their device and follows the instructions to enter their age, gender, weight, height, exercise level, and health goals.

[1219] Specific terminal operation: The terminal stores the entered information in temporary memory and prepares to send it to the server.

[1220] Input: User's age, gender, weight, height, fitness level, health goals

[1221] Output: Data packets containing collected user information

[1222] Step 2: Receiving and saving user information

[1223] Specific server operation: The server receives user information data packets sent from the terminal and stores that information in the database.

[1224] Input: Data packet containing user information

[1225] Output: User information stored in the database

[1226] Step 3: Calculating Basal Metabolic Rate (BMR)

[1227] Specific server operation: The server calculates the basal metabolic rate (BMR) from the stored user information. For example, it uses the Mifflin-St. Jeor equation.

[1228] The inputs used are weight, height, age, and gender.

[1229] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[1230] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[1231] Input: Weight, height, age, gender

[1232] Output: Calculated basal metabolic rate (BMR)

[1233] Step 4: Calculate Total Daily Expenditure (TDEE)

[1234] Specific server operation: The server calculates total daily exercise expenditure (TDEE) based on the exercise level. For example, if the exercise level is "moderate," it calculates as follows:

[1235] TDEE = BMR 1.55 (for moderate activity level)

[1236] Input: Basal metabolic rate (BMR), exercise level

[1237] Output: Calculated Total Daily Expenditure (TDEE)

[1238] Step 5: Setting a target calorie intake

[1239] Specific server operation: The server sets a target calorie intake based on the user's health goals. For example, if the health goal is weight maintenance, the target calorie intake will be TDEE (Total Daily Excess).

[1240] Input: Total Daily Expenditure (TDEE), Health Goals

[1241] Output: Set target calories

[1242] Step 6: Creating a low-calorie menu

[1243] Server operation: The server uses a generative AI model to generate a week's worth of low-calorie menus based on target calories. Enter an example of a prompt message.

[1244] Example prompt: "Generate a one-week low-calorie menu for a 30-year-old male, weighing 70kg, 175cm tall, with a moderate fitness level and a target calorie intake of 2567.19 kcal."

[1245] Input: Target calories, prompt text

[1246] Output: Generated low-calorie menu for one week

[1247] Step 7: Create and order the ingredients list.

[1248] Specific server operation: Based on the generated menu, the server creates a list of necessary ingredients, checks the supplier's inventory information, and automatically places orders.

[1249] Input: Generated low-calorie menu for one week

[1250] Output: List of required ingredients, ordering information for suppliers

[1251] Step 8: Recipe generation and distribution

[1252] Specific server operation: The server generates recipes based on a week's worth of menus and delivers them to the user's terminal. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[1253] Input: Weekly menu

[1254] Output: Recipe delivered to the device

[1255] (Application Example 1)

[1256] Next, we will explain Application Example 1. In the following explanation, 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."

[1257] Many people today find it difficult to plan and maintain healthy eating habits amidst their busy lifestyles. Furthermore, creating specific menus for proper calorie management and maintaining good health is challenging. In particular, the effort required to gather all necessary ingredients at once and maintaining a balanced meal plan are significant obstacles. Therefore, there is a need for a system that calculates calorie targets based on the user's health information and continuously provides low-calorie, balanced menus.

[1258] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1259] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from partner suppliers, means for delivering the generated recipes to the user's terminal, and means for calculating the basal metabolic rate, total calorie expenditure, and target calorie intake based on the user's information, generating a week's worth of low-calorie menus based on these, automatically ordering the necessary ingredients, and delivering the generated recipes to the smartphone. This makes it possible for the user to easily and consistently practice a healthy diet.

[1260] "User health information" refers to information necessary to understand the user's health status, such as age, gender, weight, height, exercise level, and health goals.

[1261] A "calorie target" refers to the amount of calories a user should consume per day, calculated based on their health information.

[1262] A "low-calorie menu" refers to a one-week meal plan that is calculated based on a calorie target and includes a balanced range of nutrients.

[1263] A "food ingredient list" refers to a list of ingredients needed based on the generated low-calorie menu.

[1264] A "supplier" refers to a company that provides the necessary ingredients and works in conjunction with the server to automatically supply those ingredients to the user.

[1265] "Automatic ordering" refers to the system automatically placing orders for necessary ingredients with suppliers without any user intervention.

[1266] A "recipe" refers to a cooking method based on a generated low-calorie menu, including the necessary ingredients, cooking steps, cooking time, and calorie information.

[1267] "Device" refers to information devices used by users, such as smartphones, tablets, and personal computers.

[1268] "Basal metabolic rate (BMR)" refers to a value that calculates the amount of energy a user needs while at rest.

[1269] "Total Daily Energy Expenditure (TDEE)" refers to the total amount of energy needed per day, calculated by taking into account the basal metabolic rate and the level of exercise.

[1270] "Delivering to smartphones" refers to sending generated recipes, menus, and other information from the server to the user's smartphone for display.

[1271] Basic configuration

[1272] This system collects users' health information, calculates calorie targets based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device, enabling users to easily and consistently practice healthy eating.

[1273] Server-side processing

[1274] 1. User Information Collection

[1275] The server receives information such as age, gender, weight, height, exercise level, and health goals entered by the user via their device. This information is necessary to accurately understand the user's health status.

[1276] 2. Calculating your calorie target

[1277] The server calculates the basal metabolic rate (BMR) based on the user's health information. For example, it calculates it using the Mifflin-St. Jeor equation as follows:

[1278] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[1279] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[1280] Next, calculate your total daily exercise expenditure (TDEE) based on your activity level. For example, if your activity level is moderate:

[1281] TDEE = BMR 1.55

[1282] Finally, set a target calorie intake according to the user's health goals. For example, if the goal is to maintain weight:

[1283] Target Calories = TDEE

[1284] 3. Creating low-calorie menus

[1285] The server generates a week's worth of low-calorie menus based on your target calorie intake. The generated menus are designed to include a balanced range of nutrients.

[1286] 4. Create ingredient list and automate ordering.

[1287] The server creates a list of necessary ingredients based on the generated menu. It checks the supplier's inventory and automatically orders the necessary ingredients via API.

[1288] 5. Recipe generation and distribution to users

[1289] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include the necessary ingredients, cooking instructions, cooking time, and calorie information.

[1290] Terminal-side processing

[1291] 1. Provide a user interface.

[1292] The terminal provides an interface that allows the user to input necessary information. It also has the function to display delivered menus and recipes.

[1293] 2. Notification function

[1294] The terminal displays reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions sent from the server.

[1295] User actions

[1296] 1. Information Entry

[1297] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[1298] 2. Check the menu and recipes.

[1299] The user checks a week's worth of menus and recipes displayed on the device and cooks according to the recipes.

[1300] 3. Receiving the ingredients

[1301] The user receives the ingredients on the scheduled date notified by the server.

[1302] Implementation of specific examples

[1303] Initial setup

[1304] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[1305] Calorie setting

[1306] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[1307] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[1308] The target calorie intake for maintaining weight is 2567.19 kcal.

[1309] Menu Generation

[1310] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[1311] Automated ingredient procurement

[1312] Create a list of necessary ingredients and automatically place orders via API.

[1313] Recipe distribution

[1314] The recipe, based on the completed menu, will be sent to the user's device for review.

[1315] Example of a prompt:

[1316] "Development of a food delivery app that calculates basal metabolic rate, total calorie expenditure, and target calorie intake based on user information, generates a week's worth of low-calorie menus based on these calculations, automatically orders the necessary ingredients, and delivers the generated recipes to the user's smartphone."

[1317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1318] Step 1: Enter and collect user information

[1319] Users use a device to input health information such as their age, gender, weight, height, exercise level, and health goals. The device then sends this input information to a server. This input information serves as the basis for calculating calorie goals and is used to monitor one's health status.

[1320] Step 2: Calculating Basal Metabolic Rate (BMR)

[1321] The server calculates each user's basal metabolic rate (BMR) based on the user's health information received in Step 1. Specifically, it calculates the BMR using the Mifflin-St. Jeor equation. For example, for a male:

[1322] Input: weight, height, age, gender

[1323] Data calculation: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[1324] Output: BMR value

[1325] Step 3: Calculate Total Daily Expenditure (TDEE)

[1326] The server calculates the total daily exercise expenditure (TDEE) by adding the exercise level to the basal metabolic rate (BMR) calculated in step 2. For example, if the exercise level is moderate:

[1327] Input: Basal metabolic rate, exercise level

[1328] Data calculation: TDEE = BMR 1.55

[1329] Output: TDEE value

[1330] Step 4: Setting a target calorie intake

[1331] The server sets a target calorie intake based on the user's health goals (weight loss, maintenance, weight gain, etc.). For example, if the goal is weight maintenance:

[1332] Input: Total calories burned, health goal

[1333] Data calculation: Target calories = TDEE

[1334] Output: Target calorie value

[1335] Step 5: Creating a low-calorie menu

[1336] Based on the target calorie intake obtained in step 4, the server generates a week's worth of low-calorie menus. The generated menus are designed to include a balanced range of nutrients. Specifically, the server selects appropriate recipes from the database and constructs the menu.

[1337] Input: Target calories

[1338] Data calculation: Execute menu optimization algorithm

[1339] Output: A week's worth of low-calorie menus

[1340] Step 6: Create a food list and automate ordering.

[1341] The server creates a list of necessary ingredients based on the menu generated in step 5. It then automatically places orders for the ingredients with partner suppliers via API.

[1342] Input: Low-calorie menu

[1343] Data processing: Create ingredient lists and send requests to supplier APIs.

[1344] Output: Ingredient list, confirmation of automatic ordering

[1345] Step 7: Recipe generation and distribution

[1346] The server generates detailed recipes based on a week's worth of menus. The generated recipes are delivered to the user's device, where the user can check the necessary ingredients, cooking instructions, cooking time, calorie information, and more.

[1347] Input: Low-calorie menu

[1348] Data processing: Execute the recipe generation algorithm.

