System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to enter information regarding personal food preferences, allergy information, health status, target nutrients, flavors, looks, textures, and cooking methods; and means for receiving and transmitting the entered information to a server in real time; and means for the server to analyze the received information, generate an optimal cooking profile for the user using AI, and store it in a database.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] While modern eating habits offer a wide variety of ingredients and cooking methods, it is difficult to provide meals that perfectly suit an individual's health condition and food preferences. Furthermore, the appearance and flavor of special ingredients such as insects can be a challenge, making their consumption difficult to popularize. Furthermore, many people find it difficult to choose meals that take their preferences and nutritional balance into account when eating out or ordering food delivery. These challenges reduce consumer satisfaction and hinder the achievement of a healthy diet. [Means for solving the problem]

[0005] The present invention provides optimal dishes tailored to individual preferences and health conditions through a system that includes a means for users to input information about their food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method; a terminal means for receiving the input information in real time and sending it to a server; and a server means for analyzing the received data, using AI to generate an optimal dish profile for the user, and storing it in a database. The system also includes a server means for sending the user's order details to a server and generating optimal cooking methods and portion sizes based on the user's profile data, and a terminal means for displaying the data transmitted from the server on a VR device and improving the appearance, thereby reducing the visual irritation of special ingredients such as insects and providing a delicious and enjoyable dining experience. Furthermore, the system also includes a server means for transmitting the generated dish data to a kitchen or affiliated restaurant and requesting cooking, a terminal means for displaying the cooking process and delivery status to the user in real time, and a terminal means for the user to track the waiting time for the dish and provide ratings and feedback after it arrives, thereby achieving a seamless and individually optimized dining experience.

[0006] "User" refers to an individual who uses the system to order and experience individually optimized meals.

[0007] "Ingredient preferences" refers to the user's preferences for specific ingredients, and includes information about preferred ingredients and ingredients to avoid.

[0008] "Allergy information" refers to information on whether a user has an allergic reaction to a particular food ingredient.

[0009] "Health status" refers to the user's physical condition and health status, including medical information and precautions regarding the intake of certain foods.

[0010] "Target nutrients" refers to the amount and balance of nutrients that a user should set as their daily intake goal.

[0011] "Seasoning" refers to the cooking method and combination of seasonings that the user prefers to use to adjust the flavor of the dish.

[0012] "Appearance" refers to the user's preference regarding the appearance of the dish, and includes visual elements such as color and arrangement.

[0013] "Texture" refers to a user's preference regarding the feel or texture of food when it is put into the mouth.

[0014] "Cooking method" is a general term for the specific steps and techniques used in preparing a dish.

[0015] "Input means" refers to an interface or device that allows a user to input information about their preferences and health status into the system.

[0016] "Terminal means" refers to a device that receives input information in real time and transmits it to a server.

[0017] "Server" refers to a computer system that analyzes information received from users and generates a dish profile.

[0018] "AI" refers to artificial intelligence that uses machine learning and data analysis techniques to generate a profile of the best dish for each user.

[0019] A "food profile" refers to a collection of information about optimal dishes based on a user's preferences and health status.

[0020] "VR device" refers to a device that uses virtual reality technology to visually improve the appearance of food.

[0021] "Kitchen" refers to the facility or place where food is actually prepared.

[0022] "Affiliated restaurant" refers to a restaurant that cooperates with the system to provide food to users.

[0023] "Delivery status" refers to information about the current location and progress of the ordered food until it is delivered to the user. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0026] First, the terms used in the following description will be explained.

[0027] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0028] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0029] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0030] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0031] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0042] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] MODE FOR CARRYING OUT THE INVENTION

[0046] This invention provides a system that links users, devices, and servers to provide individually optimized meals. This system proposes and serves optimal meals based on the user's preferences and health status. Furthermore, by using VR technology to improve the visual experience, users can enjoy meals with unusual ingredients without any hesitation.

[0047] Below, the program processing of this system will be explained in natural language, with concrete examples.

[0048] User information registration and analysis

[0049] 1. User: Logs in to the system and enters information about their food preferences, allergies, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method.

[0050] Example: User A starts a smartphone app and answers questions, entering information such as "My favorite food is chicken," "I like spicy food," and "I prefer low-fat, high-protein meals."

[0051] 2. Terminal: Receives information entered by the user in real time and sends it to the server.

[0052] Example: Once the user has completed entering the data, the device automatically encrypts the data and sends it to the server.

[0053] 3. Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database.

[0054] Example: The server analyzes user A's information, generates a recommendation for "spicy grilled chicken," and saves it as a profile.

[0055] User Orders

[0056] 4. User: Logs into the system and places a food order.

[0057] Example: User A enters "I want spicy grilled chicken" into the app and presses the order button.

[0058] 5. Terminal: Sends the user's order details to the server.

[0059] Example: The device links user A's order information and profile and sends a request to the server.

[0060] 6. Server: Generates optimal cooking methods and portion sizes based on the user's profile data, and also checks the availability of necessary ingredients.

[0061] Example: A server generates a specific recipe and measurements for cooking "spicy grilled chicken" and checks availability.

[0062] Improving appearance with VR technology

[0063] 7. Server: When using special ingredients such as insects, data is generated to correct visually undesirable aspects using VR technology.

[0064] Example: The server generates a 3D model to make "insect steak" look like beef steak, and creates it as VR data.

[0065] 8. Terminal: Displays the data sent from the server on a device such as a VR headset.

[0066] Example: A VR device displays a retouched image of a real dish, showing user A a visually enhanced beef steak.

[0067] 9. Users: They can see the improved appearance of ingredients through the VR device and enjoy eating without any hesitation.

[0068] Example: User A puts on a VR headset and eats "insect steak" while watching a beautified image, and enjoys the meal without any resistance.

[0069] Food delivery and tracking

[0070] 10. Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking.

[0071] Example: A server sends order data and cooking instructions for "spicy grilled chicken" to a partner restaurant.

[0072] 11. Terminal: Displays information to the user to track the food preparation process and delivery status in real time.

[0073] Example: The device notifies user A in real time that food is being cooked or that the delivery person has departed.

[0074] 12. Users: Track how long they wait for their order to arrive and provide a rating and feedback upon completion.

[0075] Example: User A receives a meal and rates it "very delicious" on the app.

[0076] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual enhancement, and serving.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] User: Logs in to the system and enters information about their food preferences, allergy information, health status, target nutrients, preferred seasoning, appearance, texture, and cooking method. Specifically, the user responds to questions in the application by operating the system to enter specific preferences and health information, such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[0080] Step 2:

[0081] Terminal: Receives information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[0082] Step 3:

[0083] Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, the server runs an algorithm to generate the appropriate cooking type and cooking method based on information about food preferences and health status, generating a recommended cooking profile such as "spicy grilled chicken."

[0084] Step 4:

[0085] User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[0086] Step 5:

[0087] Terminal: Sends the user's order details to the server. Specifically, it sends the order details of the dishes selected by the user along with the profile data to the server.

[0088] Step 6:

[0089] Server: Generates optimal cooking methods and quantities based on the user's profile data. It also references the recipe database and checks the availability of ingredients. Specifically, it generates specific cooking instructions and quantities for "spicy grilled chicken" and connects with the inventory management system of partner restaurants to check the ingredients.

[0090] Step 7:

[0091] Server: When special ingredients such as insects are included in the VR content, AI is used to generate data to correct visually undesirable aspects of the content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[0092] Step 8:

[0093] Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[0094] Step 9:

[0095] Users can visually enjoy the improved appearance of food through a VR device. Specifically, users can wear a VR headset and enjoy eating the actual food while looking at the beautified food, allowing them to enjoy their meal without experiencing any visual discomfort.

[0096] Step 10:

[0097] Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[0098] Step 11:

[0099] Device: The device tracks the cooking process and delivery status in real time and displays them to the user. Specifically, the device displays information about the cooking process and the delivery person's location on the application to notify the user.

[0100] Step 12:

[0101] User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[0102] Example 1

[0103] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0104] Conventional meal recommendation systems have difficulty providing individually optimized meals based on the user's preferences and health status, and they also lack the means to improve visually unfavorable ingredients using VR technology. Therefore, there is a need for a system that allows users to enjoy healthy and optimal meals without any resistance.

[0105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0106] In this invention, the server includes a means for a user to input information about personal food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method, a terminal means for receiving the input information in real time and sending it to the server, and a means for analyzing the received data and using a generative AI model to generate a cooking profile optimal for the user and store it in a database. This individually optimizes the user's dining experience and enables more satisfying meal suggestions, including visual improvements.

[0107] A "user" is an entity that uses the system to input information about personal food preferences and health status, and has the system suggest optimal dishes.

[0108] The "terminal means" is a device or application for receiving information input by a user in real time and transmitting it to a server.

[0109] The "server means" is a part of the system that analyzes the received user data, generates an optimal cooking profile using a generative AI model, and stores it in a database.

[0110] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate optimal cooking profiles based on user data.

[0111] A "cooking profile" is a data set that includes optimal cooking recipes, cooking methods, and portion sizes based on a user's preferences and health status.

[0112] A "VR device" is a device that uses virtual reality technology to visually improve the appearance of food.

[0113] A "cooking facility or affiliated facility" is a place or company that actually cooks and serves food based on the cooking data sent from the server.

[0114] The "terminal means for providing ratings and feedback" is a device or application that allows a user to input ratings and feedback on a dish received and transmit them to the server.

[0115] This invention provides a system that links users, devices, and servers to provide individually optimized meals for each user. The system proposes optimal dishes based on the user's preferences and health status, and further improves the appearance of specific ingredients using VR technology, aiming to allow users to enjoy meals more comfortably.

[0116] System Configuration

[0117] Hardware and software used

[0118] Terminal: A device such as a smartphone or tablet on which a user inputs and receives information, with a dedicated application installed.

[0119] Server: A central server that analyzes user data and uses generative AI models to generate and store optimal cooking profiles.

[0120] VR Device: A device that uses virtual reality technology to visually enhance the appearance of food (e.g., a VR headset).

[0121] Processing steps

[0122] User information registration and analysis

[0123] 1. User: Logs in to the system and enters information such as their food preferences, allergy information, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method.

[0124] Example: User A starts a smartphone app and enters information such as "My favorite food is chicken," "I like spicy food," and "I prefer low-fat, high-protein meals."

[0125] 2. Terminal: Receives information entered by the user in real time, encrypts it, and sends it to the server.

[0126] Example: Once the user has completed entering the data, the device automatically encrypts the data and sends it to the server.

[0127] 3. Server: Analyzes the received user data and uses a generative AI model to generate the optimal cooking profile for each user, which is then stored in a database.

[0128] Example: The server analyzes user A's information, generates suggestions such as "spicy grilled chicken," and saves them in a database as a profile.

[0129] Improving appearance with VR technology

[0130] 1. Server: When using special ingredients such as insects, generate VR data to correct visually undesirable aspects.

[0131] Example: Generate a 3D model to make "insect steak" look like beef steak and create it as VR data.

[0132] 2. Terminal: Displays the VR data sent from the server on a device such as a VR headset.

[0133] Example: A VR device displays a retouched image of a real dish, showing user A a visually enhanced beef steak.

[0134] 3. User: Eat while visually enjoying the improved appearance of ingredients through the VR device.

[0135] Example: User A puts on a VR headset and eats "insect steak" without any hesitation while watching a beautified image.

[0136] Specific examples and prompts for the generative AI model

[0137] To give a specific example, when User B inputs "vegetarian food," "high protein, low calories," and "strong sour taste" into the system, the server analyzes this and makes suggestions such as "spicy tofu steak." This suggestion is set up in the VR video so that the tofu appears as a steak served on a beautiful plate. User B can use this VR headset to enjoy the visually beautiful food.

[0138] An example of a prompt for a generative AI model is shown below.

[0139] "Please analyze the data entered by the user (food preferences, allergy information, health condition, target nutrients, seasoning preferences, appearance, texture, cooking method) and recommend the most suitable dish. Also, please generate a 3D model to adjust the appearance of the dish in VR to make it easier for users to eat insects."

[0140] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual enhancement, and serving.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] Input: The user inputs personal food preferences, allergy information, health conditions, target nutrients, seasoning, appearance, texture, cooking method, etc.

[0144] Specific operation: The user logs in to a dedicated application using a device such as a smartphone or tablet and enters the necessary information into a questionnaire-style or multiple-choice form displayed on the system.

[0145] Output: The entered information is saved on the device.

[0146] Step 2:

[0147] Input: User-entered data.

[0148] Specific operation: Once the user has completed the input, the device performs a process to encrypt the data internally and sends it to the server.

[0149] Output: The encrypted data is sent to the server.

[0150] Step 3:

[0151] Input: Encrypted user data.

[0152] What happens: The server receives the encrypted data, decrypts it, and stores it in the database.

[0153] Output: The user data is decrypted and stored in the server's database.

[0154] Step 4:

[0155] Input: User data.

[0156] Specific operation: The server inputs the received data into the generative AI model, performs analysis, and generates the optimal cooking profile for the user.

[0157] Output: The analysis results in a food profile that is generated and stored in a database.

[0158] Step 5:

[0159] Input: Optimal cooking profile.

[0160] Specific operation: The user logs in to the system again and places a food order through the application.

[0161] Output: The order information is saved on the device.

[0162] Step 6:

[0163] Input: User's order data.

[0164] Specific operation: The terminal encrypts the order information and sends it to the server.

[0165] Output: The encrypted order data is sent to the server.

[0166] Step 7:

[0167] Input: Encrypted order data.

[0168] What it does: The server decrypts the encrypted order data and generates the optimal cooking method and portion size based on the user's profile data.

[0169] Output: The generated cooking recipe and quantity information are saved on the server.

[0170] Step 8:

[0171] Input: Optimal food cooking method and portion information.

[0172] Specific operation: When the server uses special ingredients such as insects, it generates VR data to correct any visually undesirable parts.

[0173] Output: The generated VR data is saved on the server.

[0174] Step 9:

[0175] Input: Generated VR data.

[0176] Specific operation: The device receives VR data from the server and displays it on the VR headset.

[0177] Output: The user's VR device displays a video of the improved food.

[0178] Step 10:

[0179] Input: Profile and order data.

[0180] Specific operation: The server sends the generated dish profile and order data to the cooking facility or affiliated facility and requests cooking.

[0181] Output: Cooking request data is sent to the cooking facility.

[0182] Step 11:

[0183] Input: Cooking progress information.

[0184] Specific operation: The server tracks the cooking process and delivery status in real time and sends that information to the terminal.

[0185] Output: Cooking and delivery status information is notified to the user.

[0186] Step 12:

[0187] Input: Food arrives.

[0188] Specific operation: After the food arrives, the user enters their rating and feedback using a dedicated application, and the device then sends the data to the server.

[0189] Output: The feedback information is sent to the server and stored.

[0190] Through these steps, users can enjoy a personalized and optimized dining experience.

[0191] (Application example 1)

[0192] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0193] Conventional food delivery systems have difficulty in suggesting optimal dishes tailored to individual user preferences and health conditions, and lack visual correction when using special ingredients, making it difficult to provide a satisfying dining experience for users. Furthermore, they lack real-time tracking and evaluation / feedback functions throughout the entire process from ordering to delivery.

[0194] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0195] In this invention, the server includes: means for a user to input information regarding personal preferences, allergy information, health condition, target nutrients, seasoning, appearance, texture, and cooking method; terminal means for receiving the input information in real time and sending it to the server; server means for analyzing the received data and using a generative AI model to generate a dish profile optimal for the user and store it in a database; and server means for receiving dish order information, generating a prompt for visual correction based on the generated dish profile, and sending it to the terminal. This makes it possible to suggest optimal dishes based on the user's individual preferences and health condition, and even when using special ingredients, visual correction can be performed to allow the user to enjoy a meal without any hesitation. It also makes it possible to track the entire process from order to delivery in real time and provide evaluations and feedback.

[0196] "User preferences" refers to the preferences or biases that a user has toward specific ingredients or seasonings.

[0197] "Allergy Information" refers to information about specific ingredients or substances that may cause an allergic reaction when ingested by a user.

[0198] "Health status" refers to information about the user's physical condition, illness, and health.

[0199] "Target nutrients" refers to the amount and balance of specific nutrients that a user aims to consume in order to maintain or improve their health.

[0200] "Seasoning" refers to the way food is seasoned using condiments or cooking methods.

[0201] "Appearance" refers to the visual characteristics of a dish, i.e., its appearance.

[0202] "Texture" refers to the tactile and physical sensations of food when it is eaten.

[0203] "Cooking method" refers to the specific steps and techniques used to prepare a dish.

[0204] "Terminal means" refers to a device that allows a user to input information and transmit it to a server.

[0205] "Server means" refers to a server that has the function of analyzing the received data, generating a cooking profile, and storing it in a database.

[0206] A "generative AI model" refers to an artificial intelligence system that generates optimal cooking profiles and visual correction prompts based on user information.

[0207] "Visual correction prompts" refer to instructions or data used to improve the appearance of dishes made with special ingredients.

[0208] "Vision correction device" refers to a device used to improve the visual impression of the food being served (e.g., a VR headset).

[0209] "Cooking facility" refers to the location where the food is actually prepared (e.g., kitchen or affiliated restaurant).

[0210] A "profile" refers to a set of information about dishes that are optimal for an individual user, generated based on the user's preferences, health status, target nutrients, etc.

[0211] "Real-time" refers to a time period in which data is collected, transmitted, analyzed, and displayed immediately, without delay.

[0212] This invention relates to a food delivery system that provides individually optimized meals for each user. The system proposes the most suitable meal based on the user's preferences and health condition, and supports the entire process from ordering to delivery. In addition, when special ingredients are used, a vision correction device is used to allow the user to enjoy their meal comfortably.

[0213] Hardware and software used

[0214] Smartphones: Used by users to input personal preferences and health information and place food orders.