[1349] Output: Recipe delivery to user terminals

[1350] Example of a prompt:

[1351] "Development of a food delivery app that calculates basal metabolic rate, total calorie expenditure, and target calorie intake based on user information, generates a week's worth of low-calorie menus based on these calculations, automatically orders the necessary ingredients, and delivers the generated recipes to the user's smartphone."

[1352] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1353] Basic configuration

[1354] This system collects the user's health information, calculates a calorie target based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device. Furthermore, it incorporates an emotion engine that recognizes the user's emotions, adjusting the menus and recipes based on those emotions.

[1355] Server-side processing

[1356] 1. User Information Collection

[1357] The user uses their device to input their age, gender, weight, height, exercise level, and health goals. The device then sends this information to the server.

[1358] 2. Calculating your calorie target

[1359] The server calculates the basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on the user's health information, and sets a target calorie intake based on their health goals.

[1360] 3. Creating low-calorie menus

[1361] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[1362] 4. Create a list of ingredients and place orders with suppliers.

[1363] The server creates a list of necessary ingredients based on the generated menu and automatically places orders with suppliers.

[1364] 5. Recipe generation and distribution to users

[1365] The server creates detailed recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[1366] 6. Emotion recognition by an emotion engine

[1367] The user inputs emotions using a device. The emotion engine recognizes this input and records the user's current emotional state.

[1368] 7. Adjusting the menu and recipes

[1369] The server adjusts menus and recipes based on the recognized user's emotions. For example, it suggests a menu using ingredients that promote relaxation for a user who is feeling stressed.

[1370] 8. Utilizing past emotional data

[1371] The server analyzes past sentiment data to optimize the menu for the following week. It learns the user's sentiment patterns and provides the most suitable menu and recipes.

[1372] Terminal-side processing

[1373] 1. Provide a user interface.

[1374] The device provides an interface that allows users to input necessary information. It also has functions for receiving delivered menus and recipes, and for inputting emotions.

[1375] 2. Notification function

[1376] The terminal displays reminder notifications from the server regarding the expected arrival date of ingredients and cooking instructions, as well as requests for emotional input.

[1377] User actions

[1378] 1. Information Entry

[1379] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[1380] 2. Emotional Input

[1381] Users periodically input their emotional state into the device. The emotion engine recognizes this data and sends it to the server.

[1382] 3. Check the menu and recipes.

[1383] The user checks the menu and recipes for the week displayed on the device and cooks according to the recipes.

[1384] 4. Receiving the ingredients

[1385] The user receives the ingredients on the scheduled date notified by the server.

[1386] 5. Receive and adjust next week's menu.

[1387] At the end of each week, users receive a menu optimized for the following week based on their emotional data.

[1388] Implementation of specific examples

[1389] Initial setup

[1390] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[1391] Calorie setting

[1392] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[1393] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[1394] The target calorie intake for maintaining weight is 2567.19 kcal.

[1395] Menu Generation

[1396] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[1397] Emotional input and adjustment

[1398] If a user is experiencing work-related stress, the emotion engine recognizes this and provides a menu containing ingredients effective in reducing stress. Furthermore, the menu for the following week is optimized based on the user's emotional history.

[1399] Automated ingredient procurement

[1400] Create a list of necessary ingredients and automatically place orders with suppliers.

[1401] Recipe distribution

[1402] The recipe, based on the completed menu, will be sent to the user's device for review.

[1403] In this way, users can easily implement a consistent low-calorie diet based on their health information and emotional state.

[1404] The following describes the processing flow.

[1405] Step 1:

[1406] The user accesses the system's registration screen using their device and enters personal information such as age, gender, weight, height, exercise level, and health goals.

[1407] Step 2:

[1408] The terminal sends the user's entered personal information to the server.

[1409] Step 3:

[1410] The server registers the received user information in the database.

[1411] Step 4:

[1412] The server calculates the basal metabolic rate (BMR) for each user. The calculation method is based on gender, age, weight, and height.

[1413] Step 5:

[1414] The server calculates total daily exercise expenditure (TDEE) based on the exercise level. The calculation is performed using a coefficient that corresponds to the exercise level.

[1415] Step 6:

[1416] The server sets target calories according to the user's health goals (weight loss, maintenance, or weight gain).

[1417] Step 7:

[1418] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[1419] Step 8:

[1420] The server creates a list of necessary ingredients based on the generated menu.

[1421] Step 9:

[1422] The server checks the inventory information of food suppliers and places orders for the necessary ingredients.

[1423] Step 10:

[1424] The server creates detailed recipes based on a week's worth of menus. The recipes include required ingredients, cooking instructions, cooking time, and calorie information.

[1425] Step 11:

[1426] The server delivers the generated recipe to the user's device.

[1427] Step 12:

[1428] The device displays the received recipes and menus to the user.

[1429] Step 13:

[1430] The device sends reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions.

[1431] Step 14:

[1432] The user cooks according to the recipe displayed on their device.

[1433] Step 15:

[1434] Users periodically input their emotional state into the device. For example, their emotional state might be "feeling stressed" or "feeling relaxed."

[1435] Step 16:

[1436] The device sends the entered emotional state information to the server.

[1437] Step 17:

[1438] The server uses an emotion engine to analyze and record the user's emotional state.

[1439] Step 18:

[1440] The server adjusts menus and recipes based on the user's emotional state. For example, if a user is feeling stressed, it will offer a menu using ingredients that have a relaxing effect.

[1441] Step 19:

[1442] The server analyzes past sentiment data to optimize the menu for the following week. It learns the user's sentiment patterns and provides the most suitable menu and recipes.

[1443] Step 20:

[1444] The server delivers the next week's menu and adjusted recipes to the user's device.

[1445] Step 21:

[1446] Users receive new menus and recipes every week, encouraging them to consistently practice healthy eating.

[1447] (Example 2)

[1448] Next, we will describe Example 2. 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."

[1449] In modern society, individual health management is a crucial issue, and improving dietary habits is particularly essential for maintaining and promoting health. However, it is difficult for many people to design and follow an optimal meal plan based on their own health condition. Furthermore, preparing the necessary ingredients and implementing appropriate recipes in a busy daily life requires considerable time and effort. Moreover, the influence of a user's emotional state on food choices cannot be ignored. This invention aims to solve these problems and provide individual users with an effective and easy-to-use means of health management.

[1450] The identification processing performed by the identification 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 collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from suppliers, means for delivering the generated recipes to the user's terminal, and means for collecting emotional information and adjusting the menus and recipes based on it. As a result, the user can easily obtain meal menus optimized for their individual health condition and efficiently prepare the necessary ingredients and execute the recipes. In addition, the provision of appropriate menus according to the user's emotional state is realized, promoting comprehensive health management.

[1451] "User health information" refers to information about the user's health status and lifestyle, such as the user's age, gender, weight, height, exercise level, and health goals.

[1452] A "calorie target" is the appropriate amount of energy a user should consume per day to achieve their health goals.

[1453] A "low-calorie menu" is a balanced meal plan for one week, tailored to the user's calorie goals.

[1454] The "list of required ingredients" is a list of ingredients needed to execute a given menu, based on the generated menu.

[1455] A "supplier" is a company or organization that is responsible for providing users with the necessary ingredients.

[1456] A "recipe" is information that describes the detailed steps and necessary ingredients for cooking a specific dish.

[1457] "Emotional information" refers to data that represents the user's emotional state, and this is used to adjust menus and recipes.

[1458] An "emotion engine" is a system that recognizes the user's emotional input and determines their current emotional state.

[1459] Basal metabolic rate (BMR) is an indicator that shows the amount of energy a user consumes while at rest.

[1460] Total Daily Energy Expenditure (TDEE) is an indicator that shows the total amount of energy a user consumes in a day, and is calculated by adding the energy consumed through exercise and daily activities to the basal metabolic rate.

[1461] A "generative AI model" is an artificial intelligence model that automatically generates menus and recipes based on user input.

[1462] This invention is a system that calculates a calorie target based on the user's health and emotional information, and generates and delivers a week's worth of low-calorie menus. The following hardware and software are used to implement this invention.

[1463] The server and terminals exchange data over the internet. The server runs on the Linux operating system and executes Python programs. The server uses MySQL as its database and utilizes common AI models for generating AI models.

[1464] Users input health and emotional information using their devices (e.g., smartphones, tablets, PCs). The device software is developed using the React Native framework and provides notification functionality using Firebase Cloud Messaging.

[1465] The server receives health information sent by the user and calculates the calorie target using the Python NumPy library. For example, for a 30-year-old male, weighing 70kg, 175cm tall, and with an exercise level of "medium," the calculation would be as follows:

[1466] Basal metabolic rate (BMR): 10 70 + 6.25 175 - 5 30 + 5 = 1656.25 kcal

[1467] Total Daily Energy Expenditure (TDEE): BMR 1.55 = 1656.25 1.55 ≈ 2567.19 kcal

[1468] After calculation, the server uses a generating AI model (e.g., OpenAI's GPT-3) to send a prompt message like the following to generate a week's worth of low-calorie menus:

[1469] "The user's target calorie intake is 2567 kcal. Please generate a balanced 1-week meal plan."

[1470] Based on the generated menu, the server uses the Pandas library to create a list of necessary ingredients and automatically places orders for them. It uses the AWS API to place orders with suppliers and notifies the terminal of the order confirmation.

[1471] Detailed recipes are generated by sending the following prompt to the generative AI model:

[1472] "Please generate a detailed recipe based on this menu."

[1473] The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information, which is sent to the device in JSON format and displayed on the user interface.

[1474] Furthermore, when emotional information is entered by the user, the server analyzes it using the Microsoft Azure Emotion API and saves the results to a database. The system adjusts menus and recipes based on this emotional information. If the user enters that they are feeling stressed, the server instructs the generating AI model to "adjust the menu to one with a relaxing effect."

[1475] Based on past sentiment data, the server optimizes the menu for the following week. The server analyzes past sentiment data using Scikit-learn's machine learning algorithm and prompts the generative AI model to "generate the optimal menu based on the user's sentiment patterns."