[0215] Server: Receives the information sent by the user, generates the optimal food profile using the generative AI model, and generates prompts for visual correction and sends them to the device.

[0216] Generative AI model: Analyzes user information and generates optimal food profiles and visual correction prompts.

[0217] Vision correction devices: Devices that improve the visual impression of dishes containing special ingredients (e.g., VR headsets).

[0218] Food preparation facility: Where the food is actually prepared (e.g., kitchen or partner restaurant).

[0219] User information management

[0220] Users use a smartphone app to input information such as their preferences, allergies, health status, target nutrients, seasonings, appearance, texture, and cooking methods. This input data is sent to a server in real time. The server analyzes the received data and uses a generative AI model (e.g., using TensorFlow or PyTorch) to generate an optimal cooking profile for each user, which is then stored in a database.

[0221] Ordering food

[0222] A user orders food using a smartphone app. Once an order is placed, the device sends the information to a server. The server then generates the optimal cooking method and portion size based on the user's profile data. It also generates visual correction prompts as needed and sends them to the visual correction device.

[0223] Improving appearance with VR technology

[0224] When special ingredients (e.g., insects) are used, the server generates visual correction prompts to compensate for this and sends them to the device. Visual correction devices (e.g., VR headsets using Unity or Unreal Engine) can provide users with visually appealing images, making the meal more enjoyable.

[0225] Food delivery and tracking

[0226] The server then sends the generated recipe data to the cooking facility or partner restaurant and requests cooking. The cooking process and delivery status of the food are displayed to the user in real time via the terminal. The user can track the entire process from ordering to delivery, and can provide ratings and feedback after the food arrives.

[0227] Examples and prompts

[0228] 1. Example:

[0229] User A starts the smartphone app and inputs their preferences and health information. The server analyzes this information and suggests "spicy grilled chicken." User A places the order and can visually enjoy the insect steak, which looks like a beef steak, through the visual correction device.

[0230] 2. Example prompts for the generative AI model:

[0231] A user logs into a food delivery app and provides the following information:

[0232] Favorite food: Chicken

[0233] Favorite taste: Spicy food

[0234] Health Goals: High protein, low fat

[0235] Please let the server analyze this and suggest the best dish to serve, and also provide ways to improve the appearance if special ingredients are used.

[0236] This system allows users to enjoy individually optimized meals and special ingredients in a comfortable environment, and also allows them to track the entire process from order to delivery in real time and provide feedback.

[0237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0238] Step 1:

[0239] Using a smartphone app, users input information such as their preferences, allergies, health status, target nutrients, seasoning, appearance, texture, and cooking method. This data is collected in real time by the device and sent to the server. The input includes various pieces of user information, and this is the output data sent to the server.

[0240] Step 2:

[0241] The server analyzes the received user data and uses a generative AI model (e.g., TensorFlow or PyTorch) to generate the optimal cooking profile for the user. This profile is stored in a database. The input is the user data, and the output is the cooking profile as the analysis result. Data processing involves suggesting optimal dishes based on preferences and health information.

[0242] Step 3:

[0243] A user opens a smartphone app and orders food. The order details are sent to the server by the device. The input is the food order information, and the output is the transmission of the order data to the server. The operation is the user performing the ordering operation.

[0244] Step 4:

[0245] The server receives the order and generates the optimal cooking method and portion size by comparing it with existing food profiles. It also generates visual correction prompts as needed and sends them to the terminal. The input is the order data, and the output is the cooking method, portion size, and visual correction prompts. Data calculations involve matching the profile with the order information and generating visual correction data.

[0246] Step 5:

[0247] The terminal receives the visual correction prompt text and displays it on the visual correction device (e.g., a VR headset). The input is the visual correction prompt text, and the output is the data displayed on the visual device. The operation is that the terminal interacts with the device to display the visual data.

[0248] Step 6:

[0249] The server sends the generated dish data to a cooking facility or affiliated restaurant to request cooking. The input is a dish profile and order data, and the output is cooking instructions to the cooking facility. The operation is that the server sends the data, and the cooking facility receives it and starts cooking.

[0250] Step 7:

[0251] The server tracks the cooking process and delivery status of the food and displays the information on the terminal in real time. The input is the progress of cooking and delivery, and the output is real-time status notification. The operation is to collect progress information and notify the user terminal.

[0252] Step 8:

[0253] After receiving the dish, the user provides a rating and feedback through a smartphone app. The input is the rating and feedback for the dish, and the output is the rating data sent to the server. The operation is for the user to fill out the rating form in the app and submit it.

[0254] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0255] MODE FOR CARRYING OUT THE INVENTION

[0256] This system provides individually optimized meals by utilizing detailed personal data such as the user's food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, cooking method, etc. It also incorporates an emotion engine that recognizes the user's emotions and reflects them in real time, providing an even more personalized dining experience.

[0257] Below, the program processing of this system will be explained in natural language, with concrete examples.

[0258] User information registration and analysis

[0259] 1. User: Logs in to the system and enters information about their food preferences, allergy information, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method. Specifically, the information is entered in the form of answering questions from the application. For example, "User A" enters information such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[0260] 2. Terminal: Receives the information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[0261] 3. Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, it generates the appropriate food type and cooking method based on the user information, and generates a recommended food profile such as "spicy grilled chicken."

[0262] Introducing the Emotion Engine

[0263] 4. User: Logs in to the system and allows the emotion engine to recognize emotions. Specifically, the emotion engine allows the camera and microphone to analyze facial expressions and voice.

[0264] 5. Device: Acquires emotion data in real time and sends it to the server. Specifically, the device analyzes the user's facial expressions and voice using a camera and microphone, and sends the emotion recognition data to the server in real time.

[0265] 6. Server: Analyzes the received emotion data and reflects it in the cooking profile. Specifically, if User A seems to be "enjoying" the food, the server adjusts the spiciness slightly, for example.

[0266] User Orders

[0267] 7. User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[0268] 8. Terminal: Sends the user's order details to the server. Specifically, the order details of the dishes selected by the user and the profile data are sent to the server.

[0269] 9. Server: Based on the user's profile data, the server generates the optimal cooking method and portion sizes. It also references the recipe database and checks the availability of ingredients. Specifically, it checks the recipe and availability of "spicy grilled chicken."

[0270] Improving appearance with VR technology

[0271] 10. Server: When special ingredients such as insects are included, AI is used to generate data to correct visually undesirable aspects of the VR content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[0272] 11. Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[0273] 12. User: Through the VR device, users can visually enjoy the improved appearance of food. Specifically, users can wear a VR headset and enjoy eating insect steaks that look like beef steaks without any hesitation.

[0274] Food delivery and tracking

[0275] 13. Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[0276] 14. Device: The device tracks the food preparation process and delivery status in real time and displays them to the user. Specifically, the device displays the cooking progress and the delivery person's location information on the application to notify the user.

[0277] 15. User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[0278] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual improvement, emotional reflection, and delivery.

[0279] The processing flow will be explained below.

[0280] Step 1:

[0281] User: Logs into the system and enters information about their food preferences, allergy information, health condition, target nutrients, seasoning, appearance, texture, and cooking method. Specifically, the information is entered in the form of answering questions from the application. For example, "User A" enters information such as "I like chicken," "I like spicy food," and "I like low-fat, high-protein foods."

[0282] Step 2:

[0283] Terminal: Receives information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[0284] Step 3:

[0285] Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, it runs an algorithm that generates the appropriate food type and cooking method based on user information, generating a recommended food profile such as "spicy grilled chicken."

[0286] Step 4:

[0287] User: Allows the system to use the emotion engine. Specifically, the user sets the system to allow the emotion engine to analyze facial expressions and voice using the camera and microphone.

[0288] Step 5:

[0289] Device: Acquires emotion data in real time and sends it to the server. Specifically, the device analyzes the user's facial expressions and voice using a camera and microphone, and sends the emotion recognition data to the server in real time.

[0290] Step 6:

[0291] Server: Analyzes the received emotion data and reflects it in the cooking profile. Specifically, if User A seems to be "enjoying" the food, the server adjusts the spiciness a little.

[0292] Step 7:

[0293] User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[0294] Step 8:

[0295] Terminal: Sends the user's order details to the server. Specifically, it sends the order details of the dishes selected by the user along with the profile data to the server.

[0296] Step 9:

[0297] Server: Generates optimal cooking methods and portion sizes based on the user's profile data and emotional data. It also references the recipe database and checks the availability of ingredients. Specifically, it checks the recipe and inventory for "spicy grilled chicken."

[0298] Step 10:

[0299] Server: When special ingredients such as insects are included in the VR content, AI is used to generate data to correct visually undesirable aspects of the content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[0300] Step 11:

[0301] Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[0302] Step 12:

[0303] Users can visually enjoy the improved appearance of food through a VR device. Specifically, users can wear a VR headset and enjoy eating insect steaks that look like beef steaks without any hesitation.

[0304] Step 13:

[0305] Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[0306] Step 14:

[0307] Device: The device tracks the cooking process and delivery status in real time and displays them to the user. Specifically, the device displays information about the cooking process and the delivery person's location on the application to notify the user.

[0308] Step 15:

[0309] User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[0310] Example 2

[0311] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0312] Conventional systems have difficulty providing meals based on a user's individual preferences and health status, and lack a way to reflect these preferences in real time. Furthermore, they lack a way to improve the appearance of meals that contain ingredients that are visually undesirable. These issues must be resolved to provide users with a more personalized dining experience.

[0313] The identification process 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 analyzing received data, generating an optimal dish profile for the user using a generative AI model, and saving the profile in a database; means for incorporating an emotion engine that recognizes the user's emotions and reflecting the user's emotion data in the user profile; and means for transmitting the generated dish data to a cooking location or affiliated restaurant and requesting cooking. This makes it possible to provide individually optimized dishes, adjust the dishes in real time based on the user's emotions, and provide visually appealing ingredients.

[0314] A "user" is a person who orders food by inputting information about personal preferences, allergy information, health information, nutritional goals, taste, appearance, texture, and cooking method into the system.

[0315] "Preferences" refers to personal preferences such as the ingredients, characteristics of dishes, and seasonings that the user prefers.

[0316] "Allergy information" is information about allergens to which a user has an allergy, and is used to avoid allergic reactions to specific food ingredients or components.

[0317] "Health Information" is information related to a user's health status and health goals, including intake restrictions and recommendations for certain nutrients.

[0318] "Nutrition goal" refers to the nutrient balance or target value that a user wishes to achieve.

[0319] "Taste" is information that indicates the type and intensity of flavors that a user prefers.

[0320] "Appearance" refers to the visual characteristics that a user expects from a dish.

[0321] "Texture" refers to information about the physical texture, hardness, and softness that a user feels when eating food.

[0322] "Cooking methods" refers to information about the means, techniques, and procedures for preparing food, including specific cooking techniques such as baking, boiling, and steaming.

[0323] "Terminal means" refers to a device through which a user inputs information and transmits the information to a server in real time.

[0324] "Server means" refers to a system device that analyzes the received data, generates a cooking profile that is optimal for the user, and stores it in a database.

[0325] "Generative AI model" refers to an artificial intelligence mechanism that analyzes received user data and generates an optimal cooking profile.

[0326] An "emotion engine" refers to a system that has the ability to recognize a user's emotions, analyze that data, and reflect it in a cooking profile.

[0327] "VR Device" means a device that uses virtual reality technology to enable a user to visually enhance the appearance of food being served.

[0328] "Cooking location" refers to the location where the food is actually cooked based on the generated cooking profile.

[0329] "Partner restaurant" refers to a restaurant that partners with the system and provides food to users.

[0330] MODE FOR CARRYING OUT THE INVENTION

[0331] This system provides individually optimized meals by utilizing detailed personal data such as the user's preferences, allergy information, health information, nutritional goals, taste, appearance, texture, cooking method, etc. Furthermore, by incorporating an emotion engine that recognizes the user's emotions and reflects them in real time, it provides an even more personalized dining experience.

[0332] User information registration and analysis

[0333] Users log in to the system and enter information about their preferences, allergies, health information, nutritional goals, preferred seasonings, appearance, texture, and cooking methods. Input is done by answering questions within the application. For example, a user might enter information such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[0334] The device receives the information entered by the user in real time and sends it to the server. The data is encrypted using the Transport Layer Security (TLS) protocol and safely transferred to the server.

[0335] The server decrypts the received data using AES (Advanced Encryption Standard) and launches a generative AI model for analysis. The generative AI model analyzes the user's preferences and health information and generates a corresponding food profile. For example, the user may be suggested "spicy grilled chicken." The generated profile is then stored in a database.

[0336] Introducing the Emotion Engine

[0337] After logging in to the system, the user authorizes the use of the emotion engine by selecting "Allow emotion recognition" within the application. This activates the device's camera and microphone to collect the user's facial expressions and voice data.

[0338] The device sends the collected emotional data to the server in real time. The server analyzes the received emotional data and reflects it in the cooking profile. For example, if the user is "having fun," the server may adjust the spiciness a little.

[0339] User Orders

[0340] The user selects the desired dish from the list of dishes suggested by the application and clicks the "Order" button. The device encrypts the selected dish profile and order data and sends it to the server.

[0341] The server analyzes the received order data and generates the optimal cooking method and portion size. It also references the recipe database to check the availability of the necessary ingredients. For example, it checks the specific steps and ingredients for cooking "spicy grilled chicken."

[0342] Improving appearance with VR technology

[0343] When special ingredients are included, the server uses AI to generate VR content that corrects visually undesirable aspects. For example, it generates a 3D model and video data to make an insect steak look like a beef steak, and sends them to the device.

[0344] The device displays the data sent from the server on the VR headset, providing the user with visually improved ingredients, allowing the user to visually enjoy the cooking process through the VR headset.

[0345] Food delivery and tracking

[0346] The server sends the generated dish data to the cooking location or partner restaurant, requests cooking, and sends detailed cooking instructions and order details to request the start of cooking.

[0347] The device notifies the user in real time about the cooking process and delivery status, and the progress and location of the delivery person are displayed in the application.

[0348] Users can track the progress of their order until it arrives in the app, and provide feedback after it arrives by entering ratings and adding comments about the taste, quantity, appearance, and service.

[0349] Examples of specific examples and prompts

[0350] As a concrete example, we explain the process of a user entering information such as "I like chicken," "I like spicy food," and "Low fat, high protein" to order "spicy grilled chicken." We also include a process for users to visually improve their cooking using VR.

[0351] Example prompts to input to a generative AI model:

[0352] The user entered "nut allergy" as their allergy information, "on a diet" as their health information, "spicy" as their preferred seasoning, and "colorful" as their appearance preference. Based on this, please suggest low-calorie, high-protein menus. Also, if the dish looks like edible insects, please generate a 3D model to make it look like beefsteak, and further optimize it based on the user's emotional data.

[0353] As described above, the present invention is a system that utilizes detailed personal data and real-time emotional data of users to create and serve individually optimized dishes.

[0354] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0355] Step 1:

[0356] Users log in to the system and enter information about their personal preferences, allergy information, health information, nutritional goals, taste, appearance, texture, and cooking method. For example, they enter information in the form of answering questions in the application, providing information such as "I like chicken," "I like spicy food," and "Low fat, high protein." The entered information is sent to the terminal.

[0357] input:

[0358] Personal information that users enter into the application

[0359] output:

[0360] User personal data sent to the device

[0361] Step 2:

[0362] The terminal receives the information entered by the user in real time, encrypts the data, for example, using the Transport Layer Security (TLS) protocol, and then transmits the encrypted data to the server.

[0363] input:

[0364] User's personal data

[0365] output:

[0366] Encrypted user data is sent to the server

[0367] Step 3:

[0368] The server receives the data sent from the device and uses AES (Advanced Encryption Standard) to decrypt it. The decrypted user data is then input into a generative AI model, which analyzes the data. As a result of the analysis, an optimal cooking profile is generated based on the user's preferences and health information. For example, based on information such as "I like chicken" and "I like spicy food," "spicy grilled chicken" is suggested.

[0369] input:

[0370] Encrypted user data

[0371] output:

[0372] Decrypted user data

[0373] Food profile as analysis result

[0374] Step 4:

[0375] The server stores the generated cooking profile in a database, which contains user-specific information linked to the user ID.

[0376] input:

[0377] Analyzed food profile

[0378] output:

[0379] Cuisine profiles stored in a database

[0380] Step 5:

[0381] The user allows the use of the emotion engine by selecting "Allow emotion recognition" within the application.

[0382] input:

[0383] User Permissions

[0384] output:

[0385] Starting emotion recognition

[0386] Step 6:

[0387] The device activates the camera and microphone to collect the user's facial expressions and voice data, and transmits the collected emotional data to the server in real time.

[0388] input:

[0389] User facial expression data

[0390] User voice data

[0391] output:

[0392] Emotion data sent to the server in real time

[0393] Step 7:

[0394] The server analyzes the received emotion data and reflects it in the cooking profile. For example, if the user seems to be enjoying the food, it may adjust the spiciness a little.

[0395] input:

[0396] Emotional Data

[0397] output:

[0398] Tailored food profiles

[0399] Step 8:

[0400] The user selects the desired dish from the list of dishes suggested by the application and clicks the "Order" button. The device encrypts the selected dish profile and order data and sends it to the server.

[0401] input:

[0402] User's food selection

[0403] Order Data

[0404] output:

[0405] Encrypted Order Data

[0406] Step 9:

[0407] The server analyzes the received order data and generates the optimal cooking method and portion size. It also references the recipe database to check the availability of the necessary ingredients. For example, it checks the specific steps and ingredients for cooking "spicy grilled chicken."

[0408] input:

[0409] Encrypted Order Data

[0410] Recipe Database

[0411] output:

[0412] Best cooking method and portion size

[0413] Step 10:

[0414] The server sends the generated dish data to the cooking location or partner restaurant, requests cooking, and sends detailed cooking instructions and order details to request the start of cooking.

[0415] input:

[0416] Order details

[0417] Cooking method data

[0418] output:

[0419] Cooking location or cooking request data for partner restaurants

[0420] Step 11:

[0421] The device notifies the user in real time about the cooking process and delivery status, for example by displaying the progress and the delivery person's location in the application.

[0422] input:

[0423] Cooking progress information

[0424] Delivery status information

[0425] output:

[0426] User Notification

[0427] Step 12:

[0428] Users can check the progress of their order through the application until it arrives. After the food arrives, they can provide feedback and ratings through the application. They can enter ratings for taste, quantity, appearance, and service, and add comments.

[0429] input:

[0430] Progress Information

[0431] Evaluation items

[0432] output:

[0433] Ratings and Feedback Data

[0434] Step 13:

[0435] When special ingredients such as insects are included, the server uses AI to generate VR content that corrects visually undesirable aspects. For example, it generates a 3D model and video data to make an insect steak look like a beef steak, and sends them to the device.

[0436] input:

[0437] Special food information

[0438] output:

[0439] Generated VR content

[0440] Step 14:

[0441] The device displays the data sent from the server on the VR headset, providing the user with visually improved ingredients, allowing the user to visually enjoy the cooking process through the VR headset.

[0442] input:

[0443] Generated VR content

[0444] output:

[0445] VR display to the user

[0446] (Application example 2)

[0447] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0448] Conventional personalized meal delivery systems suggest dishes based on a user's ingredient preferences, allergy information, health status, cooking methods, etc., but because they do not take the user's emotions into account, the suggested dishes may not be optimal. Furthermore, dishes containing visually undesirable ingredients may reduce the user's appetite. Therefore, a system that further personalizes the dining experience and stimulates the user's appetite is needed.

[0449] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input information regarding personal food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method; terminal means for receiving the input information in real time and transmitting it to the server; means for analyzing the received data, using artificial intelligence to generate a cooking profile optimal for the user, and storing it in a database; and terminal means for recognizing the user's emotions and reflecting them in the cooking profile. This enables more personalized cooking suggestions that take into account the user's emotions in addition to their preferences and health status. Furthermore, visually undesirable parts can be corrected using a virtual reality device, thereby maintaining or improving the user's appetite.

[0450] A "user" is an individual who uses the system to provide information such as food preferences, allergy information, and health conditions, and receives the most suitable meal.

[0451] "Ingredient preferences" is information about specific ingredients, cooking methods, and seasonings that the user prefers.

[0452] "Allergy information" is information about specific food ingredients to which the user is allergic.

[0453] "Health status" refers to data related to the user's health, such as blood pressure and cholesterol levels.

[0454] A "nutrient target" is a specific nutritional goal that a user wishes to achieve.

[0455] "Seasoning" is information about the flavor of food and the use of spices according to the user's preferences.

[0456] "Appearance" refers to information about the visual elements that users desire in a dish, such as presentation and color.

[0457] "Texture" refers to information about a user's preferences regarding the mouthfeel and texture of a dish.

[0458] "Cooking method" is information about the cooking method preferred by the user, such as baking, boiling, frying, etc.

[0459] A "terminal" is hardware that allows a user to input information and send it to a server.

[0460] A "server" is a computer system that analyzes data received from users and generates an optimal cooking profile.

[0461] "Artificial intelligence" is the technology used to analyze the received data and generate the optimal cooking profile for the user.

[0462] An "emotion-recognizing device" is a combination of hardware and software that analyzes a user's facial expressions and voice and collects emotional data.

[0463] A "cuisine profile" is a data set that details the cuisine that is best suited to a user.

[0464] A "virtual reality device" is a device that incorporates virtual reality technology used to correct visual imperfections.

[0465] A "visually objectionable portion" refers to an ingredient or part of a dish that a user finds visually objectionable.

[0466] To implement this invention, three main components are used: a user, a terminal, and a server. The following describes these components and their respective operations in detail.

[0467] Enter user information

[0468] Users use devices such as smartphones or head-mounted displays to input information about their food preferences, allergies, health conditions, target nutrients, seasonings, appearance, texture, and cooking methods. The device, equipped with an emotion engine, obtains emotional data in real time from the user's facial expressions and voice.

[0469] Receiving and analyzing data

[0470] The device sends the input information and emotional data in an encrypted format to the server. The server analyzes the received user data and uses a generative AI model to generate an optimal cooking profile for each user, which is then stored in a database. The generative AI model selects dishes based on the user's ingredient preferences and health status.

[0471] Emotion-based food adjustment

[0472] When data is acquired by the emotion engine, the server analyzes it and reflects it in the cooking profile. For example, if the user is "having fun," the server can adjust the spiciness and seasoning of the food based on that emotion.

[0473] Visual improvements

[0474] If the server detects ingredients that are visually objectionable to the user, it generates data to correct the problem using a virtual reality device. This data is sent to the user's device, and the user can view the corrected visual data through a VR headset. Specifically, it generates a 3D model and video data to make a dish containing insects look like beefsteak.

[0475] Food Serving

[0476] The server then sends the created dish profile to the kitchen or partner restaurant to request cooking. The cooking process and delivery status are displayed to the user in real time via the device. The user can track the waiting time and provide feedback after the food arrives.

[0477] The specific hardware and software used

[0478] Devices: Smartphone, head-mounted display, VR headset (emotion data analysis and user information input)

[0479] Server: Data analysis server, database (for generating cooking profiles and storing data)

[0480] Software: Generative AI model (AI data analysis), emotion recognition engine (emotion data analysis)

[0481] Examples and prompts

[0482] As a specific example, the following case will be described where the user likes chicken, likes spicy food, and has recently been feeling "happy."

[0483] Example prompt sentence:

[0484] {

[0485] "user_id": "userA",

[0486] "preferences": {

[0487] "meat": "chicken",

[0488] "spiciness": "high",

[0489] "diet": "low-fat, high-protein"

[0490] },

[0491] "allergies": ["gluten"],

[0492] "health": {"blood_pressure": "normal", "cholesterol": "low"},

[0493] "emotion": "happy"

[0494] }

[0495] In this way, a personalized dining experience can be provided to the user.

[0496] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0497] Step 1:

[0498] The user uses a smartphone or head-mounted display to input information such as food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method. In addition, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data. This information is then input into the device.

[0499] Input: User's personal data and emotional data

[0500] Output: Real-time encrypted user data

[0501] Step 2:

[0502] The device receives the input information in real time and transmits it in encrypted form to the server, where the device aggregates, encrypts, and transmits the data securely.

[0503] Input: User's encrypted data

[0504] Output: Encrypted data sent to the server

[0505] Step 3:

[0506] The server analyzes the received data and uses a generative AI model to generate an optimal cooking profile for each user, which is then stored in a database. Here, the AI ​​determines the type of food, cooking method, nutrient balance, etc. based on the user's preferences and health status.

[0507] Input: Encrypted user data

[0508] Output: Food profiles stored in a database

[0509] Step 4:

[0510] The user's emotional data is sent to the server in real time, where it is analyzed. Based on the analysis results, the emotional data is reflected in the cooking profile. For example, if the user appears to be "having fun," the spiciness of the food may be slightly increased, and other adjustments may be made in real time.

[0511] Input: Emotion data

[0512] Output: Adjusted cooking profile

[0513] Step 5:

[0514] The server determines the recommended dishes, cooking methods, and portion sizes based on the user's profile data, and generates visual correction data for the virtual reality device: a 3D model and video data that makes the insect appear like a beefsteak.

[0515] Input: Food profiles stored in the database

[0516] Output: Vision correction data for virtual reality devices

[0517] Step 6:

[0518] The device receives the visual correction data sent from the server and transmits it to the virtual reality device. When the user puts on the VR headset, the visually improved dish is displayed.

[0519] Input: Vision correction data

[0520] Output: Image displayed on a virtual reality device

[0521] Step 7:

[0522] The server sends the created dish profile to the kitchen or partner restaurant and requests cooking. Here, the server sends detailed cooking instructions and order details and requests the start of cooking.

[0523] Input: Cuisine Profile

[0524] Output: The kitchen or partner restaurant that received the cooking instructions

[0525] Step 8:

[0526] The device displays the cooking process and delivery status to the user in real time, and the user can check the cooking status and delivery person's location through the application.

[0527] Input: Cooking progress, delivery information

[0528] Output: Real-time information displayed on the user's device

[0529] Step 9:

[0530] Users wait for their food to arrive and then provide feedback and ratings on the food and service through the app, which collects data to improve the service.

[0531] Input: Food rating, feedback

[0532] Output: Evaluation data and feedback stored on the server

[0533] Through these steps, a series of processes is realized, from user input to generating the optimal dish, visual improvement, reflection of emotions, and finally serving it.

[0534] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0535] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0536] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0537] [Second embodiment]

[0538] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0539] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0540] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0541] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0542] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0543] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0544] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0545] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0546] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0547] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0548] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0549] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0550] MODE FOR CARRYING OUT THE INVENTION

[0551] This invention provides a system that links users, devices, and servers to provide individually optimized meals. This system proposes and serves optimal meals based on the user's preferences and health status. Furthermore, by using VR technology to improve the visual experience, users can enjoy meals with unusual ingredients without any hesitation.

[0552] Below, the program processing of this system will be explained in natural language, with concrete examples.

[0553] User information registration and analysis

[0554] 1. User: Logs in to the system and enters information about their food preferences, allergies, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method.

[0555] Example: User A starts a smartphone app and answers questions, entering information such as "My favorite food is chicken," "I like spicy food," and "I prefer low-fat, high-protein meals."

[0556] 2. Terminal: Receives information entered by the user in real time and sends it to the server.

[0557] Example: Once the user has completed entering the data, the device automatically encrypts the data and sends it to the server.

[0558] 3. Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database.

[0559] Example: The server analyzes user A's information, generates a recommendation for "spicy grilled chicken," and saves it as a profile.

[0560] User Orders

[0561] 4. User: Logs into the system and places a food order.

[0562] Example: User A enters "I want spicy grilled chicken" into the app and presses the order button.

[0563] 5. Terminal: Sends the user's order details to the server.

[0564] Example: The device links user A's order information and profile and sends a request to the server.

[0565] 6. Server: Generates optimal cooking methods and portion sizes based on the user's profile data, and also checks the availability of necessary ingredients.

[0566] Example: A server generates a specific recipe and measurements for cooking "spicy grilled chicken" and checks availability.

[0567] Improving appearance with VR technology

[0568] 7. Server: When using special ingredients such as insects, data is generated to correct visually undesirable aspects using VR technology.

[0569] Example: The server generates a 3D model to make "insect steak" look like beef steak, and creates it as VR data.

[0570] 8. Terminal: Displays the data sent from the server on a device such as a VR headset.

[0571] Example: A VR device displays a retouched image of a real dish, showing user A a visually enhanced beef steak.

[0572] 9. Users: They can see the improved appearance of ingredients through the VR device and enjoy eating without any hesitation.

[0573] Example: User A puts on a VR headset and eats "insect steak" while watching a beautified image, and enjoys the meal without any resistance.

[0574] Food delivery and tracking

[0575] 10. Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking.

[0576] Example: A server sends order data and cooking instructions for "spicy grilled chicken" to a partner restaurant.

[0577] 11. Terminal: Displays information to the user to track the food preparation process and delivery status in real time.

[0578] Example: The device notifies user A in real time that food is being cooked or that the delivery person has departed.

[0579] 12. Users: Track how long they wait for their order to arrive and provide a rating and feedback upon completion.

[0580] Example: User A receives a meal and rates it "very delicious" on the app.

[0581] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual enhancement, and serving.

[0582] The processing flow will be explained below.

[0583] Step 1:

[0584] User: Logs in to the system and enters information about their food preferences, allergy information, health status, target nutrients, preferred seasoning, appearance, texture, and cooking method. Specifically, the user responds to questions in the application by operating the system to enter specific preferences and health information, such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[0585] Step 2:

[0586] Terminal: Receives information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[0587] Step 3:

[0588] Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, the server runs an algorithm to generate the appropriate cooking type and cooking method based on information about food preferences and health status, generating a recommended cooking profile such as "spicy grilled chicken."

[0589] Step 4:

[0590] User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[0591] Step 5:

[0592] Terminal: Sends the user's order details to the server. Specifically, it sends the order details of the dishes selected by the user along with the profile data to the server.

[0593] Step 6:

[0594] Server: Generates optimal cooking methods and quantities based on the user's profile data. It also references the recipe database and checks the availability of ingredients. Specifically, it generates specific cooking instructions and quantities for "spicy grilled chicken" and connects with the inventory management system of partner restaurants to check the ingredients.

[0595] Step 7:

[0596] Server: When special ingredients such as insects are included in the VR content, AI is used to generate data to correct visually undesirable aspects of the content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[0597] Step 8:

[0598] Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[0599] Step 9:

[0600] Users can visually enjoy the improved appearance of food through a VR device. Specifically, users can wear a VR headset and enjoy eating the actual food while looking at the beautified food, allowing them to enjoy their meal without experiencing any visual discomfort.

[0601] Step 10:

[0602] Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[0603] Step 11:

[0604] Device: The device tracks the cooking process and delivery status in real time and displays them to the user. Specifically, the device displays information about the cooking process and the delivery person's location on the application to notify the user.

[0605] Step 12:

[0606] User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[0607] Example 1

[0608] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0609] Conventional meal recommendation systems have difficulty providing individually optimized meals based on the user's preferences and health status, and they also lack the means to improve visually unfavorable ingredients using VR technology. Therefore, there is a need for a system that allows users to enjoy healthy and optimal meals without any resistance.

[0610] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0611] In this invention, the server includes a means for a user to input information about personal food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method, a terminal means for receiving the input information in real time and sending it to the server, and a means for analyzing the received data and using a generative AI model to generate a cooking profile optimal for the user and store it in a database. This individually optimizes the user's dining experience and enables more satisfying meal suggestions, including visual improvements.

[0612] A "user" is an entity that uses the system to input information about personal food preferences and health status, and has the system suggest optimal dishes.

[0613] The "terminal means" is a device or application for receiving information input by a user in real time and transmitting it to a server.

[0614] The "server means" is a part of the system that analyzes the received user data, generates an optimal cooking profile using a generative AI model, and stores it in a database.

[0615] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate optimal cooking profiles based on user data.

[0616] A "cooking profile" is a data set that includes optimal cooking recipes, cooking methods, and portion sizes based on a user's preferences and health status.

[0617] A "VR device" is a device that uses virtual reality technology to visually improve the appearance of food.

[0618] A "cooking facility or affiliated facility" is a place or company that actually cooks and serves food based on the cooking data sent from the server.

[0619] The "terminal means for providing ratings and feedback" is a device or application that allows a user to input ratings and feedback on a dish received and transmit them to the server.

[0620] This invention provides a system that links users, devices, and servers to provide individually optimized meals for each user. The system proposes optimal dishes based on the user's preferences and health status, and further improves the appearance of specific ingredients using VR technology, aiming to allow users to enjoy meals more comfortably.

[0621] System Configuration

[0622] Hardware and software used

[0623] Terminal: A device such as a smartphone or tablet on which a user inputs and receives information, with a dedicated application installed.

[0624] Server: A central server that analyzes user data and uses generative AI models to generate and store optimal cooking profiles.

[0625] VR Device: A device that uses virtual reality technology to visually enhance the appearance of food (e.g., a VR headset).

[0626] Processing steps

[0627] User information registration and analysis

[0628] 1. User: Logs in to the system and enters information such as their food preferences, allergy information, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method.

[0629] Example: User A starts a smartphone app and enters information such as "My favorite food is chicken," "I like spicy food," and "I prefer low-fat, high-protein meals."

[0630] 2. Terminal: Receives information entered by the user in real time, encrypts it, and sends it to the server.

[0631] Example: Once the user has completed entering the data, the device automatically encrypts the data and sends it to the server.

[0632] 3. Server: Analyzes the received user data and uses a generative AI model to generate the optimal cooking profile for each user, which is then stored in a database.

[0633] Example: The server analyzes user A's information, generates suggestions such as "spicy grilled chicken," and saves them in a database as a profile.

[0634] Improving appearance with VR technology

[0635] 1. Server: When using special ingredients such as insects, generate VR data to correct visually undesirable aspects.

[0636] Example: Generate a 3D model to make "insect steak" look like beef steak and create it as VR data.

[0637] 2. Terminal: Displays the VR data sent from the server on a device such as a VR headset.

[0638] Example: A VR device displays a retouched image of a real dish, showing user A a visually enhanced beef steak.

[0639] 3. User: Eat while visually enjoying the improved appearance of ingredients through the VR device.

[0640] Example: User A puts on a VR headset and eats "insect steak" without any hesitation while watching a beautified image.

[0641] Specific examples and prompts for the generative AI model

[0642] To give a specific example, when User B inputs "vegetarian food," "high protein, low calories," and "strong sour taste" into the system, the server analyzes this and makes suggestions such as "spicy tofu steak." This suggestion is set up in the VR video so that the tofu appears as a steak served on a beautiful plate. User B can use this VR headset to enjoy the visually beautiful food.

[0643] An example of a prompt for a generative AI model is shown below.

[0644] "Please analyze the data entered by the user (food preferences, allergy information, health condition, target nutrients, seasoning preferences, appearance, texture, cooking method) and recommend the most suitable dish. Also, please generate a 3D model to adjust the appearance of the dish in VR to make it easier for users to eat insects."

[0645] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual enhancement, and serving.

[0646] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0647] Step 1:

[0648] Input: The user inputs personal food preferences, allergy information, health conditions, target nutrients, seasoning, appearance, texture, cooking method, etc.

[0649] Specific operation: The user logs in to a dedicated application using a device such as a smartphone or tablet and enters the necessary information into a questionnaire-style or multiple-choice form displayed on the system.

[0650] Output: The entered information is saved on the device.

[0651] Step 2:

[0652] Input: User-entered data.

[0653] Specific operation: Once the user has completed the input, the device performs a process to encrypt the data internally and sends it to the server.

[0654] Output: The encrypted data is sent to the server.