[1476] In this way, the system can automatically suggest and implement the optimal meal plan based on the user's health and emotional information.

[1477] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1478] Step 1:

[1479] Users input health information using their devices. Specifically, they enter their age, gender, weight, height, exercise level, and health goals on an input screen. The entered data is sent to the server in JSON format.

[1480] Input: Data entered by the user, including age, gender, weight, height, exercise level, and health goals.

[1481] Output: User health information data in JSON format is sent to the server.

[1482] Step 2:

[1483] The server uses the NumPy library in Python to calculate the calorie target based on the received health information data.

[1484] Input: Health information data in JSON format submitted in Step 1.

[1485] Output: Basal metabolic rate (BMR) and total daily energy expenditure (TDEE) values.

[1486] As a concrete example, perform the following calculation:

[1487] Basal metabolic rate (BMR): 10 Weight (kg) + 6.25 Height (cm) - 5 Age (years) + 5 (for males)

[1488] Total Daily Expenditure (TDEE): BMR (Body Mass Index)

[1489] Step 3:

[1490] The server uses a generative AI model to send the following prompt to generate a week's worth of low-calorie menus: "The user's target calorie intake is 2567 kcal. Please generate a balanced week's worth of menus."

[1491] Input: Basal metabolic rate (BMR) and total daily energy expenditure (TDEE).

[1492] Output: A week's worth of low-calorie menus generated.

[1493] As a concrete example, a prompt message is sent to a generative AI model, and the generated text-formatted menu is converted to JSON format and saved.

[1494] Step 4:

[1495] The server creates a list of required ingredients based on the generated menu. It uses the Pandas library to generate a dataframe and list the required ingredients.

[1496] Input: A week's worth of low-calorie menus generated by a generative AI model.

[1497] Output: List of required ingredients.

[1498] As a concrete example, the ingredient information extracted from the menu is listed and saved in JSON format.

[1499] Step 5:

[1500] The server uses the AWS API to automatically place orders with suppliers based on the required ingredient list. It then notifies the terminal of the order confirmation.

[1501] Input: List of required ingredients.

[1502] Output: Order confirmation notification to the supplier.

[1503] As a concrete example, a POST request is sent to an AWS API endpoint, and an acknowledgment is received from the supplier.

[1504] Step 6:

[1505] The server sends a prompt to the generating AI model to generate a detailed recipe: "Generate a detailed recipe based on this menu."

[1506] Input: A week's worth of low-calorie menus.

[1507] Output: Detailed recipe.

[1508] The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information, and is sent to the device in JSON format.

[1509] Step 7:

[1510] The user inputs emotional information through their device. They select their current emotional state on the emotional input screen, and the data is sent to the server.

[1511] Input: User sentiment information.

[1512] Output: Sentiment information data in JSON format is sent to the server.

[1513] Step 8:

[1514] The server analyzes emotional information using the Microsoft Azure Emotion API and stores the results in a database.

[1515] Input: Sentiment information data in JSON format.

[1516] Output: Analyzed emotional state data.

[1517] As a concrete example, emotion data is sent to the Emotion API, the analysis results are obtained, and the data is saved in a database.

[1518] Step 9:

[1519] The server sends a prompt to the AI ​​model based on past sentiment data to optimize the menu for the following week: "Generate the optimal menu based on the user's sentiment patterns."

[1520] Input: Analyzed historical sentiment data.

[1521] Output: Optimized menu for next week.

[1522] As a concrete example, past emotional patterns are analyzed using Scikit-learn, and an optimized menu is generated by sending prompt sentences to a generative AI model.

[1523] (Application Example 2)

[1524] Next, we will explain application example 2. In the following explanation, 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."

[1525] Conventional meal management systems can calculate calories and generate menus based on a user's health information, but they cannot manage meals while considering the user's emotional state. Therefore, it was difficult to provide appropriate menus when users were experiencing stress or fatigue. Furthermore, the lack of a mechanism to optimize meals based on emotional state made it difficult to improve the user's mental satisfaction. Therefore, the present invention aims to provide a system that integrates the management of a user's health information and emotional state to provide more individualized and optimal meal menus.

[1526] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1527] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, and means for generating a week's worth of low-calorie menus. To this end, it further includes means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from partner suppliers, means for delivering the generated recipes to the user's terminal, means for collecting the user's emotional state and adjusting the menus based on this, and means for optimizing the menus for the following week using past emotional data. This makes it possible to provide personalized and optimal meal menus that take the user's emotional state into consideration.

[1528] "User health information" refers to information related to an individual's health status and lifestyle, such as the user's age, gender, weight, height, exercise level, and health goals.

[1529] A "calorie target" refers to the appropriate amount of calories a user should consume daily to achieve a specific health goal, based on their basal metabolic rate and total calorie expenditure.

[1530] A "low-calorie menu" refers to a set of meal plans designed to help users meet their health goals while keeping their calorie intake low.

[1531] A "food ingredient list" refers to a list of specific ingredients needed for cooking, based on the generated menu.

[1532] "Supplier" refers to a business or service that sells or provides food ingredients that users need.

[1533] "Automated ordering" refers to the process by which a system automatically places orders for ingredients with suppliers.

[1534] A "recipe" is a document that describes the detailed steps for preparing a specific dish, including ingredients, cooking instructions, cooking time, and calorie information.

[1535] "Emotional state" refers to the emotions and psychological state that a user is experiencing. For example, it can refer to states such as stress, fatigue, or happiness.

[1536] "Menu adjustment" refers to the process of optimizing or modifying existing menus, taking into account the user's current emotional state.

[1537] "Emotional data" refers to historical information about emotional states collected through user input or emotion recognition systems.

[1538] Modes for carrying out the invention

[1539] Basic configuration

[1540] This system automatically generates and manages meal plans based on the user's health information and emotional state. Specifically, it collects and analyzes the user's health information, sets calorie targets, generates menus, automatically orders ingredients, and adjusts menus to take the user's emotional state into consideration, thereby providing a system that meets the individual needs of the user.

[1541] Server-side processing

[1542] 1. User Information Collection

[1543] Users use their devices to input health information such as their age, gender, weight, height, exercise level, and health goals. This information is transmitted to the server in real time and stored in a database.

[1544] 2. Calculating your calorie target

[1545] The server calculates the basal metabolic rate (BMR) and total daily allowance (TDEE) based on the received health information, and sets individual calorie targets. By using Python and Flask for these calculations, highly accurate results are provided in real time.

[1546] 3. Creating low-calorie menus

[1547] Using a generative AI model, a week's worth of low-calorie menus are generated based on calculated calorie targets. The generated menus take into account nutritional balance and variety of ingredients, contributing to the user's health maintenance.

[1548] 4. Create a list of ingredients and place orders with suppliers.

[1549] The server creates a list of necessary ingredients based on the generated menu. Based on this list, it automatically places orders with partner suppliers. The ingredient list is sent in JSON format, and suppliers procure and deliver the ingredients accordingly.

[1550] 5. Recipe generation and distribution to users

[1551] The server creates a detailed recipe based on the menu and delivers it to the user's device. This recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information. The user then cooks according to the recipe.

[1552] 6. Emotion recognition by an emotion engine

[1553] Users periodically input their emotional state into their device. The emotion engine recognizes this data and sends it to the server. Based on this information, the server stores the user's emotional state in a database and performs analysis.

[1554] 7. Adjusting the menu and recipes

[1555] The server adjusts menus and recipes based on the recognized emotional state. For example, if a user is feeling stressed, it suggests a menu using ingredients with relaxing properties. Python and Flask are used to analyze emotional data and adjust menus in real time.

[1556] 8. Utilizing past emotional data

[1557] The server analyzes past emotional data and learns the user's emotional patterns to optimize the menu for the following week. It uses a generative AI model to provide menus based on emotional patterns.

[1558] Terminal-side processing

[1559] 1. Provide a user interface.

[1560] The device provides an interface that allows users to input necessary information. It also has functions for receiving delivered menus and recipes, and for inputting emotions.

[1561] 2. Notification function

[1562] The terminal displays reminder notifications from the server regarding the expected arrival date of ingredients and cooking instructions, as well as requests for emotional input.

[1563] Implementation of specific examples

[1564] Initial setup

[1565] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[1566] Calorie setting

[1567] The basal metabolic rate (BMR) is 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal. The total daily allowance (TDEE) is BMR 1.55 = 1656.25 / 1.55 ​​≈ 2567.19 kcal. The target calorie intake for weight maintenance is 2567.19 kcal.

[1568] Menu Generation

[1569] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[1570] Emotional input and adjustment

[1571] If a user is experiencing work-related stress, the emotion engine recognizes this and provides a menu containing ingredients effective in reducing stress. Furthermore, the menu for the following week is optimized based on the user's emotional history.

[1572] Example of a prompt

[1573] When generating recipes or menus using a generative AI model, use the following prompts.

[1574] The user is 30 years old, male, weighs 70kg, and is 175cm tall. Their exercise level is moderate, and their goal is to maintain their weight. Please calculate calories and generate a one-week low-calorie meal plan for this user. Also, considering the user's stress levels, please include ingredients that are effective in reducing stress in the meal plan.

[1575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1576] Step 1:

[1577] Collection of user information

[1578] The terminal displays an interface for the user to enter their health information (age, gender, weight, height, exercise level, and health goals). Once the user enters this information and presses the submit button, the terminal sends the entered information to the server. The entered data is sent to the server in JSON format, and the server stores it in its database.

[1579] Step 2:

[1580] Calculating calorie targets

[1581] The server calculates the basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on the user information received. Specifically, the BMR is calculated using the following formula: BMR = 10 / weight (kg) + 6.25 / height (cm) - 5 / age (years) + gender (5 for males, -161 for females). Next, the TDEE is calculated by multiplying the BMR by the exercise level. After calculation, the server stores the daily target calories in the database.

[1582] Step 3:

[1583] Creating low-calorie menus

[1584] The server uses a generative AI model to generate a week's worth of low-calorie menus based on a set calorie goal. The generative AI model is given the following prompt as input: "The user is 30 years old, male, weighs 70 kg, and is 175 cm tall. Their exercise level is moderate, and their goal is to maintain their weight. For this user, please calculate calories and generate a week's worth of low-calorie menus." Based on the prompt, the generative AI model generates the menus and outputs them to the server. The server saves the generated menus to its database.

[1585] Step 4:

[1586] Creating ingredient lists and automated ordering.

[1587] The server creates a list of necessary ingredients based on the generated menu. Specifically, it analyzes each menu item and lists the required ingredients and their quantities. This list is generated in JSON format, and the server automatically places an order with its partner suppliers. The order data is sent to the supplier's API.

[1588] Step 5:

[1589] Recipe generation and distribution

[1590] The server creates a detailed recipe based on the menu. The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information. This is then delivered to the user's device. The device provides an interface for the user to view the recipe and sends reminders via notifications.

[1591] Step 6:

[1592] Emotional input and recognition

[1593] Users periodically input their emotional state using a device. The entered emotional data is sent from the device to the server. The server analyzes this data using an emotion engine and stores the user's current emotional state in a database.

[1594] Step 7:

[1595] Adjustments to the menu and recipes

[1596] The server adjusts menus and recipes based on the recognized user's emotions. For example, if the user is feeling stressed, it will suggest a menu using ingredients that have a relaxing effect. The server uses a generative AI model to generate new menus and delivers them to the user's device.

[1597] Step 8:

[1598] Utilizing past emotional data

[1599] The server analyzes past emotional data and learns the user's emotional patterns. It then makes suggestions that take these emotional patterns into account for future menu optimization. The server saves the generated optimized menu to a database and delivers it to the user's device.

[1600] In this way, a series of processes involving the server, terminal, and user provides a personalized meal menu based on the user's health information and emotional state.

[1601] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1602] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1603] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1604] [Fourth Embodiment]

[1605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1606] As shown in Figure 7, the 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.

[1607] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1608] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1609] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1610] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1611] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1612] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1613] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1614] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1615] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1616] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1617] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1618] Basic configuration

[1619] This system collects users' health information, calculates calorie targets based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device, enabling users to easily and consistently practice healthy eating.

[1620] Server-side processing

[1621] 1. User Information Collection

[1622] The user uses their device to input their age, gender, weight, height, exercise level, health goals, etc. The device then sends this information to the server.

[1623] 2. Calculating your calorie target

[1624] The server calculates the basal metabolic rate (BMR) based on the user's health information. For example, it calculates it using the Mifflin-St. Jeor equation as follows:

[1625] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[1626] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[1627] Next, calculate your total daily exercise expenditure (TDEE) based on your activity level. Example:

[1628] Low activity level: TDEE = BMR 1.2

[1629] Moderate exercise level: TDEE = BMR 1.55

[1630] High activity level: TDEE = BMR 1.9

[1631] Finally, set a target calorie intake according to the user's health goals. Example:

[1632] Health goal is weight loss: Target calories = TDEE - 500

[1633] Maintaining health goals: Target calories = TDEE

[1634] Health goal is weight gain: Target calories = TDEE + 500

[1635] 3. Creating low-calorie menus

[1636] The server generates a week's worth of low-calorie menus based on your target calorie intake. The generated menus are designed to include a balanced range of nutrients.

[1637] 4. Create a list of ingredients and place orders with suppliers.

[1638] The server creates a list of necessary ingredients based on the generated menu. It checks the supplier's inventory and automatically places orders for the required ingredients.

[1639] 5. Recipe generation and distribution to users

[1640] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[1641] Terminal-side processing

[1642] 1. Provide a user interface.

[1643] The terminal provides an interface that allows the user to input necessary information. It also has the function to display delivered menus and recipes.

[1644] 2. Notification function

[1645] The terminal displays reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions sent from the server.

[1646] User actions

[1647] 1. Information Entry

[1648] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[1649] 2. Check the menu and recipes.

[1650] The user checks a week's worth of menus and recipes displayed on the device and cooks according to the recipes.

[1651] 3. Receiving the ingredients

[1652] The user receives the ingredients on the scheduled date notified by the server.

[1653] Implementation of specific examples

[1654] Initial setup

[1655] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[1656] Calorie setting

[1657] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[1658] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[1659] The target calorie intake for maintaining weight is 2567.19 kcal.

[1660] Menu Generation

[1661] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[1662] Automated ingredient procurement

[1663] Create a list of necessary ingredients and automatically place orders with suppliers.

[1664] Recipe distribution

[1665] The recipe, based on the completed menu, will be sent to the user's device for review.

[1666] This makes it easy for users to consistently follow a low-calorie, balanced diet.

[1667] The following describes the processing flow.

[1668] Step 1:

[1669] The user accesses the system's registration screen using their device and enters personal information such as age, gender, weight, height, exercise level, and health goals.

[1670] Step 2:

[1671] The device sends the personal information entered by the user to the server.

[1672] Step 3:

[1673] The server registers the received user information in the database.

[1674] Step 4:

[1675] The server calculates the basal metabolic rate (BMR) for each user. The calculation method is based on gender, age, weight, and height.

[1676] Step 5:

[1677] The server calculates total daily exercise expenditure (TDEE) based on the exercise level. The calculation is performed using a coefficient that corresponds to the exercise level.

[1678] Step 6:

[1679] The server sets target calories according to the user's health goals (weight loss, maintenance, or weight gain).

[1680] Step 7:

[1681] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[1682] Step 8:

[1683] The server creates a list of necessary ingredients based on the generated menu.

[1684] Step 9:

[1685] The server checks the inventory information of food suppliers and places orders for the necessary ingredients.

[1686] Step 10:

[1687] The server creates detailed recipes based on a week's worth of menus. The recipes include required ingredients, cooking instructions, cooking time, and calorie information.

[1688] Step 11:

[1689] The server delivers the generated recipe to the user's device.

[1690] Step 12:

[1691] The device displays the received recipes and menus to the user.

[1692] Step 13:

[1693] The device sends reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions.

[1694] Step 14:

[1695] The user cooks according to the recipe displayed on their device.

[1696] Step 15:

[1697] Users will continue to practice healthy eating by receiving new recipes and menus every week.

[1698] (Example 1)

[1699] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1700] Maintaining a healthy diet is not easy in today's busy lifestyle. In particular, calculating appropriate calorie intake based on individual health conditions and lifestyles, and planning meals accordingly, requires expertise, time, and effort, making it a burden for many users. Furthermore, sourcing appropriate ingredients and managing recipes have become significant challenges in daily life. As a result, many users find it difficult to maintain a healthy diet.

[1701] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1702] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus based on the target calories, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from suppliers, and means for delivering the generated recipes to the user's terminal. This makes it possible for the user to easily plan and practice appropriate calorie intake according to their health condition, and to continuously maintain a balanced diet.

[1703] "Means of collecting user health information" refers to devices, systems, or software that allow users to input and collect information such as their age, gender, weight, height, exercise level, and health goals.

[1704] "Means for calculating calorie targets" refers to a device, system, or software that calculates a user's basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on collected user health information, and determines an appropriate calorie target based on these.

[1705] "Means for generating a week's worth of low-calorie meals" refers to a device, system, or software that automatically generates a week's worth of meal plans containing balanced nutrients based on a set calorie target.

[1706] "Means for creating an ingredient list" refers to a device, system, or software that lists the necessary ingredients and their quantities based on a generated menu.

[1707] "Means of automatically ordering ingredients from suppliers" refers to a device, system, or software that checks the inventory information of partner suppliers based on a created ingredient list and automatically orders the necessary ingredients.

[1708] "Means for delivering generated recipes to the user's terminal" refers to a device, system, or software that sends a recipe to the user's terminal, including necessary ingredients, cooking procedures, cooking time, and calorie information, based on a generated week's worth of menus.

[1709] "Means for calculating basal metabolic rate and total calorie expenditure based on exercise level and health goals" refers to a device, system, or software for calculating basal metabolic rate (BMR) and total calorie expenditure (TDEE) based on a user's exercise level and health goals.

[1710] "Means including necessary ingredients, cooking procedures, cooking time, and calorie information" refers to a device, system, or software that manages and provides the ingredients required for a generated recipe, specific cooking procedures, cooking time, and calorie information for each recipe.

[1711] This invention is a system that automatically collects a user's health information, calculates their calorie target, and generates a low-calorie menu based on that target. Furthermore, it automatically orders the necessary ingredients from suppliers and delivers the generated recipe to the user's terminal, making it easy for the user to practice healthy eating.

[1712] Server-side functionality

[1713] User information collection

[1714] The server receives health information such as age, gender, weight, height, exercise level, and health goals entered by the user on the device and stores it in a database. To do this, the server uses the HTTPS protocol to securely receive data from the device.

[1715] Calculating calorie targets

[1716] The server calculates the basal metabolic rate (BMR) based on the received health information. Specifically, it uses the Mifflin-St. Jeor equation and calculates it as follows:

[1717] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[1718] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[1719] Furthermore, the total daily exercise expenditure (TDEE) is calculated based on the exercise level. For example, if the exercise level is "moderate," it is calculated as follows:

[1720] TDEE = BMR 1.55

[1721] Finally, a target calorie intake is set based on the user's health goals. For example, if the goal is to maintain weight, the target calorie intake would be TDEE (Total Daily Excess).

[1722] Creating low-calorie menus

[1723] The server uses a generative AI model to generate a week's worth of low-calorie menus based on target calories. An example of a specific prompt message is as follows:

[1724] "Please generate a one-week low-calorie menu for a 30-year-old male, weighing 70kg, 175cm tall, with a moderate fitness level, and a target calorie intake of 2567.19 kcal."

[1725] The generated menu contains a balanced set of nutrients.

[1726] Creating a list of ingredients and placing orders with suppliers.

[1727] The server creates a list of necessary ingredients based on the generated menu. Then, it uses the supplier's API to check inventory information and automatically places orders for the ingredients.