[0655] Step 3:

[0656] Input: Encrypted user data.

[0657] What happens: The server receives the encrypted data, decrypts it, and stores it in the database.

[0658] Output: The user data is decrypted and stored in the server's database.

[0659] Step 4:

[0660] Input: User data.

[0661] Specific operation: The server inputs the received data into the generative AI model, performs analysis, and generates the optimal cooking profile for the user.

[0662] Output: The analysis results in a food profile that is generated and stored in a database.

[0663] Step 5:

[0664] Input: Optimal cooking profile.

[0665] Specific operation: The user logs in to the system again and places a food order through the application.

[0666] Output: The order information is saved on the device.

[0667] Step 6:

[0668] Input: User's order data.

[0669] Specific operation: The terminal encrypts the order information and sends it to the server.

[0670] Output: The encrypted order data is sent to the server.

[0671] Step 7:

[0672] Input: Encrypted order data.

[0673] What it does: The server decrypts the encrypted order data and generates the optimal cooking method and portion size based on the user's profile data.

[0674] Output: The generated cooking recipe and quantity information are saved on the server.

[0675] Step 8:

[0676] Input: Optimal food cooking method and portion information.

[0677] Specific operation: When the server uses special ingredients such as insects, it generates VR data to correct any visually undesirable parts.

[0678] Output: The generated VR data is saved on the server.

[0679] Step 9:

[0680] Input: Generated VR data.

[0681] Specific operation: The device receives VR data from the server and displays it on the VR headset.

[0682] Output: The user's VR device displays a video of the improved food.

[0683] Step 10:

[0684] Input: Profile and order data.

[0685] Specific operation: The server sends the generated dish profile and order data to the cooking facility or affiliated facility and requests cooking.

[0686] Output: Cooking request data is sent to the cooking facility.

[0687] Step 11:

[0688] Input: Cooking progress information.

[0689] Specific operation: The server tracks the cooking process and delivery status in real time and sends that information to the terminal.

[0690] Output: Cooking and delivery status information is notified to the user.

[0691] Step 12:

[0692] Input: Food arrives.

[0693] Specific operation: After the food arrives, the user enters their rating and feedback using a dedicated application, and the device then sends the data to the server.

[0694] Output: The feedback information is sent to the server and stored.

[0695] Through these steps, users can enjoy a personalized and optimized dining experience.

[0696] (Application example 1)

[0697] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0698] Conventional food delivery systems have difficulty in suggesting optimal dishes tailored to individual user preferences and health conditions, and lack visual correction when using special ingredients, making it difficult to provide a satisfying dining experience for users. Furthermore, they lack real-time tracking and evaluation / feedback functions throughout the entire process from ordering to delivery.

[0699] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0700] In this invention, the server includes: means for a user to input information regarding personal preferences, allergy information, health condition, target nutrients, seasoning, appearance, texture, and cooking method; terminal means for receiving the input information in real time and sending it to the server; server means for analyzing the received data and using a generative AI model to generate a dish profile optimal for the user and store it in a database; and server means for receiving dish order information, generating a prompt for visual correction based on the generated dish profile, and sending it to the terminal. This makes it possible to suggest optimal dishes based on the user's individual preferences and health condition, and even when using special ingredients, visual correction can be performed to allow the user to enjoy a meal without any hesitation. It also makes it possible to track the entire process from order to delivery in real time and provide evaluations and feedback.

[0701] "User preferences" refers to the preferences or biases that a user has toward specific ingredients or seasonings.

[0702] "Allergy Information" refers to information about specific ingredients or substances that may cause an allergic reaction when ingested by a user.

[0703] "Health status" refers to information about the user's physical condition, illness, and health.

[0704] "Target nutrients" refers to the amount and balance of specific nutrients that a user aims to consume in order to maintain or improve their health.

[0705] "Seasoning" refers to the way food is seasoned using condiments or cooking methods.

[0706] "Appearance" refers to the visual characteristics of a dish, i.e., its appearance.

[0707] "Texture" refers to the tactile and physical sensations of food when it is eaten.

[0708] "Cooking method" refers to the specific steps and techniques used to prepare a dish.

[0709] "Terminal means" refers to a device that allows a user to input information and transmit it to a server.

[0710] "Server means" refers to a server that has the function of analyzing the received data, generating a cooking profile, and storing it in a database.

[0711] A "generative AI model" refers to an artificial intelligence system that generates optimal cooking profiles and visual correction prompts based on user information.

[0712] "Visual correction prompts" refer to instructions or data used to improve the appearance of dishes made with special ingredients.

[0713] "Vision correction device" refers to a device used to improve the visual impression of the food being served (e.g., a VR headset).

[0714] "Cooking facility" refers to the location where the food is actually prepared (e.g., kitchen or affiliated restaurant).

[0715] A "profile" refers to a set of information about dishes that are optimal for an individual user, generated based on the user's preferences, health status, target nutrients, etc.

[0716] "Real-time" refers to a time period in which data is collected, transmitted, analyzed, and displayed immediately, without delay.

[0717] This invention relates to a food delivery system that provides individually optimized meals for each user. The system proposes the most suitable meal based on the user's preferences and health condition, and supports the entire process from ordering to delivery. In addition, when special ingredients are used, a vision correction device is used to allow the user to enjoy their meal comfortably.

[0718] Hardware and software used

[0719] Smartphones: Used by users to input personal preferences and health information and place food orders.

[0720] Server: Receives the information sent by the user, generates the optimal food profile using the generative AI model, and generates prompts for visual correction and sends them to the device.

[0721] Generative AI model: Analyzes user information and generates optimal food profiles and visual correction prompts.

[0722] Vision correction devices: Devices that improve the visual impression of dishes containing special ingredients (e.g., VR headsets).

[0723] Food preparation facility: Where the food is actually prepared (e.g., kitchen or partner restaurant).

[0724] User information management

[0725] Users use a smartphone app to input information such as their preferences, allergies, health status, target nutrients, seasonings, appearance, texture, and cooking methods. This input data is sent to a server in real time. The server analyzes the received data and uses a generative AI model (e.g., using TensorFlow or PyTorch) to generate an optimal cooking profile for each user, which is then stored in a database.

[0726] Ordering food

[0727] A user orders food using a smartphone app. Once an order is placed, the device sends the information to a server. The server then generates the optimal cooking method and portion size based on the user's profile data. It also generates visual correction prompts as needed and sends them to the visual correction device.

[0728] Improving appearance with VR technology

[0729] When special ingredients (e.g., insects) are used, the server generates visual correction prompts to compensate for this and sends them to the device. Visual correction devices (e.g., VR headsets using Unity or Unreal Engine) can provide users with visually appealing images, making the meal more enjoyable.

[0730] Food delivery and tracking

[0731] The server then sends the generated recipe data to the cooking facility or partner restaurant and requests cooking. The cooking process and delivery status of the food are displayed to the user in real time via the terminal. The user can track the entire process from ordering to delivery, and can provide ratings and feedback after the food arrives.

[0732] Examples and prompts

[0733] 1. Example:

[0734] User A starts the smartphone app and inputs their preferences and health information. The server analyzes this information and suggests "spicy grilled chicken." User A places the order and can visually enjoy the insect steak, which looks like a beef steak, through the visual correction device.

[0735] 2. Example prompts for the generative AI model:

[0736] A user logs into a food delivery app and provides the following information:

[0737] Favorite food: Chicken

[0738] Favorite taste: Spicy food

[0739] Health Goals: High protein, low fat

[0740] Please let the server analyze this and suggest the best dish to serve, and also provide ways to improve the appearance if special ingredients are used.

[0741] This system allows users to enjoy individually optimized meals and special ingredients in a comfortable environment, and also allows them to track the entire process from order to delivery in real time and provide feedback.

[0742] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0743] Step 1:

[0744] Using a smartphone app, users input information such as their preferences, allergies, health status, target nutrients, seasoning, appearance, texture, and cooking method. This data is collected in real time by the device and sent to the server. The input includes various pieces of user information, and this is the output data sent to the server.

[0745] Step 2:

[0746] The server analyzes the received user data and uses a generative AI model (e.g., TensorFlow or PyTorch) to generate the optimal cooking profile for the user. This profile is stored in a database. The input is the user data, and the output is the cooking profile as the analysis result. Data processing involves suggesting optimal dishes based on preferences and health information.

[0747] Step 3:

[0748] A user opens a smartphone app and orders food. The order details are sent to the server by the device. The input is the food order information, and the output is the transmission of the order data to the server. The operation is the user performing the ordering operation.

[0749] Step 4:

[0750] The server receives the order and generates the optimal cooking method and portion size by comparing it with existing food profiles. It also generates visual correction prompts as needed and sends them to the terminal. The input is the order data, and the output is the cooking method, portion size, and visual correction prompts. Data calculations involve matching the profile with the order information and generating visual correction data.

[0751] Step 5:

[0752] The terminal receives the visual correction prompt text and displays it on the visual correction device (e.g., a VR headset). The input is the visual correction prompt text, and the output is the data displayed on the visual device. The operation is that the terminal interacts with the device to display the visual data.

[0753] Step 6:

[0754] The server sends the generated dish data to a cooking facility or affiliated restaurant to request cooking. The input is a dish profile and order data, and the output is cooking instructions to the cooking facility. The operation is that the server sends the data, and the cooking facility receives it and starts cooking.

[0755] Step 7:

[0756] The server tracks the cooking process and delivery status of the food and displays the information on the terminal in real time. The input is the progress of cooking and delivery, and the output is real-time status notification. The operation is to collect progress information and notify the user terminal.

[0757] Step 8:

[0758] After receiving the dish, the user provides a rating and feedback through a smartphone app. The input is the rating and feedback for the dish, and the output is the rating data sent to the server. The operation is for the user to fill out the rating form in the app and submit it.

[0759] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0760] MODE FOR CARRYING OUT THE INVENTION

[0761] This system provides individually optimized meals by utilizing detailed personal data such as the user's food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, cooking method, etc. It also incorporates an emotion engine that recognizes the user's emotions and reflects them in real time, providing an even more personalized dining experience.

[0762] Below, the program processing of this system will be explained in natural language, with concrete examples.

[0763] User information registration and analysis

[0764] 1. User: Logs in to the system and enters information about their food preferences, allergy information, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method. Specifically, the information is entered in the form of answering questions from the application. For example, "User A" enters information such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[0765] 2. Terminal: Receives the information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[0766] 3. Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, it generates the appropriate food type and cooking method based on the user information, and generates a recommended food profile such as "spicy grilled chicken."

[0767] Introducing the Emotion Engine

[0768] 4. User: Logs in to the system and allows the emotion engine to recognize emotions. Specifically, the emotion engine allows the camera and microphone to analyze facial expressions and voice.

[0769] 5. Device: Acquires emotion data in real time and sends it to the server. Specifically, the device analyzes the user's facial expressions and voice using a camera and microphone, and sends the emotion recognition data to the server in real time.

[0770] 6. Server: Analyzes the received emotion data and reflects it in the cooking profile. Specifically, if User A seems to be "enjoying" the food, the server adjusts the spiciness slightly, for example.

[0771] User Orders

[0772] 7. User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[0773] 8. Terminal: Sends the user's order details to the server. Specifically, the order details of the dishes selected by the user and the profile data are sent to the server.

[0774] 9. Server: Based on the user's profile data, the server generates the optimal cooking method and portion sizes. It also references the recipe database and checks the availability of ingredients. Specifically, it checks the recipe and availability of "spicy grilled chicken."

[0775] Improving appearance with VR technology

[0776] 10. Server: When special ingredients such as insects are included, AI is used to generate data to correct visually undesirable aspects of the VR content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[0777] 11. Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[0778] 12. User: Through the VR device, users can visually enjoy the improved appearance of food. Specifically, users can wear a VR headset and enjoy eating insect steaks that look like beef steaks without any hesitation.

[0779] Food delivery and tracking

[0780] 13. Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[0781] 14. Device: The device tracks the food preparation process and delivery status in real time and displays them to the user. Specifically, the device displays the cooking progress and the delivery person's location information on the application to notify the user.

[0782] 15. User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[0783] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual improvement, emotional reflection, and delivery.

[0784] The processing flow will be explained below.

[0785] Step 1:

[0786] User: Logs into the system and enters information about their food preferences, allergy information, health condition, target nutrients, seasoning, appearance, texture, and cooking method. Specifically, the information is entered in the form of answering questions from the application. For example, "User A" enters information such as "I like chicken," "I like spicy food," and "I like low-fat, high-protein foods."

[0787] Step 2:

[0788] Terminal: Receives information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[0789] Step 3:

[0790] Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, it runs an algorithm that generates the appropriate food type and cooking method based on user information, generating a recommended food profile such as "spicy grilled chicken."

[0791] Step 4:

[0792] User: Allows the system to use the emotion engine. Specifically, the user sets the system to allow the emotion engine to analyze facial expressions and voice using the camera and microphone.

[0793] Step 5:

[0794] Device: Acquires emotion data in real time and sends it to the server. Specifically, the device analyzes the user's facial expressions and voice using a camera and microphone, and sends the emotion recognition data to the server in real time.

[0795] Step 6:

[0796] Server: Analyzes the received emotion data and reflects it in the cooking profile. Specifically, if User A seems to be "enjoying" the food, the server adjusts the spiciness a little.

[0797] Step 7:

[0798] User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[0799] Step 8:

[0800] Terminal: Sends the user's order details to the server. Specifically, it sends the order details of the dishes selected by the user along with the profile data to the server.

[0801] Step 9:

[0802] Server: Generates optimal cooking methods and portion sizes based on the user's profile data and emotional data. It also references the recipe database and checks the availability of ingredients. Specifically, it checks the recipe and inventory for "spicy grilled chicken."

[0803] Step 10:

[0804] Server: When special ingredients such as insects are included in the VR content, AI is used to generate data to correct visually undesirable aspects of the content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[0805] Step 11:

[0806] Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[0807] Step 12:

[0808] Users can visually enjoy the improved appearance of food through a VR device. Specifically, users can wear a VR headset and enjoy eating insect steaks that look like beef steaks without any hesitation.

[0809] Step 13:

[0810] Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[0811] Step 14:

[0812] Device: The device tracks the cooking process and delivery status in real time and displays them to the user. Specifically, the device displays information about the cooking process and the delivery person's location on the application to notify the user.

[0813] Step 15:

[0814] User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[0815] Example 2

[0816] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0817] Conventional systems have difficulty providing meals based on a user's individual preferences and health status, and lack a way to reflect these preferences in real time. Furthermore, they lack a way to improve the appearance of meals that contain ingredients that are visually undesirable. These issues must be resolved to provide users with a more personalized dining experience.

[0818] The identification process 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 analyzing received data, generating an optimal dish profile for the user using a generative AI model, and saving the profile in a database; means for incorporating an emotion engine that recognizes the user's emotions and reflecting the user's emotion data in the user profile; and means for transmitting the generated dish data to a cooking location or affiliated restaurant and requesting cooking. This makes it possible to provide individually optimized dishes, adjust the dishes in real time based on the user's emotions, and provide visually appealing ingredients.

[0819] A "user" is a person who orders food by inputting information about personal preferences, allergy information, health information, nutritional goals, taste, appearance, texture, and cooking method into the system.

[0820] "Preferences" refers to personal preferences such as the ingredients, characteristics of dishes, and seasonings that the user prefers.

[0821] "Allergy information" is information about allergens to which a user has an allergy, and is used to avoid allergic reactions to specific food ingredients or components.

[0822] "Health Information" is information related to a user's health status and health goals, including intake restrictions and recommendations for certain nutrients.

[0823] "Nutrition goal" refers to the nutrient balance or target value that a user wishes to achieve.

[0824] "Taste" is information that indicates the type and intensity of flavors that a user prefers.

[0825] "Appearance" refers to the visual characteristics that a user expects from a dish.

[0826] "Texture" refers to information about the physical texture, hardness, and softness that a user feels when eating food.

[0827] "Cooking methods" refers to information about the means, techniques, and procedures for preparing food, including specific cooking techniques such as baking, boiling, and steaming.

[0828] "Terminal means" refers to a device through which a user inputs information and transmits the information to a server in real time.

[0829] "Server means" refers to a system device that analyzes the received data, generates a cooking profile that is optimal for the user, and stores it in a database.

[0830] "Generative AI model" refers to an artificial intelligence mechanism that analyzes received user data and generates an optimal cooking profile.

[0831] An "emotion engine" refers to a system that has the ability to recognize a user's emotions, analyze that data, and reflect it in a cooking profile.

[0832] "VR Device" means a device that uses virtual reality technology to enable a user to visually enhance the appearance of food being served.

[0833] "Cooking location" refers to the location where the food is actually cooked based on the generated cooking profile.

[0834] "Partner restaurant" refers to a restaurant that partners with the system and provides food to users.

[0835] MODE FOR CARRYING OUT THE INVENTION

[0836] This system provides individually optimized meals by utilizing detailed personal data such as the user's preferences, allergy information, health information, nutritional goals, taste, appearance, texture, cooking method, etc. Furthermore, by incorporating an emotion engine that recognizes the user's emotions and reflects them in real time, it provides an even more personalized dining experience.

[0837] User information registration and analysis

[0838] Users log in to the system and enter information about their preferences, allergies, health information, nutritional goals, preferred seasonings, appearance, texture, and cooking methods. Input is done by answering questions within the application. For example, a user might enter information such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[0839] The device receives the information entered by the user in real time and sends it to the server. The data is encrypted using the Transport Layer Security (TLS) protocol and safely transferred to the server.

[0840] The server decrypts the received data using AES (Advanced Encryption Standard) and launches a generative AI model for analysis. The generative AI model analyzes the user's preferences and health information and generates a corresponding food profile. For example, the user may be suggested "spicy grilled chicken." The generated profile is then stored in a database.

[0841] Introducing the Emotion Engine

[0842] After logging in to the system, the user authorizes the use of the emotion engine by selecting "Allow emotion recognition" within the application. This activates the device's camera and microphone to collect the user's facial expressions and voice data.

[0843] The device sends the collected emotional data to the server in real time. The server analyzes the received emotional data and reflects it in the cooking profile. For example, if the user is "having fun," the server may adjust the spiciness a little.

[0844] User Orders

[0845] The user selects the desired dish from the list of dishes suggested by the application and clicks the "Order" button. The device encrypts the selected dish profile and order data and sends it to the server.

[0846] The server analyzes the received order data and generates the optimal cooking method and portion size. It also references the recipe database to check the availability of the necessary ingredients. For example, it checks the specific steps and ingredients for cooking "spicy grilled chicken."

[0847] Improving appearance with VR technology

[0848] When special ingredients are included, the server uses AI to generate VR content that corrects visually undesirable aspects. For example, it generates a 3D model and video data to make an insect steak look like a beef steak, and sends them to the device.

[0849] The device displays the data sent from the server on the VR headset, providing the user with visually improved ingredients, allowing the user to visually enjoy the cooking process through the VR headset.

[0850] Food delivery and tracking

[0851] The server sends the generated dish data to the cooking location or partner restaurant, requests cooking, and sends detailed cooking instructions and order details to request the start of cooking.

[0852] The device notifies the user in real time about the cooking process and delivery status, and the progress and location of the delivery person are displayed in the application.

[0853] Users can track the progress of their order until it arrives in the app, and provide feedback after it arrives by entering ratings and adding comments about the taste, quantity, appearance, and service.

[0854] Examples of specific examples and prompts

[0855] As a concrete example, we explain the process of a user entering information such as "I like chicken," "I like spicy food," and "Low fat, high protein" to order "spicy grilled chicken." We also include a process for users to visually improve their cooking using VR.

[0856] Example prompts to input to a generative AI model:

[0857] The user entered "nut allergy" as their allergy information, "on a diet" as their health information, "spicy" as their preferred seasoning, and "colorful" as their appearance preference. Based on this, please suggest low-calorie, high-protein menus. Also, if the dish looks like edible insects, please generate a 3D model to make it look like beefsteak, and further optimize it based on the user's emotional data.

[0858] As described above, the present invention is a system that utilizes detailed personal data and real-time emotional data of users to create and serve individually optimized dishes.

[0859] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0860] Step 1:

[0861] Users log in to the system and enter information about their personal preferences, allergy information, health information, nutritional goals, taste, appearance, texture, and cooking method. For example, they enter information in the form of answering questions in the application, providing information such as "I like chicken," "I like spicy food," and "Low fat, high protein." The entered information is sent to the terminal.

[0862] input:

[0863] Personal information that users enter into the application

[0864] output:

[0865] User personal data sent to the device

[0866] Step 2:

[0867] The terminal receives the information entered by the user in real time, encrypts the data, for example, using the Transport Layer Security (TLS) protocol, and then transmits the encrypted data to the server.

[0868] input:

[0869] User's personal data

[0870] output:

[0871] Encrypted user data is sent to the server

[0872] Step 3:

[0873] The server receives the data sent from the device and uses AES (Advanced Encryption Standard) to decrypt it. The decrypted user data is then input into a generative AI model, which analyzes the data. As a result of the analysis, an optimal cooking profile is generated based on the user's preferences and health information. For example, based on information such as "I like chicken" and "I like spicy food," "spicy grilled chicken" is suggested.

[0874] input:

[0875] Encrypted user data

[0876] output:

[0877] Decrypted user data

[0878] Food profile as analysis result

[0879] Step 4:

[0880] The server stores the generated cooking profile in a database, which contains user-specific information linked to the user ID.

[0881] input:

[0882] Analyzed food profile

[0883] output:

[0884] Cuisine profiles stored in a database

[0885] Step 5:

[0886] The user allows the use of the emotion engine by selecting "Allow emotion recognition" within the application.

[0887] input:

[0888] User Permissions

[0889] output:

[0890] Starting emotion recognition

[0891] Step 6:

[0892] The device activates the camera and microphone to collect the user's facial expressions and voice data, and transmits the collected emotional data to the server in real time.

[0893] input:

[0894] User facial expression data

[0895] User voice data

[0896] output:

[0897] Emotion data sent to the server in real time

[0898] Step 7:

[0899] The server analyzes the received emotion data and reflects it in the cooking profile. For example, if the user seems to be enjoying the food, it may adjust the spiciness a little.

[0900] input:

[0901] Emotional Data

[0902] output:

[0903] Tailored food profiles

[0904] Step 8:

[0905] The user selects the desired dish from the list of dishes suggested by the application and clicks the "Order" button. The device encrypts the selected dish profile and order data and sends it to the server.

[0906] input:

[0907] User's food selection

[0908] Order Data

[0909] output:

[0910] Encrypted Order Data

[0911] Step 9:

[0912] The server analyzes the received order data and generates the optimal cooking method and portion size. It also references the recipe database to check the availability of the necessary ingredients. For example, it checks the specific steps and ingredients for cooking "spicy grilled chicken."

[0913] input:

[0914] Encrypted Order Data

[0915] Recipe Database

[0916] output:

[0917] Best cooking method and portion size

[0918] Step 10:

[0919] The server sends the generated dish data to the cooking location or partner restaurant, requests cooking, and sends detailed cooking instructions and order details to request the start of cooking.

[0920] input:

[0921] Order details

[0922] Cooking method data

[0923] output:

[0924] Cooking location or cooking request data for partner restaurants

[0925] Step 11:

[0926] The device notifies the user in real time about the cooking process and delivery status, for example by displaying the progress and the delivery person's location in the application.

[0927] input:

[0928] Cooking progress information

[0929] Delivery status information

[0930] output:

[0931] User Notification

[0932] Step 12:

[0933] Users can check the progress of their order through the application until it arrives. After the food arrives, they can provide feedback and ratings through the application. They can enter ratings for taste, quantity, appearance, and service, and add comments.

[0934] input:

[0935] Progress Information

[0936] Evaluation items

[0937] output:

[0938] Ratings and Feedback Data

[0939] Step 13:

[0940] When special ingredients such as insects are included, the server uses AI to generate VR content that corrects visually undesirable aspects. For example, it generates a 3D model and video data to make an insect steak look like a beef steak, and sends them to the device.

[0941] input:

[0942] Special food information

[0943] output:

[0944] Generated VR content

[0945] Step 14:

[0946] The device displays the data sent from the server on the VR headset, providing the user with visually improved ingredients, allowing the user to visually enjoy the cooking process through the VR headset.

[0947] input:

[0948] Generated VR content

[0949] output:

[0950] VR display to the user

[0951] (Application example 2)

[0952] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0953] Conventional personalized meal delivery systems suggest dishes based on a user's ingredient preferences, allergy information, health status, cooking methods, etc., but because they do not take the user's emotions into account, the suggested dishes may not be optimal. Furthermore, dishes containing visually undesirable ingredients may reduce the user's appetite. Therefore, a system that further personalizes the dining experience and stimulates the user's appetite is needed.

[0954] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input information regarding personal food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method; terminal means for receiving the input information in real time and transmitting it to the server; means for analyzing the received data, using artificial intelligence to generate a cooking profile optimal for the user, and storing it in a database; and terminal means for recognizing the user's emotions and reflecting them in the cooking profile. This enables more personalized cooking suggestions that take into account the user's emotions in addition to their preferences and health status. Furthermore, visually undesirable parts can be corrected using a virtual reality device, thereby maintaining or improving the user's appetite.

[0955] A "user" is an individual who uses the system to provide information such as food preferences, allergy information, and health conditions, and receives the most suitable meal.

[0956] "Ingredient preferences" is information about specific ingredients, cooking methods, and seasonings that the user prefers.

[0957] "Allergy information" is information about specific food ingredients to which the user is allergic.

[0958] "Health status" refers to data related to the user's health, such as blood pressure and cholesterol levels.

[0959] A "nutrient target" is a specific nutritional goal that a user wishes to achieve.

[0960] "Seasoning" is information about the flavor of food and the use of spices according to the user's preferences.

[0961] "Appearance" refers to information about the visual elements that users desire in a dish, such as presentation and color.

[0962] "Texture" refers to information about a user's preferences regarding the mouthfeel and texture of a dish.

[0963] "Cooking method" is information about the cooking method preferred by the user, such as baking, boiling, frying, etc.

[0964] A "terminal" is hardware that allows a user to input information and send it to a server.

[0965] A "server" is a computer system that analyzes data received from users and generates an optimal cooking profile.

[0966] "Artificial intelligence" is the technology used to analyze the received data and generate the optimal cooking profile for the user.

[0967] An "emotion-recognizing device" is a combination of hardware and software that analyzes a user's facial expressions and voice and collects emotional data.

[0968] A "cuisine profile" is a data set that details the cuisine that is best suited to a user.

[0969] A "virtual reality device" is a device that incorporates virtual reality technology used to correct visual imperfections.

[0970] A "visually objectionable portion" refers to an ingredient or part of a dish that a user finds visually objectionable.

[0971] To implement this invention, three main components are used: a user, a terminal, and a server. The following describes these components and their respective operations in detail.

[0972] Enter user information

[0973] Users use devices such as smartphones or head-mounted displays to input information about their food preferences, allergies, health conditions, target nutrients, seasonings, appearance, texture, and cooking methods. The device, equipped with an emotion engine, obtains emotional data in real time from the user's facial expressions and voice.

[0974] Receiving and analyzing data

[0975] The device sends the input information and emotional data in an encrypted format to the server. The server analyzes the received user data and uses a generative AI model to generate an optimal cooking profile for each user, which is then stored in a database. The generative AI model selects dishes based on the user's ingredient preferences and health status.

[0976] Emotion-based food adjustment

[0977] When data is acquired by the emotion engine, the server analyzes it and reflects it in the cooking profile. For example, if the user is "having fun," the server can adjust the spiciness and seasoning of the food based on that emotion.

[0978] Visual improvements

[0979] If the server detects ingredients that are visually objectionable to the user, it generates data to correct the problem using a virtual reality device. This data is sent to the user's device, and the user can view the corrected visual data through a VR headset. Specifically, it generates a 3D model and video data to make a dish containing insects look like beefsteak.

[0980] Food Serving

[0981] The server then sends the created dish profile to the kitchen or partner restaurant to request cooking. The cooking process and delivery status are displayed to the user in real time via the device. The user can track the waiting time and provide feedback after the food arrives.

[0982] The specific hardware and software used

[0983] Devices: Smartphone, head-mounted display, VR headset (emotion data analysis and user information input)

[0984] Server: Data analysis server, database (for generating cooking profiles and storing data)

[0985] Software: Generative AI model (AI data analysis), emotion recognition engine (emotion data analysis)

[0986] Examples and prompts

[0987] As a specific example, the following case will be described where the user likes chicken, likes spicy food, and has recently been feeling "happy."

[0988] Example prompt sentence:

[0989] {

[0990] "user_id": "userA",

[0991] "preferences": {

[0992] "meat": "chicken",

[0993] "spiciness": "high",

[0994] "diet": "low-fat, high-protein"

[0995] },

[0996] "allergies": ["gluten"],

[0997] "health": {"blood_pressure": "normal", "cholesterol": "low"},

[0998] "emotion": "happy"

[0999] }

[1000] In this way, a personalized dining experience can be provided to the user.

[1001] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1002] Step 1:

[1003] The user uses a smartphone or head-mounted display to input information such as food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method. In addition, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data. This information is then input into the device.

[1004] Input: User's personal data and emotional data

[1005] Output: Real-time encrypted user data

[1006] Step 2:

[1007] The device receives the input information in real time and transmits it in encrypted form to the server, where the device aggregates, encrypts, and transmits the data securely.

[1008] Input: User's encrypted data

[1009] Output: Encrypted data sent to the server

[1010] Step 3:

[1011] The server analyzes the received data and uses a generative AI model to generate an optimal cooking profile for each user, which is then stored in a database. Here, the AI ​​determines the type of food, cooking method, nutrient balance, etc. based on the user's preferences and health status.

[1012] Input: Encrypted user data

[1013] Output: Food profiles stored in a database

[1014] Step 4:

[1015] The user's emotional data is sent to the server in real time, where it is analyzed. Based on the analysis results, the emotional data is reflected in the cooking profile. For example, if the user appears to be "having fun," the spiciness of the food may be slightly increased, and other adjustments may be made in real time.

[1016] Input: Emotion data

[1017] Output: Adjusted cooking profile

[1018] Step 5:

[1019] The server determines the recommended dishes, cooking methods, and portion sizes based on the user's profile data, and generates visual correction data for the virtual reality device: a 3D model and video data that makes the insect appear like a beefsteak.

[1020] Input: Food profiles stored in the database

[1021] Output: Vision correction data for virtual reality devices

[1022] Step 6:

[1023] The device receives the visual correction data sent from the server and transmits it to the virtual reality device. When the user puts on the VR headset, the visually improved dish is displayed.

[1024] Input: Vision correction data

[1025] Output: Image displayed on a virtual reality device

[1026] Step 7:

[1027] The server sends the created dish profile to the kitchen or partner restaurant and requests cooking. Here, the server sends detailed cooking instructions and order details and requests the start of cooking.

[1028] Input: Cuisine Profile

[1029] Output: The kitchen or partner restaurant that received the cooking instructions

[1030] Step 8:

[1031] The device displays the cooking process and delivery status to the user in real time, and the user can check the cooking status and delivery person's location through the application.

[1032] Input: Cooking progress, delivery information

[1033] Output: Real-time information displayed on the user's device

[1034] Step 9:

[1035] Users wait for their food to arrive and then provide feedback and ratings on the food and service through the app, which collects data to improve the service.

[1036] Input: Food rating, feedback

[1037] Output: Evaluation data and feedback stored on the server

[1038] Through these steps, a series of processes is realized, from user input to generating the optimal dish, visual improvement, reflection of emotions, and finally serving it.

[1039] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1040] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1041] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1042] [Third embodiment]

[1043] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1044] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1045] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1046] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1047] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1048] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1049] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1050] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1051] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1052] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1053] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1054] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1055] MODE FOR CARRYING OUT THE INVENTION

[1056] This invention provides a system that links users, devices, and servers to provide individually optimized meals. This system proposes and serves optimal meals based on the user's preferences and health status. Furthermore, by using VR technology to improve the visual experience, users can enjoy meals with unusual ingredients without any hesitation.

[1057] Below, the program processing of this system will be explained in natural language, with concrete examples.

[1058] User information registration and analysis

[1059] 1. User: Logs in to the system and enters information about their food preferences, allergies, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method.

[1060] Example: User A starts a smartphone app and answers questions, entering information such as "My favorite food is chicken," "I like spicy food," and "I prefer low-fat, high-protein meals."

[1061] 2. Terminal: Receives information entered by the user in real time and sends it to the server.

[1062] Example: Once the user has completed entering the data, the device automatically encrypts the data and sends it to the server.

[1063] 3. Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database.

[1064] Example: The server analyzes user A's information, generates a recommendation for "spicy grilled chicken," and saves it as a profile.

[1065] User Orders

[1066] 4. User: Logs into the system and places a food order.

[1067] Example: User A enters "I want spicy grilled chicken" into the app and presses the order button.

[1068] 5. Terminal: Sends the user's order details to the server.

[1069] Example: The device links user A's order information and profile and sends a request to the server.

[1070] 6. Server: Generates optimal cooking methods and portion sizes based on the user's profile data, and also checks the availability of necessary ingredients.

[1071] Example: A server generates a specific recipe and measurements for cooking "spicy grilled chicken" and checks availability.

[1072] Improving appearance with VR technology

[1073] 7. Server: When using special ingredients such as insects, data is generated to correct visually undesirable aspects using VR technology.

[1074] Example: The server generates a 3D model to make "insect steak" look like beef steak, and creates it as VR data.

[1075] 8. Terminal: Displays the data sent from the server on a device such as a VR headset.

[1076] Example: A VR device displays a retouched image of a real dish, showing user A a visually enhanced beef steak.

[1077] 9. Users: They can see the improved appearance of ingredients through the VR device and enjoy eating without any hesitation.

[1078] Example: User A puts on a VR headset and eats "insect steak" while watching a beautified image, and enjoys the meal without any resistance.

[1079] Food delivery and tracking

[1080] 10. Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking.

[1081] Example: A server sends order data and cooking instructions for "spicy grilled chicken" to a partner restaurant.

[1082] 11. Terminal: Displays information to the user to track the food preparation process and delivery status in real time.

[1083] Example: The device notifies user A in real time that food is being cooked or that the delivery person has departed.

[1084] 12. Users: Track how long they wait for their order to arrive and provide a rating and feedback upon completion.

[1085] Example: User A receives a meal and rates it "very delicious" on the app.

[1086] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual enhancement, and serving.

[1087] The processing flow will be explained below.

[1088] Step 1:

[1089] User: Logs in to the system and enters information about their food preferences, allergy information, health status, target nutrients, preferred seasoning, appearance, texture, and cooking method. Specifically, the user responds to questions in the application by operating the system to enter specific preferences and health information, such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[1090] Step 2:

[1091] Terminal: Receives information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[1092] Step 3:

[1093] Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, the server runs an algorithm to generate the appropriate cooking type and cooking method based on information about food preferences and health status, generating a recommended cooking profile such as "spicy grilled chicken."

[1094] Step 4:

[1095] User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[1096] Step 5:

[1097] Terminal: Sends the user's order details to the server. Specifically, it sends the order details of the dishes selected by the user along with the profile data to the server.

[1098] Step 6:

[1099] Server: Generates optimal cooking methods and quantities based on the user's profile data. It also references the recipe database and checks the availability of ingredients. Specifically, it generates specific cooking instructions and quantities for "spicy grilled chicken" and connects with the inventory management system of partner restaurants to check the ingredients.