[1728] Recipe generation and distribution to users

[1729] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[1730] Device-side functions

[1731] User interface provided

[1732] The device provides an interface for users to input information. It displays fields for entering age, gender, weight, height, exercise level, and health goals in a form. It also has the functionality to display delivered menus and recipes.

[1733] Notification function

[1734] The device notifies the user via pop-up or push notifications about the expected arrival date of ingredients sent from the server and reminders about cooking instructions.

[1735] User behavior

[1736] Information entry

[1737] The user launches the terminal app and follows the instructions to enter their age, gender, weight, height, exercise level, and health goals. After reviewing the entered information, they press the submit button to send the information to the server.

[1738] Check the menu and recipes.

[1739] The user checks a week's worth of menus and recipes displayed on their device. They then cook according to the recipes, checking the necessary ingredients and cooking steps.

[1740] Receiving groceries

[1741] The user checks the expected arrival date of the ingredients notified by the supplier and receives the ingredients on that day. They then check the received ingredients and cook according to the recipe.

[1742] This system allows users to easily plan and implement appropriate calorie intake tailored to their health condition, enabling them to maintain a balanced diet on a regular basis.

[1743] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1744] Step 1: Enter and collect user information

[1745] Specific user actions: The user launches the app on their device and follows the instructions to enter their age, gender, weight, height, exercise level, and health goals.

[1746] Specific terminal operation: The terminal stores the entered information in temporary memory and prepares to send it to the server.

[1747] Input: User's age, gender, weight, height, fitness level, health goals

[1748] Output: Data packets containing collected user information

[1749] Step 2: Receiving and saving user information

[1750] Specific server operation: The server receives user information data packets sent from the terminal and stores that information in the database.

[1751] Input: Data packet containing user information

[1752] Output: User information stored in the database

[1753] Step 3: Calculating Basal Metabolic Rate (BMR)

[1754] Specific server operation: The server calculates the basal metabolic rate (BMR) from the stored user information. For example, it uses the Mifflin-St. Jeor equation.

[1755] The inputs used are weight, height, age, and gender.

[1756] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[1757] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[1758] Input: Weight, height, age, gender

[1759] Output: Calculated basal metabolic rate (BMR)

[1760] Step 4: Calculate Total Daily Expenditure (TDEE)

[1761] Specific server operation: The server calculates total daily exercise expenditure (TDEE) based on the exercise level. For example, if the exercise level is "moderate," it calculates as follows:

[1762] TDEE = BMR 1.55 (for moderate activity level)

[1763] Input: Basal metabolic rate (BMR), exercise level

[1764] Output: Calculated Total Daily Expenditure (TDEE)

[1765] Step 5: Setting a target calorie intake

[1766] Specific server operation: The server sets a target calorie intake based on the user's health goals. For example, if the health goal is weight maintenance, the target calorie intake will be TDEE (Total Daily Excess).

[1767] Input: Total Daily Expenditure (TDEE), Health Goals

[1768] Output: Set target calories

[1769] Step 6: Creating a low-calorie menu

[1770] Server operation: The server uses a generative AI model to generate a week's worth of low-calorie menus based on target calories. Enter an example of a prompt message.

[1771] Example prompt: "Generate a one-week low-calorie menu for a 30-year-old male, weighing 70kg, 175cm tall, with a moderate fitness level and a target calorie intake of 2567.19 kcal."

[1772] Input: Target calories, prompt text

[1773] Output: Generated low-calorie menu for one week

[1774] Step 7: Create and order the ingredients list.

[1775] Specific server operation: Based on the generated menu, the server creates a list of necessary ingredients, checks the supplier's inventory information, and automatically places orders.

[1776] Input: Generated low-calorie menu for one week

[1777] Output: List of required ingredients, ordering information for suppliers

[1778] Step 8: Recipe generation and distribution

[1779] Specific server operation: The server generates recipes based on a week's worth of menus and delivers them to the user's terminal. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[1780] Input: Weekly menu

[1781] Output: Recipe delivered to the device

[1782] (Application Example 1)

[1783] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1784] Many people today find it difficult to plan and maintain healthy eating habits amidst their busy lifestyles. Furthermore, creating specific menus for proper calorie management and maintaining good health is challenging. In particular, the effort required to gather all necessary ingredients at once and maintaining a balanced meal plan are significant obstacles. Therefore, there is a need for a system that calculates calorie targets based on the user's health information and continuously provides low-calorie, balanced menus.

[1785] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1786] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from partner suppliers, means for delivering the generated recipes to the user's terminal, and means for calculating the basal metabolic rate, total calorie expenditure, and target calorie intake based on the user's information, generating a week's worth of low-calorie menus based on these, automatically ordering the necessary ingredients, and delivering the generated recipes to the smartphone. This makes it possible for the user to easily and consistently practice a healthy diet.

[1787] "User health information" refers to information necessary to understand the user's health status, such as age, gender, weight, height, exercise level, and health goals.

[1788] A "calorie target" refers to the amount of calories a user should consume per day, calculated based on their health information.

[1789] A "low-calorie menu" refers to a one-week meal plan that is calculated based on a calorie target and includes a balanced range of nutrients.

[1790] A "food ingredient list" refers to a list of ingredients needed based on the generated low-calorie menu.

[1791] A "supplier" refers to a company that provides the necessary ingredients and works in conjunction with the server to automatically supply those ingredients to the user.

[1792] "Automatic ordering" refers to the system automatically placing orders for necessary ingredients with suppliers without any user intervention.

[1793] A "recipe" refers to a cooking method based on a generated low-calorie menu, including the necessary ingredients, cooking steps, cooking time, and calorie information.

[1794] "Device" refers to information devices used by users, such as smartphones, tablets, and personal computers.

[1795] "Basal metabolic rate (BMR)" refers to a value that calculates the amount of energy a user needs while at rest.

[1796] "Total Daily Energy Expenditure (TDEE)" refers to the total amount of energy needed per day, calculated by taking into account the basal metabolic rate and the level of exercise.

[1797] "Delivering to smartphones" refers to sending generated recipes, menus, and other information from the server to the user's smartphone for display.

[1798] Basic configuration

[1799] This system collects users' health information, calculates calorie targets based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device, enabling users to easily and consistently practice healthy eating.

[1800] Server-side processing

[1801] 1. User Information Collection

[1802] The server receives information such as age, gender, weight, height, exercise level, and health goals entered by the user via their device. This information is necessary to accurately understand the user's health status.

[1803] 2. Calculating your calorie target

[1804] The server calculates the basal metabolic rate (BMR) based on the user's health information. For example, it calculates it using the Mifflin-St. Jeor equation as follows:

[1805] For men: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[1806] For women: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age - 161

[1807] Next, calculate your total daily exercise expenditure (TDEE) based on your activity level. For example, if your activity level is moderate:

[1808] TDEE = BMR 1.55

[1809] Finally, set a target calorie intake according to the user's health goals. For example, if the goal is to maintain weight:

[1810] Target Calories = TDEE

[1811] 3. Creating low-calorie menus

[1812] The server generates a week's worth of low-calorie menus based on your target calorie intake. The generated menus are designed to include a balanced range of nutrients.

[1813] 4. Create ingredient list and automate ordering.

[1814] The server creates a list of necessary ingredients based on the generated menu. It checks the supplier's inventory and automatically orders the necessary ingredients via API.

[1815] 5. Recipe generation and distribution to users

[1816] The server generates recipes based on a week's worth of menus and delivers them to the user's device. The recipes include the necessary ingredients, cooking instructions, cooking time, and calorie information.

[1817] Terminal-side processing

[1818] 1. Provide a user interface.

[1819] The terminal provides an interface that allows the user to input necessary information. It also has the function to display delivered menus and recipes.

[1820] 2. Notification function

[1821] The terminal displays reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions sent from the server.

[1822] User actions

[1823] 1. Information Entry

[1824] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[1825] 2. Check the menu and recipes.

[1826] The user checks a week's worth of menus and recipes displayed on the device and cooks according to the recipes.

[1827] 3. Receiving the ingredients

[1828] The user receives the ingredients on the scheduled date notified by the server.

[1829] Implementation of specific examples

[1830] Initial setup

[1831] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[1832] Calorie setting

[1833] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[1834] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[1835] The target calorie intake for maintaining weight is 2567.19 kcal.

[1836] Menu Generation

[1837] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[1838] Automated ingredient procurement

[1839] Create a list of necessary ingredients and automatically place orders via API.

[1840] Recipe distribution

[1841] The recipe, based on the completed menu, will be sent to the user's device for review.

[1842] Example of a prompt:

[1843] "Development of a food delivery app that calculates basal metabolic rate, total calorie expenditure, and target calorie intake based on user information, generates a week's worth of low-calorie menus based on these calculations, automatically orders the necessary ingredients, and delivers the generated recipes to the user's smartphone."

[1844] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1845] Step 1: Enter and collect user information

[1846] Users use a device to input health information such as their age, gender, weight, height, exercise level, and health goals. The device then sends this input information to a server. This input information serves as the basis for calculating calorie goals and is used to monitor one's health status.

[1847] Step 2: Calculating Basal Metabolic Rate (BMR)

[1848] The server calculates each user's basal metabolic rate (BMR) based on the user's health information received in Step 1. Specifically, it calculates the BMR using the Mifflin-St. Jeor equation. For example, for a male:

[1849] Input: weight, height, age, gender

[1850] Data calculation: BMR = 10 Weight (kg) + 6.25 Height (cm) - 5 Age + 5

[1851] Output: BMR value

[1852] Step 3: Calculate Total Daily Expenditure (TDEE)

[1853] The server calculates the total daily exercise expenditure (TDEE) by adding the exercise level to the basal metabolic rate (BMR) calculated in step 2. For example, if the exercise level is moderate:

[1854] Input: Basal metabolic rate, exercise level

[1855] Data calculation: TDEE = BMR 1.55

[1856] Output: TDEE value

[1857] Step 4: Setting a target calorie intake

[1858] The server sets a target calorie intake based on the user's health goals (weight loss, maintenance, weight gain, etc.). For example, if the goal is weight maintenance:

[1859] Input: Total calories burned, health goal

[1860] Data calculation: Target calories = TDEE

[1861] Output: Target calorie value

[1862] Step 5: Creating a low-calorie menu

[1863] Based on the target calorie intake obtained in step 4, the server generates a week's worth of low-calorie menus. The generated menus are designed to include a balanced range of nutrients. Specifically, the server selects appropriate recipes from the database and constructs the menu.