[1100] Step 7:

[1101] Server: When special ingredients such as insects are included in the VR content, AI is used to generate data to correct visually undesirable aspects of the content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[1102] Step 8:

[1103] Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[1104] Step 9:

[1105] Users can visually enjoy the improved appearance of food through a VR device. Specifically, users can wear a VR headset and enjoy eating the actual food while looking at the beautified food, allowing them to enjoy their meal without experiencing any visual discomfort.

[1106] Step 10:

[1107] Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[1108] Step 11:

[1109] Device: The device tracks the cooking process and delivery status in real time and displays them to the user. Specifically, the device displays information about the cooking process and the delivery person's location on the application to notify the user.

[1110] Step 12:

[1111] User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[1112] Example 1

[1113] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1114] Conventional meal recommendation systems have difficulty providing individually optimized meals based on the user's preferences and health status, and they also lack the means to improve visually unfavorable ingredients using VR technology. Therefore, there is a need for a system that allows users to enjoy healthy and optimal meals without any resistance.

[1115] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1116] In this invention, the server includes a means for a user to input information about personal food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method, a terminal means for receiving the input information in real time and sending it to the server, and a means for analyzing the received data and using a generative AI model to generate a cooking profile optimal for the user and store it in a database. This individually optimizes the user's dining experience and enables more satisfying meal suggestions, including visual improvements.

[1117] A "user" is an entity that uses the system to input information about personal food preferences and health status, and has the system suggest optimal dishes.

[1118] The "terminal means" is a device or application for receiving information input by a user in real time and transmitting it to a server.

[1119] The "server means" is a part of the system that analyzes the received user data, generates an optimal cooking profile using a generative AI model, and stores it in a database.

[1120] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate optimal cooking profiles based on user data.

[1121] A "cooking profile" is a data set that includes optimal cooking recipes, cooking methods, and portion sizes based on a user's preferences and health status.

[1122] A "VR device" is a device that uses virtual reality technology to visually improve the appearance of food.

[1123] A "cooking facility or affiliated facility" is a place or company that actually cooks and serves food based on the cooking data sent from the server.

[1124] The "terminal means for providing ratings and feedback" is a device or application that allows a user to input ratings and feedback on a dish received and transmit them to the server.

[1125] This invention provides a system that links users, devices, and servers to provide individually optimized meals for each user. The system proposes optimal dishes based on the user's preferences and health status, and further improves the appearance of specific ingredients using VR technology, aiming to allow users to enjoy meals more comfortably.

[1126] System Configuration

[1127] Hardware and software used

[1128] Terminal: A device such as a smartphone or tablet on which a user inputs and receives information, with a dedicated application installed.

[1129] Server: A central server that analyzes user data and uses generative AI models to generate and store optimal cooking profiles.

[1130] VR Device: A device that uses virtual reality technology to visually enhance the appearance of food (e.g., a VR headset).

[1131] Processing steps

[1132] User information registration and analysis

[1133] 1. User: Logs in to the system and enters information such as their food preferences, allergy information, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method.

[1134] Example: User A starts a smartphone app and enters information such as "My favorite food is chicken," "I like spicy food," and "I prefer low-fat, high-protein meals."

[1135] 2. Terminal: Receives information entered by the user in real time, encrypts it, and sends it to the server.

[1136] Example: Once the user has completed entering the data, the device automatically encrypts the data and sends it to the server.

[1137] 3. Server: Analyzes the received user data and uses a generative AI model to generate the optimal cooking profile for each user, which is then stored in a database.

[1138] Example: The server analyzes user A's information, generates suggestions such as "spicy grilled chicken," and saves them in a database as a profile.

[1139] Improving appearance with VR technology

[1140] 1. Server: When using special ingredients such as insects, generate VR data to correct visually undesirable aspects.

[1141] Example: Generate a 3D model to make "insect steak" look like beef steak and create it as VR data.

[1142] 2. Terminal: Displays the VR data sent from the server on a device such as a VR headset.

[1143] Example: A VR device displays a retouched image of a real dish, showing user A a visually enhanced beef steak.

[1144] 3. User: Eat while visually enjoying the improved appearance of ingredients through the VR device.

[1145] Example: User A puts on a VR headset and eats "insect steak" without any hesitation while watching a beautified image.

[1146] Specific examples and prompts for the generative AI model

[1147] To give a specific example, when User B inputs "vegetarian food," "high protein, low calories," and "strong sour taste" into the system, the server analyzes this and makes suggestions such as "spicy tofu steak." This suggestion is set up in the VR video so that the tofu appears as a steak served on a beautiful plate. User B can use this VR headset to enjoy the visually beautiful food.

[1148] An example of a prompt for a generative AI model is shown below.

[1149] "Please analyze the data entered by the user (food preferences, allergy information, health condition, target nutrients, seasoning preferences, appearance, texture, cooking method) and recommend the most suitable dish. Also, please generate a 3D model to adjust the appearance of the dish in VR to make it easier for users to eat insects."

[1150] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual enhancement, and serving.

[1151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1152] Step 1:

[1153] Input: The user inputs personal food preferences, allergy information, health conditions, target nutrients, seasoning, appearance, texture, cooking method, etc.

[1154] Specific operation: The user logs in to a dedicated application using a device such as a smartphone or tablet and enters the necessary information into a questionnaire-style or multiple-choice form displayed on the system.

[1155] Output: The entered information is saved on the device.

[1156] Step 2:

[1157] Input: User-entered data.

[1158] Specific operation: Once the user has completed the input, the device performs a process to encrypt the data internally and sends it to the server.

[1159] Output: The encrypted data is sent to the server.

[1160] Step 3:

[1161] Input: Encrypted user data.

[1162] What happens: The server receives the encrypted data, decrypts it, and stores it in the database.

[1163] Output: The user data is decrypted and stored in the server's database.

[1164] Step 4:

[1165] Input: User data.

[1166] Specific operation: The server inputs the received data into the generative AI model, performs analysis, and generates the optimal cooking profile for the user.

[1167] Output: The analysis results in a food profile that is generated and stored in a database.

[1168] Step 5:

[1169] Input: Optimal cooking profile.

[1170] Specific operation: The user logs in to the system again and places a food order through the application.

[1171] Output: The order information is saved on the device.

[1172] Step 6:

[1173] Input: User's order data.

[1174] Specific operation: The terminal encrypts the order information and sends it to the server.

[1175] Output: The encrypted order data is sent to the server.

[1176] Step 7:

[1177] Input: Encrypted order data.

[1178] What it does: The server decrypts the encrypted order data and generates the optimal cooking method and portion size based on the user's profile data.

[1179] Output: The generated cooking recipe and quantity information are saved on the server.

[1180] Step 8:

[1181] Input: Optimal food cooking method and portion information.

[1182] Specific operation: When the server uses special ingredients such as insects, it generates VR data to correct any visually undesirable parts.

[1183] Output: The generated VR data is saved on the server.

[1184] Step 9:

[1185] Input: Generated VR data.

[1186] Specific operation: The device receives VR data from the server and displays it on the VR headset.

[1187] Output: The user's VR device displays a video of the improved food.

[1188] Step 10:

[1189] Input: Profile and order data.

[1190] Specific operation: The server sends the generated dish profile and order data to the cooking facility or affiliated facility and requests cooking.

[1191] Output: Cooking request data is sent to the cooking facility.

[1192] Step 11:

[1193] Input: Cooking progress information.

[1194] Specific operation: The server tracks the cooking process and delivery status in real time and sends that information to the terminal.

[1195] Output: Cooking and delivery status information is notified to the user.

[1196] Step 12:

[1197] Input: Food arrives.

[1198] Specific operation: After the food arrives, the user enters their rating and feedback using a dedicated application, and the device then sends the data to the server.

[1199] Output: The feedback information is sent to the server and stored.

[1200] Through these steps, users can enjoy a personalized and optimized dining experience.

[1201] (Application example 1)

[1202] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1203] Conventional food delivery systems have difficulty in suggesting optimal dishes tailored to individual user preferences and health conditions, and lack visual correction when using special ingredients, making it difficult to provide a satisfying dining experience for users. Furthermore, they lack real-time tracking and evaluation / feedback functions throughout the entire process from ordering to delivery.

[1204] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1205] In this invention, the server includes: means for a user to input information regarding personal preferences, allergy information, health condition, target nutrients, seasoning, appearance, texture, and cooking method; terminal means for receiving the input information in real time and sending it to the server; server means for analyzing the received data and using a generative AI model to generate a dish profile optimal for the user and store it in a database; and server means for receiving dish order information, generating a prompt for visual correction based on the generated dish profile, and sending it to the terminal. This makes it possible to suggest optimal dishes based on the user's individual preferences and health condition, and even when using special ingredients, visual correction can be performed to allow the user to enjoy a meal without any hesitation. It also makes it possible to track the entire process from order to delivery in real time and provide evaluations and feedback.

[1206] "User preferences" refers to the preferences or biases that a user has toward specific ingredients or seasonings.

[1207] "Allergy Information" refers to information about specific ingredients or substances that may cause an allergic reaction when ingested by a user.

[1208] "Health status" refers to information about the user's physical condition, illness, and health.

[1209] "Target nutrients" refers to the amount and balance of specific nutrients that a user aims to consume in order to maintain or improve their health.

[1210] "Seasoning" refers to the way food is seasoned using condiments or cooking methods.

[1211] "Appearance" refers to the visual characteristics of a dish, i.e., its appearance.

[1212] "Texture" refers to the tactile and physical sensations of food when it is eaten.

[1213] "Cooking method" refers to the specific steps and techniques used to prepare a dish.

[1214] "Terminal means" refers to a device that allows a user to input information and transmit it to a server.

[1215] "Server means" refers to a server that has the function of analyzing the received data, generating a cooking profile, and storing it in a database.

[1216] A "generative AI model" refers to an artificial intelligence system that generates optimal cooking profiles and visual correction prompts based on user information.

[1217] "Visual correction prompts" refer to instructions or data used to improve the appearance of dishes made with special ingredients.

[1218] "Vision correction device" refers to a device used to improve the visual impression of the food being served (e.g., a VR headset).

[1219] "Cooking facility" refers to the location where the food is actually prepared (e.g., kitchen or affiliated restaurant).

[1220] A "profile" refers to a set of information about dishes that are optimal for an individual user, generated based on the user's preferences, health status, target nutrients, etc.

[1221] "Real-time" refers to a time period in which data is collected, transmitted, analyzed, and displayed immediately, without delay.

[1222] This invention relates to a food delivery system that provides individually optimized meals for each user. The system proposes the most suitable meal based on the user's preferences and health condition, and supports the entire process from ordering to delivery. In addition, when special ingredients are used, a vision correction device is used to allow the user to enjoy their meal comfortably.

[1223] Hardware and software used

[1224] Smartphones: Used by users to input personal preferences and health information and place food orders.

[1225] Server: Receives the information sent by the user, generates the optimal food profile using the generative AI model, and generates prompts for visual correction and sends them to the device.

[1226] Generative AI model: Analyzes user information and generates optimal food profiles and visual correction prompts.

[1227] Vision correction devices: Devices that improve the visual impression of dishes containing special ingredients (e.g., VR headsets).

[1228] Food preparation facility: Where the food is actually prepared (e.g., kitchen or partner restaurant).

[1229] User information management

[1230] Users use a smartphone app to input information such as their preferences, allergies, health status, target nutrients, seasonings, appearance, texture, and cooking methods. This input data is sent to a server in real time. The server analyzes the received data and uses a generative AI model (e.g., using TensorFlow or PyTorch) to generate an optimal cooking profile for each user, which is then stored in a database.

[1231] Ordering food

[1232] A user orders food using a smartphone app. Once an order is placed, the device sends the information to a server. The server then generates the optimal cooking method and portion size based on the user's profile data. It also generates visual correction prompts as needed and sends them to the visual correction device.

[1233] Improving appearance with VR technology

[1234] When special ingredients (e.g., insects) are used, the server generates visual correction prompts to compensate for this and sends them to the device. Visual correction devices (e.g., VR headsets using Unity or Unreal Engine) can provide users with visually appealing images, making the meal more enjoyable.

[1235] Food delivery and tracking

[1236] The server then sends the generated recipe data to the cooking facility or partner restaurant and requests cooking. The cooking process and delivery status of the food are displayed to the user in real time via the terminal. The user can track the entire process from ordering to delivery, and can provide ratings and feedback after the food arrives.

[1237] Examples and prompts

[1238] 1. Example:

[1239] User A starts the smartphone app and inputs their preferences and health information. The server analyzes this information and suggests "spicy grilled chicken." User A places the order and can visually enjoy the insect steak, which looks like a beef steak, through the visual correction device.

[1240] 2. Example prompts for the generative AI model:

[1241] A user logs into a food delivery app and provides the following information:

[1242] Favorite food: Chicken

[1243] Favorite taste: Spicy food

[1244] Health Goals: High protein, low fat

[1245] Please let the server analyze this and suggest the best dish to serve, and also provide ways to improve the appearance if special ingredients are used.

[1246] This system allows users to enjoy individually optimized meals and special ingredients in a comfortable environment, and also allows them to track the entire process from order to delivery in real time and provide feedback.

[1247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1248] Step 1:

[1249] Using a smartphone app, users input information such as their preferences, allergies, health status, target nutrients, seasoning, appearance, texture, and cooking method. This data is collected in real time by the device and sent to the server. The input includes various pieces of user information, and this is the output data sent to the server.

[1250] Step 2:

[1251] The server analyzes the received user data and uses a generative AI model (e.g., TensorFlow or PyTorch) to generate the optimal cooking profile for the user. This profile is stored in a database. The input is the user data, and the output is the cooking profile as the analysis result. Data processing involves suggesting optimal dishes based on preferences and health information.

[1252] Step 3:

[1253] A user opens a smartphone app and orders food. The order details are sent to the server by the device. The input is the food order information, and the output is the transmission of the order data to the server. The operation is the user performing the ordering operation.

[1254] Step 4:

[1255] The server receives the order and generates the optimal cooking method and portion size by comparing it with existing food profiles. It also generates visual correction prompts as needed and sends them to the terminal. The input is the order data, and the output is the cooking method, portion size, and visual correction prompts. Data calculations involve matching the profile with the order information and generating visual correction data.

[1256] Step 5:

[1257] The terminal receives the visual correction prompt text and displays it on the visual correction device (e.g., a VR headset). The input is the visual correction prompt text, and the output is the data displayed on the visual device. The operation is that the terminal interacts with the device to display the visual data.

[1258] Step 6:

[1259] The server sends the generated dish data to a cooking facility or affiliated restaurant to request cooking. The input is a dish profile and order data, and the output is cooking instructions to the cooking facility. The operation is that the server sends the data, and the cooking facility receives it and starts cooking.

[1260] Step 7:

[1261] The server tracks the cooking process and delivery status of the food and displays the information on the terminal in real time. The input is the progress of cooking and delivery, and the output is real-time status notification. The operation is to collect progress information and notify the user terminal.

[1262] Step 8:

[1263] After receiving the dish, the user provides a rating and feedback through a smartphone app. The input is the rating and feedback for the dish, and the output is the rating data sent to the server. The operation is for the user to fill out the rating form in the app and submit it.

[1264] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1265] MODE FOR CARRYING OUT THE INVENTION

[1266] This system provides individually optimized meals by utilizing detailed personal data such as the user's food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, cooking method, etc. It also incorporates an emotion engine that recognizes the user's emotions and reflects them in real time, providing an even more personalized dining experience.

[1267] Below, the program processing of this system will be explained in natural language, with concrete examples.

[1268] User information registration and analysis

[1269] 1. User: Logs in to the system and enters information about their food preferences, allergy information, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method. Specifically, the information is entered in the form of answering questions from the application. For example, "User A" enters information such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[1270] 2. Terminal: Receives the information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[1271] 3. Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, it generates the appropriate food type and cooking method based on the user information, and generates a recommended food profile such as "spicy grilled chicken."

[1272] Introducing the Emotion Engine

[1273] 4. User: Logs in to the system and allows the emotion engine to recognize emotions. Specifically, the emotion engine allows the camera and microphone to analyze facial expressions and voice.

[1274] 5. Device: Acquires emotion data in real time and sends it to the server. Specifically, the device analyzes the user's facial expressions and voice using a camera and microphone, and sends the emotion recognition data to the server in real time.

[1275] 6. Server: Analyzes the received emotion data and reflects it in the cooking profile. Specifically, if User A seems to be "enjoying" the food, the server adjusts the spiciness slightly, for example.

[1276] User Orders

[1277] 7. User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[1278] 8. Terminal: Sends the user's order details to the server. Specifically, the order details of the dishes selected by the user and the profile data are sent to the server.

[1279] 9. Server: Based on the user's profile data, the server generates the optimal cooking method and portion sizes. It also references the recipe database and checks the availability of ingredients. Specifically, it checks the recipe and availability of "spicy grilled chicken."

[1280] Improving appearance with VR technology

[1281] 10. Server: When special ingredients such as insects are included, AI is used to generate data to correct visually undesirable aspects of the VR content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[1282] 11. Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[1283] 12. User: Through the VR device, users can visually enjoy the improved appearance of food. Specifically, users can wear a VR headset and enjoy eating insect steaks that look like beef steaks without any hesitation.

[1284] Food delivery and tracking

[1285] 13. Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[1286] 14. Device: The device tracks the food preparation process and delivery status in real time and displays them to the user. Specifically, the device displays the cooking progress and the delivery person's location information on the application to notify the user.

[1287] 15. User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[1288] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual improvement, emotional reflection, and delivery.

[1289] The processing flow will be explained below.

[1290] Step 1:

[1291] User: Logs into the system and enters information about their food preferences, allergy information, health condition, target nutrients, seasoning, appearance, texture, and cooking method. Specifically, the information is entered in the form of answering questions from the application. For example, "User A" enters information such as "I like chicken," "I like spicy food," and "I like low-fat, high-protein foods."

[1292] Step 2:

[1293] Terminal: Receives information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[1294] Step 3:

[1295] Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, it runs an algorithm that generates the appropriate food type and cooking method based on user information, generating a recommended food profile such as "spicy grilled chicken."

[1296] Step 4:

[1297] User: Allows the system to use the emotion engine. Specifically, the user sets the system to allow the emotion engine to analyze facial expressions and voice using the camera and microphone.

[1298] Step 5:

[1299] Device: Acquires emotion data in real time and sends it to the server. Specifically, the device analyzes the user's facial expressions and voice using a camera and microphone, and sends the emotion recognition data to the server in real time.

[1300] Step 6:

[1301] Server: Analyzes the received emotion data and reflects it in the cooking profile. Specifically, if User A seems to be "enjoying" the food, the server adjusts the spiciness a little.

[1302] Step 7:

[1303] User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[1304] Step 8:

[1305] Terminal: Sends the user's order details to the server. Specifically, it sends the order details of the dishes selected by the user along with the profile data to the server.

[1306] Step 9:

[1307] Server: Generates optimal cooking methods and portion sizes based on the user's profile data and emotional data. It also references the recipe database and checks the availability of ingredients. Specifically, it checks the recipe and inventory for "spicy grilled chicken."

[1308] Step 10:

[1309] Server: When special ingredients such as insects are included in the VR content, AI is used to generate data to correct visually undesirable aspects of the content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[1310] Step 11:

[1311] Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[1312] Step 12:

[1313] Users can visually enjoy the improved appearance of food through a VR device. Specifically, users can wear a VR headset and enjoy eating insect steaks that look like beef steaks without any hesitation.

[1314] Step 13:

[1315] Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[1316] Step 14:

[1317] Device: The device tracks the cooking process and delivery status in real time and displays them to the user. Specifically, the device displays information about the cooking process and the delivery person's location on the application to notify the user.

[1318] Step 15:

[1319] User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[1320] Example 2

[1321] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1322] Conventional systems have difficulty providing meals based on a user's individual preferences and health status, and lack a way to reflect these preferences in real time. Furthermore, they lack a way to improve the appearance of meals that contain ingredients that are visually undesirable. These issues must be resolved to provide users with a more personalized dining experience.

[1323] The identification process 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 analyzing received data, generating an optimal dish profile for the user using a generative AI model, and saving the profile in a database; means for incorporating an emotion engine that recognizes the user's emotions and reflecting the user's emotion data in the user profile; and means for transmitting the generated dish data to a cooking location or affiliated restaurant and requesting cooking. This makes it possible to provide individually optimized dishes, adjust the dishes in real time based on the user's emotions, and provide visually appealing ingredients.

[1324] A "user" is a person who orders food by inputting information about personal preferences, allergy information, health information, nutritional goals, taste, appearance, texture, and cooking method into the system.

[1325] "Preferences" refers to personal preferences such as the ingredients, characteristics of dishes, and seasonings that the user prefers.

[1326] "Allergy information" is information about allergens to which a user has an allergy, and is used to avoid allergic reactions to specific food ingredients or components.

[1327] "Health Information" is information related to a user's health status and health goals, including intake restrictions and recommendations for certain nutrients.

[1328] "Nutrition goal" refers to the nutrient balance or target value that a user wishes to achieve.

[1329] "Taste" is information that indicates the type and intensity of flavors that a user prefers.

[1330] "Appearance" refers to the visual characteristics that a user expects from a dish.

[1331] "Texture" refers to information about the physical texture, hardness, and softness that a user feels when eating food.

[1332] "Cooking methods" refers to information about the means, techniques, and procedures for preparing food, including specific cooking techniques such as baking, boiling, and steaming.

[1333] "Terminal means" refers to a device through which a user inputs information and transmits the information to a server in real time.

[1334] "Server means" refers to a system device that analyzes the received data, generates a cooking profile that is optimal for the user, and stores it in a database.

[1335] "Generative AI model" refers to an artificial intelligence mechanism that analyzes received user data and generates an optimal cooking profile.

[1336] An "emotion engine" refers to a system that has the ability to recognize a user's emotions, analyze that data, and reflect it in a cooking profile.

[1337] "VR Device" means a device that uses virtual reality technology to enable a user to visually enhance the appearance of food being served.

[1338] "Cooking location" refers to the location where the food is actually cooked based on the generated cooking profile.

[1339] "Partner restaurant" refers to a restaurant that partners with the system and provides food to users.

[1340] MODE FOR CARRYING OUT THE INVENTION

[1341] This system provides individually optimized meals by utilizing detailed personal data such as the user's preferences, allergy information, health information, nutritional goals, taste, appearance, texture, cooking method, etc. Furthermore, by incorporating an emotion engine that recognizes the user's emotions and reflects them in real time, it provides an even more personalized dining experience.

[1342] User information registration and analysis

[1343] Users log in to the system and enter information about their preferences, allergies, health information, nutritional goals, preferred seasonings, appearance, texture, and cooking methods. Input is done by answering questions within the application. For example, a user might enter information such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[1344] The device receives the information entered by the user in real time and sends it to the server. The data is encrypted using the Transport Layer Security (TLS) protocol and safely transferred to the server.

[1345] The server decrypts the received data using AES (Advanced Encryption Standard) and launches a generative AI model for analysis. The generative AI model analyzes the user's preferences and health information and generates a corresponding food profile. For example, the user may be suggested "spicy grilled chicken." The generated profile is then stored in a database.

[1346] Introducing the Emotion Engine

[1347] After logging in to the system, the user authorizes the use of the emotion engine by selecting "Allow emotion recognition" within the application. This activates the device's camera and microphone to collect the user's facial expressions and voice data.

[1348] The device sends the collected emotional data to the server in real time. The server analyzes the received emotional data and reflects it in the cooking profile. For example, if the user is "having fun," the server may adjust the spiciness a little.

[1349] User Orders

[1350] The user selects the desired dish from the list of dishes suggested by the application and clicks the "Order" button. The device encrypts the selected dish profile and order data and sends it to the server.

[1351] The server analyzes the received order data and generates the optimal cooking method and portion size. It also references the recipe database to check the availability of the necessary ingredients. For example, it checks the specific steps and ingredients for cooking "spicy grilled chicken."

[1352] Improving appearance with VR technology

[1353] When special ingredients are included, the server uses AI to generate VR content that corrects visually undesirable aspects. For example, it generates a 3D model and video data to make an insect steak look like a beef steak, and sends them to the device.

[1354] The device displays the data sent from the server on the VR headset, providing the user with visually improved ingredients, allowing the user to visually enjoy the cooking process through the VR headset.

[1355] Food delivery and tracking

[1356] The server sends the generated dish data to the cooking location or partner restaurant, requests cooking, and sends detailed cooking instructions and order details to request the start of cooking.

[1357] The device notifies the user in real time about the cooking process and delivery status, and the progress and location of the delivery person are displayed in the application.

[1358] Users can track the progress of their order until it arrives in the app, and provide feedback after it arrives by entering ratings and adding comments about the taste, quantity, appearance, and service.

[1359] Examples of specific examples and prompts

[1360] As a concrete example, we explain the process of a user entering information such as "I like chicken," "I like spicy food," and "Low fat, high protein" to order "spicy grilled chicken." We also include a process for users to visually improve their cooking using VR.

[1361] Example prompts to input to a generative AI model:

[1362] The user entered "nut allergy" as their allergy information, "on a diet" as their health information, "spicy" as their preferred seasoning, and "colorful" as their appearance preference. Based on this, please suggest low-calorie, high-protein menus. Also, if the dish looks like edible insects, please generate a 3D model to make it look like beefsteak, and further optimize it based on the user's emotional data.

[1363] As described above, the present invention is a system that utilizes detailed personal data and real-time emotional data of users to create and serve individually optimized dishes.

[1364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1365] Step 1:

[1366] Users log in to the system and enter information about their personal preferences, allergy information, health information, nutritional goals, taste, appearance, texture, and cooking method. For example, they enter information in the form of answering questions in the application, providing information such as "I like chicken," "I like spicy food," and "Low fat, high protein." The entered information is sent to the terminal.

[1367] input:

[1368] Personal information that users enter into the application

[1369] output:

[1370] User personal data sent to the device

[1371] Step 2:

[1372] The terminal receives the information entered by the user in real time, encrypts the data, for example, using the Transport Layer Security (TLS) protocol, and then transmits the encrypted data to the server.

[1373] input:

[1374] User's personal data

[1375] output:

[1376] Encrypted user data is sent to the server

[1377] Step 3:

[1378] The server receives the data sent from the device and uses AES (Advanced Encryption Standard) to decrypt it. The decrypted user data is then input into a generative AI model, which analyzes the data. As a result of the analysis, an optimal cooking profile is generated based on the user's preferences and health information. For example, based on information such as "I like chicken" and "I like spicy food," "spicy grilled chicken" is suggested.

[1379] input:

[1380] Encrypted user data

[1381] output:

[1382] Decrypted user data

[1383] Food profile as analysis result

[1384] Step 4:

[1385] The server stores the generated cooking profile in a database, which contains user-specific information linked to the user ID.

[1386] input:

[1387] Analyzed food profile

[1388] output:

[1389] Cuisine profiles stored in a database

[1390] Step 5:

[1391] The user allows the use of the emotion engine by selecting "Allow emotion recognition" within the application.

[1392] input:

[1393] User Permissions

[1394] output:

[1395] Starting emotion recognition

[1396] Step 6:

[1397] The device activates the camera and microphone to collect the user's facial expressions and voice data, and transmits the collected emotional data to the server in real time.

[1398] input:

[1399] User facial expression data

[1400] User voice data

[1401] output:

[1402] Emotion data sent to the server in real time

[1403] Step 7:

[1404] The server analyzes the received emotion data and reflects it in the cooking profile. For example, if the user seems to be enjoying the food, it may adjust the spiciness a little.

[1405] input:

[1406] Emotional Data

[1407] output:

[1408] Tailored food profiles

[1409] Step 8:

[1410] The user selects the desired dish from the list of dishes suggested by the application and clicks the "Order" button. The device encrypts the selected dish profile and order data and sends it to the server.

[1411] input:

[1412] User's food selection

[1413] Order Data

[1414] output:

[1415] Encrypted Order Data

[1416] Step 9:

[1417] The server analyzes the received order data and generates the optimal cooking method and portion size. It also references the recipe database to check the availability of the necessary ingredients. For example, it checks the specific steps and ingredients for cooking "spicy grilled chicken."

[1418] input:

[1419] Encrypted Order Data

[1420] Recipe Database

[1421] output:

[1422] Best cooking method and portion size

[1423] Step 10:

[1424] The server sends the generated dish data to the cooking location or partner restaurant, requests cooking, and sends detailed cooking instructions and order details to request the start of cooking.

[1425] input:

[1426] Order details

[1427] Cooking method data

[1428] output:

[1429] Cooking location or cooking request data for partner restaurants

[1430] Step 11:

[1431] The device notifies the user in real time about the cooking process and delivery status, for example by displaying the progress and the delivery person's location in the application.

[1432] input:

[1433] Cooking progress information

[1434] Delivery status information

[1435] output:

[1436] User Notification

[1437] Step 12:

[1438] Users can check the progress of their order through the application until it arrives. After the food arrives, they can provide feedback and ratings through the application. They can enter ratings for taste, quantity, appearance, and service, and add comments.

[1439] input:

[1440] Progress Information

[1441] Evaluation items

[1442] output:

[1443] Ratings and Feedback Data

[1444] Step 13:

[1445] When special ingredients such as insects are included, the server uses AI to generate VR content that corrects visually undesirable aspects. For example, it generates a 3D model and video data to make an insect steak look like a beef steak, and sends them to the device.

[1446] input:

[1447] Special food information

[1448] output:

[1449] Generated VR content

[1450] Step 14:

[1451] The device displays the data sent from the server on the VR headset, providing the user with visually improved ingredients, allowing the user to visually enjoy the cooking process through the VR headset.

[1452] input:

[1453] Generated VR content

[1454] output:

[1455] VR display to the user

[1456] (Application example 2)

[1457] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1458] Conventional personalized meal delivery systems suggest dishes based on a user's ingredient preferences, allergy information, health status, cooking methods, etc., but because they do not take the user's emotions into account, the suggested dishes may not be optimal. Furthermore, dishes containing visually undesirable ingredients may reduce the user's appetite. Therefore, a system that further personalizes the dining experience and stimulates the user's appetite is needed.

[1459] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input information regarding personal food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method; terminal means for receiving the input information in real time and transmitting it to the server; means for analyzing the received data, using artificial intelligence to generate a cooking profile optimal for the user, and storing it in a database; and terminal means for recognizing the user's emotions and reflecting them in the cooking profile. This enables more personalized cooking suggestions that take into account the user's emotions in addition to their preferences and health status. Furthermore, visually undesirable parts can be corrected using a virtual reality device, thereby maintaining or improving the user's appetite.

[1460] A "user" is an individual who uses the system to provide information such as food preferences, allergy information, and health conditions, and receives the most suitable meal.

[1461] "Ingredient preferences" is information about specific ingredients, cooking methods, and seasonings that the user prefers.

[1462] "Allergy information" is information about specific food ingredients to which the user is allergic.

[1463] "Health status" refers to data related to the user's health, such as blood pressure and cholesterol levels.

[1464] A "nutrient target" is a specific nutritional goal that a user wishes to achieve.

[1465] "Seasoning" is information about the flavor of food and the use of spices according to the user's preferences.

[1466] "Appearance" refers to information about the visual elements that users desire in a dish, such as presentation and color.

[1467] "Texture" refers to information about a user's preferences regarding the mouthfeel and texture of a dish.

[1468] "Cooking method" is information about the cooking method preferred by the user, such as baking, boiling, frying, etc.

[1469] A "terminal" is hardware that allows a user to input information and send it to a server.

[1470] A "server" is a computer system that analyzes data received from users and generates an optimal cooking profile.

[1471] "Artificial intelligence" is the technology used to analyze the received data and generate the optimal cooking profile for the user.

[1472] An "emotion-recognizing device" is a combination of hardware and software that analyzes a user's facial expressions and voice and collects emotional data.

[1473] A "cuisine profile" is a data set that details the cuisine that is best suited to a user.

[1474] A "virtual reality device" is a device that incorporates virtual reality technology used to correct visual imperfections.

[1475] A "visually objectionable portion" refers to an ingredient or part of a dish that a user finds visually objectionable.

[1476] To implement this invention, three main components are used: a user, a terminal, and a server. The following describes these components and their respective operations in detail.

[1477] Enter user information

[1478] Users use devices such as smartphones or head-mounted displays to input information about their food preferences, allergies, health conditions, target nutrients, seasonings, appearance, texture, and cooking methods. The device, equipped with an emotion engine, obtains emotional data in real time from the user's facial expressions and voice.

[1479] Receiving and analyzing data

[1480] The device sends the input information and emotional data in an encrypted format to the server. The server analyzes the received user data and uses a generative AI model to generate an optimal cooking profile for each user, which is then stored in a database. The generative AI model selects dishes based on the user's ingredient preferences and health status.

[1481] Emotion-based food adjustment

[1482] When data is acquired by the emotion engine, the server analyzes it and reflects it in the cooking profile. For example, if the user is "having fun," the server can adjust the spiciness and seasoning of the food based on that emotion.

[1483] Visual improvements

[1484] If the server detects ingredients that are visually objectionable to the user, it generates data to correct the problem using a virtual reality device. This data is sent to the user's device, and the user can view the corrected visual data through a VR headset. Specifically, it generates a 3D model and video data to make a dish containing insects look like beefsteak.

[1485] Food Serving

[1486] The server then sends the created dish profile to the kitchen or partner restaurant to request cooking. The cooking process and delivery status are displayed to the user in real time via the device. The user can track the waiting time and provide feedback after the food arrives.

[1487] The specific hardware and software used

[1488] Devices: Smartphone, head-mounted display, VR headset (emotion data analysis and user information input)

[1489] Server: Data analysis server, database (for generating cooking profiles and storing data)

[1490] Software: Generative AI model (AI data analysis), emotion recognition engine (emotion data analysis)

[1491] Examples and prompts

[1492] As a specific example, the following case will be described where the user likes chicken, likes spicy food, and has recently been feeling "happy."

[1493] Example prompt sentence:

[1494] {

[1495] "user_id": "userA",

[1496] "preferences": {

[1497] "meat": "chicken",

[1498] "spiciness": "high",

[1499] "diet": "low-fat, high-protein"

[1500] },

[1501] "allergies": ["gluten"],

[1502] "health": {"blood_pressure": "normal", "cholesterol": "low"},

[1503] "emotion": "happy"

[1504] }

[1505] In this way, a personalized dining experience can be provided to the user.

[1506] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1507] Step 1:

[1508] The user uses a smartphone or head-mounted display to input information such as food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method. In addition, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data. This information is then input into the device.

[1509] Input: User's personal data and emotional data

[1510] Output: Real-time encrypted user data

[1511] Step 2:

[1512] The device receives the input information in real time and transmits it in encrypted form to the server, where the device aggregates, encrypts, and transmits the data securely.

[1513] Input: User's encrypted data

[1514] Output: Encrypted data sent to the server

[1515] Step 3:

[1516] The server analyzes the received data and uses a generative AI model to generate an optimal cooking profile for each user, which is then stored in a database. Here, the AI ​​determines the type of food, cooking method, nutrient balance, etc. based on the user's preferences and health status.

[1517] Input: Encrypted user data

[1518] Output: Food profiles stored in a database

[1519] Step 4:

[1520] The user's emotional data is sent to the server in real time, where it is analyzed. Based on the analysis results, the emotional data is reflected in the cooking profile. For example, if the user appears to be "having fun," the spiciness of the food may be slightly increased, and other adjustments may be made in real time.

[1521] Input: Emotion data

[1522] Output: Adjusted cooking profile

[1523] Step 5:

[1524] The server determines the recommended dishes, cooking methods, and portion sizes based on the user's profile data, and generates visual correction data for the virtual reality device: a 3D model and video data that makes the insect appear like a beefsteak.