[1864] Input: Target calories

[1865] Data calculation: Execute menu optimization algorithm

[1866] Output: A week's worth of low-calorie menus

[1867] Step 6: Create a food list and automate ordering.

[1868] The server creates a list of necessary ingredients based on the menu generated in step 5. It then automatically places orders for the ingredients with partner suppliers via API.

[1869] Input: Low-calorie menu

[1870] Data processing: Create ingredient lists and send requests to supplier APIs.

[1871] Output: Ingredient list, confirmation of automatic ordering

[1872] Step 7: Recipe generation and distribution

[1873] The server generates detailed recipes based on a week's worth of menus. The generated recipes are delivered to the user's device, where the user can check the necessary ingredients, cooking instructions, cooking time, calorie information, and more.

[1874] Input: Low-calorie menu

[1875] Data processing: Execute the recipe generation algorithm.

[1876] Output: Recipe delivery to user terminals

[1877] Example of a prompt:

[1878] "Development of a food delivery app that calculates basal metabolic rate, total calorie expenditure, and target calorie intake based on user information, generates a week's worth of low-calorie menus based on these calculations, automatically orders the necessary ingredients, and delivers the generated recipes to the user's smartphone."

[1879] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1880] Basic configuration

[1881] This system collects the user's health information, calculates a calorie target based on that information, and generates a week's worth of low-calorie menus. It also automatically orders necessary ingredients from suppliers and delivers the generated recipes to the user's device. Furthermore, it incorporates an emotion engine that recognizes the user's emotions, adjusting the menus and recipes based on those emotions.

[1882] Server-side processing

[1883] 1. User Information Collection

[1884] The user uses their device to input their age, gender, weight, height, exercise level, and health goals. The device then sends this information to the server.

[1885] 2. Calculating your calorie target

[1886] The server calculates the basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on the user's health information, and sets a target calorie intake based on their health goals.

[1887] 3. Creating low-calorie menus

[1888] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[1889] 4. Create a list of ingredients and place orders with suppliers.

[1890] The server creates a list of necessary ingredients based on the generated menu and automatically places orders with suppliers.

[1891] 5. Recipe generation and distribution to users

[1892] The server creates detailed recipes based on a week's worth of menus and delivers them to the user's device. The recipes include necessary ingredients, cooking instructions, cooking time, and calorie information.

[1893] 6. Emotion recognition by an emotion engine

[1894] The user inputs emotions using a device. The emotion engine recognizes this input and records the user's current emotional state.

[1895] 7. Adjusting the menu and recipes

[1896] The server adjusts menus and recipes based on the recognized user's emotions. For example, it suggests a menu using ingredients that promote relaxation for a user who is feeling stressed.

[1897] 8. Utilizing past emotional data

[1898] The server analyzes past sentiment data to optimize the menu for the following week. It learns the user's sentiment patterns and provides the most suitable menu and recipes.

[1899] Terminal-side processing

[1900] 1. Provide a user interface.

[1901] The device provides an interface that allows users to input necessary information. It also has functions for receiving delivered menus and recipes, and for inputting emotions.

[1902] 2. Notification function

[1903] The terminal displays reminder notifications from the server regarding the expected arrival date of ingredients and cooking instructions, as well as requests for emotional input.

[1904] User actions

[1905] 1. Information Entry

[1906] Users enter their age, gender, weight, height, exercise level, and health goals using their device.

[1907] 2. Emotional Input

[1908] Users periodically input their emotional state into the device. The emotion engine recognizes this data and sends it to the server.

[1909] 3. Check the menu and recipes.

[1910] The user checks the menu and recipes for the week displayed on the device and cooks according to the recipes.

[1911] 4. Receiving the ingredients

[1912] The user receives the ingredients on the scheduled date notified by the server.

[1913] 5. Receive and adjust next week's menu.

[1914] At the end of each week, users receive a menu optimized for the following week based on their emotional data.

[1915] Implementation of specific examples

[1916] Initial setup

[1917] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[1918] Calorie setting

[1919] The basal metabolic rate (BMR) is calculated as 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal.

[1920] Total daily energy expenditure (TDEE) is calculated as BMR 1.55 = 1656.25, so 1.55 ≈ 2567.19 kcal.

[1921] The target calorie intake for maintaining weight is 2567.19 kcal.

[1922] Menu Generation

[1923] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[1924] Emotional input and adjustment

[1925] If a user is experiencing work-related stress, the emotion engine recognizes this and provides a menu containing ingredients effective in reducing stress. Furthermore, the menu for the following week is optimized based on the user's emotional history.

[1926] Automated ingredient procurement

[1927] Create a list of necessary ingredients and automatically place orders with suppliers.

[1928] Recipe distribution

[1929] The recipe, based on the completed menu, will be sent to the user's device for review.

[1930] In this way, users can easily implement a consistent low-calorie diet based on their health information and emotional state.

[1931] The following describes the processing flow.

[1932] Step 1:

[1933] The user accesses the system's registration screen using their device and enters personal information such as age, gender, weight, height, exercise level, and health goals.

[1934] Step 2:

[1935] The terminal sends the user's entered personal information to the server.

[1936] Step 3:

[1937] The server registers the received user information in the database.

[1938] Step 4:

[1939] The server calculates the basal metabolic rate (BMR) for each user. The calculation method is based on gender, age, weight, and height.

[1940] Step 5:

[1941] The server calculates total daily exercise expenditure (TDEE) based on the exercise level. The calculation is performed using a coefficient that corresponds to the exercise level.

[1942] Step 6:

[1943] The server sets target calories according to the user's health goals (weight loss, maintenance, or weight gain).

[1944] Step 7:

[1945] The server generates a week's worth of low-calorie menus based on your target calorie intake, taking into account nutritional balance and meal variety.

[1946] Step 8:

[1947] The server creates a list of necessary ingredients based on the generated menu.

[1948] Step 9:

[1949] The server checks the inventory information of food suppliers and places orders for the necessary ingredients.

[1950] Step 10:

[1951] The server creates detailed recipes based on a week's worth of menus. The recipes include required ingredients, cooking instructions, cooking time, and calorie information.

[1952] Step 11:

[1953] The server delivers the generated recipe to the user's device.

[1954] Step 12:

[1955] The device displays the received recipes and menus to the user.

[1956] Step 13:

[1957] The device sends reminder notifications to the user regarding the expected arrival date of ingredients and cooking instructions.

[1958] Step 14:

[1959] The user cooks according to the recipe displayed on their device.

[1960] Step 15:

[1961] Users periodically input their emotional state into the device. For example, their emotional state might be "feeling stressed" or "feeling relaxed."

[1962] Step 16:

[1963] The device sends the entered emotional state information to the server.

[1964] Step 17:

[1965] The server uses an emotion engine to analyze and record the user's emotional state.

[1966] Step 18:

[1967] The server adjusts menus and recipes based on the user's emotional state. For example, if a user is feeling stressed, it will offer a menu using ingredients that have a relaxing effect.

[1968] Step 19:

[1969] The server analyzes past sentiment data to optimize the menu for the following week. It learns the user's sentiment patterns and provides the most suitable menu and recipes.

[1970] Step 20:

[1971] The server delivers the next week's menu and adjusted recipes to the user's device.

[1972] Step 21:

[1973] Users receive new menus and recipes every week, encouraging them to consistently practice healthy eating.

[1974] (Example 2)

[1975] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1976] In modern society, individual health management is a crucial issue, and improving dietary habits is particularly essential for maintaining and promoting health. However, it is difficult for many people to design and follow an optimal meal plan based on their own health condition. Furthermore, preparing the necessary ingredients and implementing appropriate recipes in a busy daily life requires considerable time and effort. Moreover, the influence of a user's emotional state on food choices cannot be ignored. This invention aims to solve these problems and provide individual users with an effective and easy-to-use means of health management.

[1977] The identification processing performed by the identification 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 collecting the user's health information, means for calculating a calorie target based on the collected health information, means for generating a week's worth of low-calorie menus, means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from suppliers, means for delivering the generated recipes to the user's terminal, and means for collecting emotional information and adjusting the menus and recipes based on it. As a result, the user can easily obtain meal menus optimized for their individual health condition and efficiently prepare the necessary ingredients and execute the recipes. In addition, the provision of appropriate menus according to the user's emotional state is realized, promoting comprehensive health management.

[1978] "User health information" refers to information about the user's health status and lifestyle, such as the user's age, gender, weight, height, exercise level, and health goals.

[1979] A "calorie target" is the appropriate amount of energy a user should consume per day to achieve their health goals.

[1980] A "low-calorie menu" is a balanced meal plan for one week, tailored to the user's calorie goals.

[1981] The "list of required ingredients" is a list of ingredients needed to execute a given menu, based on the generated menu.

[1982] A "supplier" is a company or organization that is responsible for providing users with the necessary ingredients.

[1983] A "recipe" is information that describes the detailed steps and necessary ingredients for cooking a specific dish.

[1984] "Emotional information" refers to data that represents the user's emotional state, and this is used to adjust menus and recipes.

[1985] An "emotion engine" is a system that recognizes the user's emotional input and determines their current emotional state.

[1986] Basal metabolic rate (BMR) is an indicator that shows the amount of energy a user consumes while at rest.

[1987] Total Daily Energy Expenditure (TDEE) is an indicator that shows the total amount of energy a user consumes in a day, and is calculated by adding the energy consumed through exercise and daily activities to the basal metabolic rate.

[1988] A "generative AI model" is an artificial intelligence model that automatically generates menus and recipes based on user input.

[1989] This invention is a system that calculates a calorie target based on the user's health and emotional information, and generates and delivers a week's worth of low-calorie menus. The following hardware and software are used to implement this invention.

[1990] The server and terminals exchange data over the internet. The server runs on the Linux operating system and executes Python programs. The server uses MySQL as its database and utilizes common AI models for generating AI models.

[1991] Users input health and emotional information using their devices (e.g., smartphones, tablets, PCs). The device software is developed using the React Native framework and provides notification functionality using Firebase Cloud Messaging.

[1992] The server receives health information sent by the user and calculates the calorie target using the Python NumPy library. For example, for a 30-year-old male, weighing 70kg, 175cm tall, and with an exercise level of "medium," the calculation would be as follows:

[1993] Basal metabolic rate (BMR): 10 70 + 6.25 175 - 5 30 + 5 = 1656.25 kcal

[1994] Total Daily Energy Expenditure (TDEE): BMR 1.55 = 1656.25 1.55 ≈ 2567.19 kcal

[1995] After calculation, the server uses a generating AI model (e.g., OpenAI's GPT-3) to send a prompt message like the following to generate a week's worth of low-calorie menus:

[1996] "The user's target calorie intake is 2567 kcal. Please generate a balanced 1-week meal plan."

[1997] Based on the generated menu, the server uses the Pandas library to create a list of necessary ingredients and automatically places orders for them. It uses the AWS API to place orders with suppliers and notifies the terminal of the order confirmation.

[1998] Detailed recipes are generated by sending the following prompt to the generative AI model:

[1999] "Please generate a detailed recipe based on this menu."

[2000] The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information, which is sent to the device in JSON format and displayed on the user interface.

[2001] Furthermore, when emotional information is entered by the user, the server analyzes it using the Microsoft Azure Emotion API and saves the results to a database. The system adjusts menus and recipes based on this emotional information. If the user enters that they are feeling stressed, the server instructs the generating AI model to "adjust the menu to one with a relaxing effect."

[2002] Based on past sentiment data, the server optimizes the menu for the following week. The server analyzes past sentiment data using Scikit-learn's machine learning algorithm and prompts the generative AI model to "generate the optimal menu based on the user's sentiment patterns."

[2003] In this way, the system can automatically suggest and implement the optimal meal plan based on the user's health and emotional information.

[2004] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2005] Step 1:

[2006] Users input health information using their devices. Specifically, they enter their age, gender, weight, height, exercise level, and health goals on an input screen. The entered data is sent to the server in JSON format.

[2007] Input: Data entered by the user, including age, gender, weight, height, exercise level, and health goals.

[2008] Output: User health information data in JSON format is sent to the server.

[2009] Step 2:

[2010] The server uses the NumPy library in Python to calculate the calorie target based on the received health information data.

[2011] Input: Health information data in JSON format submitted in Step 1.

[2012] Output: Basal metabolic rate (BMR) and total daily energy expenditure (TDEE) values.

[2013] As a concrete example, perform the following calculation:

[2014] Basal metabolic rate (BMR): 10 Weight (kg) + 6.25 Height (cm) - 5 Age (years) + 5 (for males)

[2015] Total Daily Expenditure (TDEE): BMR (Body Mass Index)

[2016] Step 3:

[2017] The server uses a generative AI model to send the following prompt to generate a week's worth of low-calorie menus: "The user's target calorie intake is 2567 kcal. Please generate a balanced week's worth of menus."

[2018] Input: Basal metabolic rate (BMR) and total daily energy expenditure (TDEE).

[2019] Output: A week's worth of low-calorie menus generated.

[2020] As a concrete example, a prompt message is sent to a generative AI model, and the generated text-formatted menu is converted to JSON format and saved.

[2021] Step 4:

[2022] The server creates a list of required ingredients based on the generated menu. It uses the Pandas library to generate a dataframe and list the required ingredients.

[2023] Input: A week's worth of low-calorie menus generated by a generative AI model.

[2024] Output: List of required ingredients.

[2025] As a concrete example, the ingredient information extracted from the menu is listed and saved in JSON format.

[2026] Step 5:

[2027] The server uses the AWS API to automatically place orders with suppliers based on the required ingredient list. It then notifies the terminal of the order confirmation.

[2028] Input: List of required ingredients.

[2029] Output: Order confirmation notification to the supplier.

[2030] As a concrete example, a POST request is sent to an AWS API endpoint, and an acknowledgment is received from the supplier.

[2031] Step 6:

[2032] The server sends a prompt to the generating AI model to generate a detailed recipe: "Generate a detailed recipe based on this menu."

[2033] Input: A week's worth of low-calorie menus.

[2034] Output: Detailed recipe.

[2035] The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information, and is sent to the device in JSON format.

[2036] Step 7:

[2037] The user inputs emotional information through their device. They select their current emotional state on the emotional input screen, and the data is sent to the server.

[2038] Input: User sentiment information.

[2039] Output: Sentiment information data in JSON format is sent to the server.

[2040] Step 8:

[2041] The server analyzes emotional information using the Microsoft Azure Emotion API and stores the results in a database.

[2042] Input: Sentiment information data in JSON format.

[2043] Output: Analyzed emotional state data.

[2044] As a concrete example, emotion data is sent to the Emotion API, the analysis results are obtained, and the data is saved in a database.

[2045] Step 9:

[2046] The server sends a prompt to the AI ​​model based on past sentiment data to optimize the menu for the following week: "Generate the optimal menu based on the user's sentiment patterns."

[2047] Input: Analyzed historical sentiment data.

[2048] Output: Optimized menu for next week.

[2049] As a concrete example, past emotional patterns are analyzed using Scikit-learn, and an optimized menu is generated by sending prompt sentences to a generative AI model.

[2050] (Application Example 2)

[2051] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2052] Conventional meal management systems can calculate calories and generate menus based on a user's health information, but they cannot manage meals while considering the user's emotional state. Therefore, it was difficult to provide appropriate menus when users were experiencing stress or fatigue. Furthermore, the lack of a mechanism to optimize meals based on emotional state made it difficult to improve the user's mental satisfaction. Therefore, the present invention aims to provide a system that integrates the management of a user's health information and emotional state to provide more individualized and optimal meal menus.

[2053] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[2054] In this invention, the server includes means for collecting the user's health information, means for calculating a calorie target based on the collected health information, and means for generating a week's worth of low-calorie menus. To this end, it further includes means for creating a list of necessary ingredients based on the generated menus, means for automatically ordering ingredients from partner suppliers, means for delivering the generated recipes to the user's terminal, means for collecting the user's emotional state and adjusting the menus based on this, and means for optimizing the menus for the following week using past emotional data. This makes it possible to provide personalized and optimal meal menus that take the user's emotional state into consideration.

[2055] "User health information" refers to information related to an individual's health status and lifestyle, such as the user's age, gender, weight, height, exercise level, and health goals.

[2056] A "calorie target" refers to the appropriate amount of calories a user should consume daily to achieve a specific health goal, based on their basal metabolic rate and total calorie expenditure.

[2057] A "low-calorie menu" refers to a set of meal plans designed to help users meet their health goals while keeping their calorie intake low.

[2058] A "food ingredient list" refers to a list of specific ingredients needed for cooking, based on the generated menu.

[2059] "Supplier" refers to a business or service that sells or provides food ingredients that users need.

[2060] "Automated ordering" refers to the process by which a system automatically places orders for ingredients with suppliers.

[2061] A "recipe" is a document that describes the detailed steps for preparing a specific dish, including ingredients, cooking instructions, cooking time, and calorie information.

[2062] "Emotional state" refers to the emotions and psychological state that a user is experiencing. For example, it can refer to states such as stress, fatigue, or happiness.

[2063] "Menu adjustment" refers to the process of optimizing or modifying existing menus, taking into account the user's current emotional state.

[2064] "Emotional data" refers to historical information about emotional states collected through user input or emotion recognition systems.

[2065] Modes for carrying out the invention

[2066] Basic configuration

[2067] This system automatically generates and manages meal plans based on the user's health information and emotional state. Specifically, it collects and analyzes the user's health information, sets calorie targets, generates menus, automatically orders ingredients, and adjusts menus to take the user's emotional state into consideration, thereby providing a system that meets the individual needs of the user.

[2068] Server-side processing

[2069] 1. User Information Collection

[2070] Users use their devices to input health information such as their age, gender, weight, height, exercise level, and health goals. This information is transmitted to the server in real time and stored in a database.

[2071] 2. Calculating your calorie target

[2072] The server calculates the basal metabolic rate (BMR) and total daily allowance (TDEE) based on the received health information, and sets individual calorie targets. By using Python and Flask for these calculations, highly accurate results are provided in real time.

[2073] 3. Creating low-calorie menus

[2074] Using a generative AI model, a week's worth of low-calorie menus are generated based on calculated calorie targets. The generated menus take into account nutritional balance and variety of ingredients, contributing to the user's health maintenance.

[2075] 4. Create a list of ingredients and place orders with suppliers.

[2076] The server creates a list of necessary ingredients based on the generated menu. Based on this list, it automatically places orders with partner suppliers. The ingredient list is sent in JSON format, and suppliers procure and deliver the ingredients accordingly.

[2077] 5. Recipe generation and distribution to users

[2078] The server creates a detailed recipe based on the menu and delivers it to the user's device. This recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information. The user then cooks according to the recipe.

[2079] 6. Emotion recognition by an emotion engine

[2080] Users periodically input their emotional state into their device. The emotion engine recognizes this data and sends it to the server. Based on this information, the server stores the user's emotional state in a database and performs analysis.

[2081] 7. Adjusting the menu and recipes

[2082] The server adjusts menus and recipes based on the recognized emotional state. For example, if a user is feeling stressed, it suggests a menu using ingredients with relaxing properties. Python and Flask are used to analyze emotional data and adjust menus in real time.

[2083] 8. Utilizing past emotional data

[2084] The server analyzes past emotional data and learns the user's emotional patterns to optimize the menu for the following week. It uses a generative AI model to provide menus based on emotional patterns.

[2085] Terminal-side processing

[2086] 1. Provide a user interface.

[2087] The device provides an interface that allows users to input necessary information. It also has functions for receiving delivered menus and recipes, and for inputting emotions.

[2088] 2. Notification function

[2089] The terminal displays reminder notifications from the server regarding the expected arrival date of ingredients and cooking instructions, as well as requests for emotional input.

[2090] Implementation of specific examples

[2091] Initial setup

[2092] If a user is 30 years old, male, weighs 70 kg, is 175 cm tall, has a moderate exercise level, and aims to maintain their weight, they will enter their user information into the terminal.

[2093] Calorie setting

[2094] The basal metabolic rate (BMR) is 1070 + 6.25 / 175 - 530 + 5 = 1656.25 kcal. The total daily allowance (TDEE) is BMR 1.55 = 1656.25 / 1.55 ​​≈ 2567.19 kcal. The target calorie intake for weight maintenance is 2567.19 kcal.

[2095] Menu Generation

[2096] Based on your target calorie intake, it generates a balanced, low-calorie menu for one week.

[2097] Emotional input and adjustment

[2098] If a user is experiencing work-related stress, the emotion engine recognizes this and provides a menu containing ingredients effective in reducing stress. Furthermore, the menu for the following week is optimized based on the user's emotional history.

[2099] Example of a prompt

[2100] When generating recipes or menus using a generative AI model, use the following prompts.

[2101] The user is 30 years old, male, weighs 70kg, and is 175cm tall. Their exercise level is moderate, and their goal is to maintain their weight. Please calculate calories and generate a one-week low-calorie meal plan for this user. Also, considering the user's stress levels, please include ingredients that are effective in reducing stress in the meal plan.

[2102] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2103] Step 1:

[2104] Collection of user information

[2105] The terminal displays an interface for the user to enter their health information (age, gender, weight, height, exercise level, and health goals). Once the user enters this information and presses the submit button, the terminal sends the entered information to the server. The entered data is sent to the server in JSON format, and the server stores it in its database.

[2106] Step 2:

[2107] Calculating calorie targets

[2108] The server calculates the basal metabolic rate (BMR) and total daily energy expenditure (TDEE) based on the user information received. Specifically, the BMR is calculated using the following formula: BMR = 10 / weight (kg) + 6.25 / height (cm) - 5 / age (years) + gender (5 for males, -161 for females). Next, the TDEE is calculated by multiplying the BMR by the exercise level. After calculation, the server stores the daily target calories in the database.

[2109] Step 3:

[2110] Creating low-calorie menus

[2111] The server uses a generative AI model to generate a week's worth of low-calorie menus based on a set calorie goal. The generative AI model is given the following prompt as input: "The user is 30 years old, male, weighs 70 kg, and is 175 cm tall. Their exercise level is moderate, and their goal is to maintain their weight. For this user, please calculate calories and generate a week's worth of low-calorie menus." Based on the prompt, the generative AI model generates the menus and outputs them to the server. The server saves the generated menus to its database.

[2112] Step 4:

[2113] Creating ingredient lists and automated ordering.

[2114] The server creates a list of necessary ingredients based on the generated menu. Specifically, it analyzes each menu item and lists the required ingredients and their quantities. This list is generated in JSON format, and the server automatically places an order with its partner suppliers. The order data is sent to the supplier's API.

[2115] Step 5:

[2116] Recipe generation and distribution

[2117] The server creates a detailed recipe based on the menu. The recipe includes the necessary ingredients, cooking instructions, cooking time, and calorie information. This is then delivered to the user's device. The device provides an interface for the user to view the recipe and sends reminders via notifications.

[2118] Step 6:

[2119] Emotional input and recognition

[2120] Users periodically input their emotional state using a device. The entered emotional data is sent from the device to the server. The server analyzes this data using an emotion engine and stores the user's current emotional state in a database.

[2121] Step 7:

[2122] Adjustments to the menu and recipes

[2123] The server adjusts menus and recipes based on the recognized user's emotions. For example, if the user is feeling stressed, it will suggest a menu using ingredients that have a relaxing effect. The server uses a generative AI model to generate new menus and delivers them to the user's device.

[2124] Step 8:

[2125] Utilizing past emotional data

[2126] The server analyzes past emotional data and learns the user's emotional patterns. It then makes suggestions that take these emotional patterns into account for future menu optimization. The server saves the generated optimized menu to a database and delivers it to the user's device.

[2127] In this way, a series of processes involving the server, terminal, and user provides a personalized meal menu based on the user's health information and emotional state.

[2128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2129] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2130] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2131] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2132] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[2133] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[2134] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[2135] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[2136] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[2137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[2138] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[2139] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[2140] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[2141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2142] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[2143] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[2144] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[2145] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[2146] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[2147] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[2148] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[2149] The following is further disclosed regarding the embodiments described above.

[2150] (Claim 1)

[2151] Means for collecting user health information,

[2152] A means of calculating calorie targets based on collected health information,

[2153] A method for generating a week's worth of low-calorie menus,

[2154] A means of creating a list of necessary ingredients based on the generated menu,

[2155] A method for automatically ordering ingredients from partner suppliers,

[2156] A means of delivering the generated recipe to the user's device,

[2157] A system that includes this.

[2158] (Claim 2)

[2159] The system according to claim 1, further comprising means for calculating basal metabolic rate and total calories burned based on exercise level and health goals.

[2160] (Claim 3)

[2161] The system according to claim 1, further comprising means for including the ingredients, cooking procedure, cooking time, and calorie information required for the generated recipe.

[2162] (Claim 4)

[2163] The system according to claim 1, further comprising means for sending a reminder notification of the expected arrival date of ingredients and cooking instructions to a terminal used by the user.

[2164] "Example 1"

[2165] (Claim 1)

[2166] Means for collecting user health information,

[2167] A means of calculating calorie targets based on collected health information,

[2168] A means of generating a week's worth of low-calorie menus based on a target calorie intake,

[2169] A means of creating a list of necessary ingredients based on the generated menu,

[2170] A means of automatically ordering ingredients from suppliers,

[2171] A means of delivering the generated recipe to the user's device,

[2172] A system that includes this.

[2173] (Claim 2)

[2174] The system according to claim 1, further comprising means for calculating basal metabolic rate and total calories burned based on exercise level and health goals.

[2175] (Claim 3)

[2176] The system according to claim 1, further comprising means for including the ingredients, cooking procedure, cooking time, and calorie information required for the generated recipe.

[2177] "Application Example 1"

[2178] (Claim 1)

[2179] Means for collecting user health information,

[2180] A means of calculating calorie targets based on collected health information,

[2181] A method for generating a week's worth of low-calorie menus,

[2182] A means of creating a list of necessary ingredients based on the generated menu,

[2183] A method for automatically ordering ingredients from partner suppliers,

[2184] A means of delivering the generated recipe to the user's device,

[2185] A method for calculating basal metabolic rate, total calorie expenditure, and target calorie intake based on user information, generating a week's worth of low-calorie menus based on these, automatically ordering necessary ingredients, and delivering the generated recipes to a smartphone.

[2186] A system that includes this.

[2187] (Claim 2)

[2188] The system according to claim 1, further comprising means for calculating basal metabolic rate and total calories burned based on exercise level and health goals, and for automatically ordering food ingredients via API.

[2189] (Claim 3)

[2190] The system according to claim 1, further comprising means for delivering to a smartphone application the generated recipe, which includes the necessary ingredients, cooking procedure, cooking time, and calorie information.

[2191] "Example 2 of combining an emotion engine"

[2192] (Claim 1)

[2193] Means for collecting user health information,

[2194] A means of calculating calorie targets based on collected health information,

[2195] A method for generating a week's worth of low-calorie menus,

[2196] A means of creating a list of necessary ingredients based on the generated menu,

[2197] A method for automatically ordering ingredients from suppliers,

[2198] A means of delivering the generated recipe to the user's device,

[2199] A means of collecting emotional information and adjusting menus and recipes based on it,

[2200] A system that includes this.

[2201] (Claim 2)

[2202] The system according to claim 1, further comprising means for calculating basal metabolic rate and total calories burned based on exercise level and health goals.

[2203] (Claim 3)

[2204] The system according to claim 1, further comprising means for including the ingredients, cooking procedure, cooking time, and calorie information required for the generated recipe.

[2205] "Application example 2 of combining emotional engines"

[2206] (Claim 1)

[2207] Means for collecting user health information,

[2208] A means of calculating calorie targets based on collected health information,

[2209] A method for generating a week's worth of low-calorie menus,

[2210] A means of creating a list of necessary ingredients based on the generated menu,

[2211] A method for automatically ordering ingredients from partner suppliers,

[2212] A means of delivering the generated recipe to the user's device,

[2213] A means of collecting the user's emotional state and adjusting the menu based on it,

[2214] A method to optimize the menu for the following week using past emotional data,

[2215] A system that includes this.

[2216] (Claim 2)

[2217] The system according to claim 1, further comprising means for calculating basal metabolic rate and total calories burned based on exercise level and health goals.

[2218] (Claim 3)

[2219] The system according to claim 1, further comprising means for including the ingredients, cooking procedure, cooking time, and calorie information required for the generated recipe. [Explanation of Symbols]

[2220] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting user health information, A means of calculating calorie targets based on collected health information, A method for generating a week's worth of low-calorie menus, A means of creating a list of necessary ingredients based on the generated menu, A method for automatically ordering ingredients from partner suppliers, A means of delivering the generated recipe to the user's device, A system that includes this.

2. The system according to claim 1, further comprising means for calculating basal metabolic rate and total calories burned based on exercise level and health goals.

3. The system according to claim 1, further comprising means for including the ingredients, cooking procedure, cooking time, and calorie information required for the generated recipe.

4. The system according to claim 1, further comprising means for sending a reminder notification of the expected arrival date of ingredients and cooking instructions to a terminal used by the user.

Images