[1525] Input: Food profiles stored in the database

[1526] Output: Vision correction data for virtual reality devices

[1527] Step 6:

[1528] The device receives the visual correction data sent from the server and transmits it to the virtual reality device. When the user puts on the VR headset, the visually improved dish is displayed.

[1529] Input: Vision correction data

[1530] Output: Image displayed on a virtual reality device

[1531] Step 7:

[1532] The server sends the created dish profile to the kitchen or partner restaurant and requests cooking. Here, the server sends detailed cooking instructions and order details and requests the start of cooking.

[1533] Input: Cuisine Profile

[1534] Output: The kitchen or partner restaurant that received the cooking instructions

[1535] Step 8:

[1536] The device displays the cooking process and delivery status to the user in real time, and the user can check the cooking status and delivery person's location through the application.

[1537] Input: Cooking progress, delivery information

[1538] Output: Real-time information displayed on the user's device

[1539] Step 9:

[1540] Users wait for their food to arrive and then provide feedback and ratings on the food and service through the app, which collects data to improve the service.

[1541] Input: Food rating, feedback

[1542] Output: Evaluation data and feedback stored on the server

[1543] Through these steps, a series of processes is realized, from user input to generating the optimal dish, visual improvement, reflection of emotions, and finally serving it.

[1544] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1545] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1546] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1547] [Fourth embodiment]

[1548] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1549] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1550] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1551] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1552] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1553] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1554] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1555] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1556] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1557] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1558] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1559] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1560] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1561] MODE FOR CARRYING OUT THE INVENTION

[1562] This invention provides a system that links users, devices, and servers to provide individually optimized meals. This system proposes and serves optimal meals based on the user's preferences and health status. Furthermore, by using VR technology to improve the visual experience, users can enjoy meals with unusual ingredients without any hesitation.

[1563] Below, the program processing of this system will be explained in natural language, with concrete examples.

[1564] User information registration and analysis

[1565] 1. User: Logs in to the system and enters information about their food preferences, allergies, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method.

[1566] Example: User A starts a smartphone app and answers questions, entering information such as "My favorite food is chicken," "I like spicy food," and "I prefer low-fat, high-protein meals."

[1567] 2. Terminal: Receives information entered by the user in real time and sends it to the server.

[1568] Example: Once the user has completed entering the data, the device automatically encrypts the data and sends it to the server.

[1569] 3. Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database.

[1570] Example: The server analyzes user A's information, generates a recommendation for "spicy grilled chicken," and saves it as a profile.

[1571] User Orders

[1572] 4. User: Logs into the system and places a food order.

[1573] Example: User A enters "I want spicy grilled chicken" into the app and presses the order button.

[1574] 5. Terminal: Sends the user's order details to the server.

[1575] Example: The device links user A's order information and profile and sends a request to the server.

[1576] 6. Server: Generates optimal cooking methods and portion sizes based on the user's profile data, and also checks the availability of necessary ingredients.

[1577] Example: A server generates a specific recipe and measurements for cooking "spicy grilled chicken" and checks availability.

[1578] Improving appearance with VR technology

[1579] 7. Server: When using special ingredients such as insects, data is generated to correct visually undesirable aspects using VR technology.

[1580] Example: The server generates a 3D model to make "insect steak" look like beef steak, and creates it as VR data.

[1581] 8. Terminal: Displays the data sent from the server on a device such as a VR headset.

[1582] Example: A VR device displays a retouched image of a real dish, showing user A a visually enhanced beef steak.

[1583] 9. Users: They can see the improved appearance of ingredients through the VR device and enjoy eating without any hesitation.

[1584] Example: User A puts on a VR headset and eats "insect steak" while watching a beautified image, and enjoys the meal without any resistance.

[1585] Food delivery and tracking

[1586] 10. Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking.

[1587] Example: A server sends order data and cooking instructions for "spicy grilled chicken" to a partner restaurant.

[1588] 11. Terminal: Displays information to the user to track the food preparation process and delivery status in real time.

[1589] Example: The device notifies user A in real time that food is being cooked or that the delivery person has departed.

[1590] 12. Users: Track how long they wait for their order to arrive and provide a rating and feedback upon completion.

[1591] Example: User A receives a meal and rates it "very delicious" on the app.

[1592] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual enhancement, and serving.

[1593] The processing flow will be explained below.

[1594] Step 1:

[1595] User: Logs in to the system and enters information about their food preferences, allergy information, health status, target nutrients, preferred seasoning, appearance, texture, and cooking method. Specifically, the user responds to questions in the application by operating the system to enter specific preferences and health information, such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[1596] Step 2:

[1597] Terminal: Receives information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[1598] Step 3:

[1599] Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, the server runs an algorithm to generate the appropriate cooking type and cooking method based on information about food preferences and health status, generating a recommended cooking profile such as "spicy grilled chicken."

[1600] Step 4:

[1601] User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[1602] Step 5:

[1603] Terminal: Sends the user's order details to the server. Specifically, it sends the order details of the dishes selected by the user along with the profile data to the server.

[1604] Step 6:

[1605] Server: Generates optimal cooking methods and quantities based on the user's profile data. It also references the recipe database and checks the availability of ingredients. Specifically, it generates specific cooking instructions and quantities for "spicy grilled chicken" and connects with the inventory management system of partner restaurants to check the ingredients.

[1606] Step 7:

[1607] Server: When special ingredients such as insects are included in the VR content, AI is used to generate data to correct visually undesirable aspects of the content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[1608] Step 8:

[1609] Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[1610] Step 9:

[1611] Users can visually enjoy the improved appearance of food through a VR device. Specifically, users can wear a VR headset and enjoy eating the actual food while looking at the beautified food, allowing them to enjoy their meal without experiencing any visual discomfort.

[1612] Step 10:

[1613] Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[1614] Step 11:

[1615] Device: The device tracks the cooking process and delivery status in real time and displays them to the user. Specifically, the device displays information about the cooking process and the delivery person's location on the application to notify the user.

[1616] Step 12:

[1617] User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[1618] Example 1

[1619] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1620] Conventional meal recommendation systems have difficulty providing individually optimized meals based on the user's preferences and health status, and they also lack the means to improve visually unfavorable ingredients using VR technology. Therefore, there is a need for a system that allows users to enjoy healthy and optimal meals without any resistance.

[1621] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1622] In this invention, the server includes a means for a user to input information about personal food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method, a terminal means for receiving the input information in real time and sending it to the server, and a means for analyzing the received data and using a generative AI model to generate a cooking profile optimal for the user and store it in a database. This individually optimizes the user's dining experience and enables more satisfying meal suggestions, including visual improvements.

[1623] A "user" is an entity that uses the system to input information about personal food preferences and health status, and has the system suggest optimal dishes.

[1624] The "terminal means" is a device or application for receiving information input by a user in real time and transmitting it to a server.

[1625] The "server means" is a part of the system that analyzes the received user data, generates an optimal cooking profile using a generative AI model, and stores it in a database.

[1626] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate optimal cooking profiles based on user data.

[1627] A "cooking profile" is a data set that includes optimal cooking recipes, cooking methods, and portion sizes based on a user's preferences and health status.

[1628] A "VR device" is a device that uses virtual reality technology to visually improve the appearance of food.

[1629] A "cooking facility or affiliated facility" is a place or company that actually cooks and serves food based on the cooking data sent from the server.

[1630] The "terminal means for providing ratings and feedback" is a device or application that allows a user to input ratings and feedback on a dish received and transmit them to the server.

[1631] This invention provides a system that links users, devices, and servers to provide individually optimized meals for each user. The system proposes optimal dishes based on the user's preferences and health status, and further improves the appearance of specific ingredients using VR technology, aiming to allow users to enjoy meals more comfortably.

[1632] System Configuration

[1633] Hardware and software used

[1634] Terminal: A device such as a smartphone or tablet on which a user inputs and receives information, with a dedicated application installed.

[1635] Server: A central server that analyzes user data and uses generative AI models to generate and store optimal cooking profiles.

[1636] VR Device: A device that uses virtual reality technology to visually enhance the appearance of food (e.g., a VR headset).

[1637] Processing steps

[1638] User information registration and analysis

[1639] 1. User: Logs in to the system and enters information such as their food preferences, allergy information, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method.

[1640] Example: User A starts a smartphone app and enters information such as "My favorite food is chicken," "I like spicy food," and "I prefer low-fat, high-protein meals."

[1641] 2. Terminal: Receives information entered by the user in real time, encrypts it, and sends it to the server.

[1642] Example: Once the user has completed entering the data, the device automatically encrypts the data and sends it to the server.

[1643] 3. Server: Analyzes the received user data and uses a generative AI model to generate the optimal cooking profile for each user, which is then stored in a database.

[1644] Example: The server analyzes user A's information, generates suggestions such as "spicy grilled chicken," and saves them in a database as a profile.

[1645] Improving appearance with VR technology

[1646] 1. Server: When using special ingredients such as insects, generate VR data to correct visually undesirable aspects.

[1647] Example: Generate a 3D model to make "insect steak" look like beef steak and create it as VR data.

[1648] 2. Terminal: Displays the VR data sent from the server on a device such as a VR headset.

[1649] Example: A VR device displays a retouched image of a real dish, showing user A a visually enhanced beef steak.

[1650] 3. User: Eat while visually enjoying the improved appearance of ingredients through the VR device.

[1651] Example: User A puts on a VR headset and eats "insect steak" without any hesitation while watching a beautified image.

[1652] Specific examples and prompts for the generative AI model

[1653] To give a specific example, when User B inputs "vegetarian food," "high protein, low calories," and "strong sour taste" into the system, the server analyzes this and makes suggestions such as "spicy tofu steak." This suggestion is set up in the VR video so that the tofu appears as a steak served on a beautiful plate. User B can use this VR headset to enjoy the visually beautiful food.

[1654] An example of a prompt for a generative AI model is shown below.

[1655] "Please analyze the data entered by the user (food preferences, allergy information, health condition, target nutrients, seasoning preferences, appearance, texture, cooking method) and recommend the most suitable dish. Also, please generate a 3D model to adjust the appearance of the dish in VR to make it easier for users to eat insects."

[1656] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual enhancement, and serving.

[1657] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1658] Step 1:

[1659] Input: The user inputs personal food preferences, allergy information, health conditions, target nutrients, seasoning, appearance, texture, cooking method, etc.

[1660] Specific operation: The user logs in to a dedicated application using a device such as a smartphone or tablet and enters the necessary information into a questionnaire-style or multiple-choice form displayed on the system.

[1661] Output: The entered information is saved on the device.

[1662] Step 2:

[1663] Input: User-entered data.

[1664] Specific operation: Once the user has completed the input, the device performs a process to encrypt the data internally and sends it to the server.

[1665] Output: The encrypted data is sent to the server.

[1666] Step 3:

[1667] Input: Encrypted user data.

[1668] What happens: The server receives the encrypted data, decrypts it, and stores it in the database.

[1669] Output: The user data is decrypted and stored in the server's database.

[1670] Step 4:

[1671] Input: User data.

[1672] Specific operation: The server inputs the received data into the generative AI model, performs analysis, and generates the optimal cooking profile for the user.

[1673] Output: The analysis results in a food profile that is generated and stored in a database.

[1674] Step 5:

[1675] Input: Optimal cooking profile.

[1676] Specific operation: The user logs in to the system again and places a food order through the application.

[1677] Output: The order information is saved on the device.

[1678] Step 6:

[1679] Input: User's order data.

[1680] Specific operation: The terminal encrypts the order information and sends it to the server.

[1681] Output: The encrypted order data is sent to the server.

[1682] Step 7:

[1683] Input: Encrypted order data.

[1684] What it does: The server decrypts the encrypted order data and generates the optimal cooking method and portion size based on the user's profile data.

[1685] Output: The generated cooking recipe and quantity information are saved on the server.

[1686] Step 8:

[1687] Input: Optimal food cooking method and portion information.

[1688] Specific operation: When the server uses special ingredients such as insects, it generates VR data to correct any visually undesirable parts.

[1689] Output: The generated VR data is saved on the server.

[1690] Step 9:

[1691] Input: Generated VR data.

[1692] Specific operation: The device receives VR data from the server and displays it on the VR headset.

[1693] Output: The user's VR device displays a video of the improved food.

[1694] Step 10:

[1695] Input: Profile and order data.

[1696] Specific operation: The server sends the generated dish profile and order data to the cooking facility or affiliated facility and requests cooking.

[1697] Output: Cooking request data is sent to the cooking facility.

[1698] Step 11:

[1699] Input: Cooking progress information.

[1700] Specific operation: The server tracks the cooking process and delivery status in real time and sends that information to the terminal.

[1701] Output: Cooking and delivery status information is notified to the user.

[1702] Step 12:

[1703] Input: Food arrives.

[1704] Specific operation: After the food arrives, the user enters their rating and feedback using a dedicated application, and the device then sends the data to the server.

[1705] Output: The feedback information is sent to the server and stored.

[1706] Through these steps, users can enjoy a personalized and optimized dining experience.

[1707] (Application example 1)

[1708] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1709] Conventional food delivery systems have difficulty in suggesting optimal dishes tailored to individual user preferences and health conditions, and lack visual correction when using special ingredients, making it difficult to provide a satisfying dining experience for users. Furthermore, they lack real-time tracking and evaluation / feedback functions throughout the entire process from ordering to delivery.

[1710] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1711] In this invention, the server includes: means for a user to input information regarding personal preferences, allergy information, health condition, target nutrients, seasoning, appearance, texture, and cooking method; terminal means for receiving the input information in real time and sending it to the server; server means for analyzing the received data and using a generative AI model to generate a dish profile optimal for the user and store it in a database; and server means for receiving dish order information, generating a prompt for visual correction based on the generated dish profile, and sending it to the terminal. This makes it possible to suggest optimal dishes based on the user's individual preferences and health condition, and even when using special ingredients, visual correction can be performed to allow the user to enjoy a meal without any hesitation. It also makes it possible to track the entire process from order to delivery in real time and provide evaluations and feedback.

[1712] "User preferences" refers to the preferences or biases that a user has toward specific ingredients or seasonings.

[1713] "Allergy Information" refers to information about specific ingredients or substances that may cause an allergic reaction when ingested by a user.

[1714] "Health status" refers to information about the user's physical condition, illness, and health.

[1715] "Target nutrients" refers to the amount and balance of specific nutrients that a user aims to consume in order to maintain or improve their health.

[1716] "Seasoning" refers to the way food is seasoned using condiments or cooking methods.

[1717] "Appearance" refers to the visual characteristics of a dish, i.e., its appearance.

[1718] "Texture" refers to the tactile and physical sensations of food when it is eaten.

[1719] "Cooking method" refers to the specific steps and techniques used to prepare a dish.

[1720] "Terminal means" refers to a device that allows a user to input information and transmit it to a server.

[1721] "Server means" refers to a server that has the function of analyzing the received data, generating a cooking profile, and storing it in a database.

[1722] A "generative AI model" refers to an artificial intelligence system that generates optimal cooking profiles and visual correction prompts based on user information.

[1723] "Visual correction prompts" refer to instructions or data used to improve the appearance of dishes made with special ingredients.

[1724] "Vision correction device" refers to a device used to improve the visual impression of the food being served (e.g., a VR headset).

[1725] "Cooking facility" refers to the location where the food is actually prepared (e.g., kitchen or affiliated restaurant).

[1726] A "profile" refers to a set of information about dishes that are optimal for an individual user, generated based on the user's preferences, health status, target nutrients, etc.

[1727] "Real-time" refers to a time period in which data is collected, transmitted, analyzed, and displayed immediately, without delay.

[1728] This invention relates to a food delivery system that provides individually optimized meals for each user. The system proposes the most suitable meal based on the user's preferences and health condition, and supports the entire process from ordering to delivery. In addition, when special ingredients are used, a vision correction device is used to allow the user to enjoy their meal comfortably.

[1729] Hardware and software used

[1730] Smartphones: Used by users to input personal preferences and health information and place food orders.

[1731] Server: Receives the information sent by the user, generates the optimal food profile using the generative AI model, and generates prompts for visual correction and sends them to the device.

[1732] Generative AI model: Analyzes user information and generates optimal food profiles and visual correction prompts.

[1733] Vision correction devices: Devices that improve the visual impression of dishes containing special ingredients (e.g., VR headsets).

[1734] Food preparation facility: Where the food is actually prepared (e.g., kitchen or partner restaurant).

[1735] User information management

[1736] Users use a smartphone app to input information such as their preferences, allergies, health status, target nutrients, seasonings, appearance, texture, and cooking methods. This input data is sent to a server in real time. The server analyzes the received data and uses a generative AI model (e.g., using TensorFlow or PyTorch) to generate an optimal cooking profile for each user, which is then stored in a database.

[1737] Ordering food

[1738] A user orders food using a smartphone app. Once an order is placed, the device sends the information to a server. The server then generates the optimal cooking method and portion size based on the user's profile data. It also generates visual correction prompts as needed and sends them to the visual correction device.

[1739] Improving appearance with VR technology

[1740] When special ingredients (e.g., insects) are used, the server generates visual correction prompts to compensate for this and sends them to the device. Visual correction devices (e.g., VR headsets using Unity or Unreal Engine) can provide users with visually appealing images, making the meal more enjoyable.

[1741] Food delivery and tracking

[1742] The server then sends the generated recipe data to the cooking facility or partner restaurant and requests cooking. The cooking process and delivery status of the food are displayed to the user in real time via the terminal. The user can track the entire process from ordering to delivery, and can provide ratings and feedback after the food arrives.

[1743] Examples and prompts

[1744] 1. Example:

[1745] User A starts the smartphone app and inputs their preferences and health information. The server analyzes this information and suggests "spicy grilled chicken." User A places the order and can visually enjoy the insect steak, which looks like a beef steak, through the visual correction device.

[1746] 2. Example prompts for the generative AI model:

[1747] A user logs into a food delivery app and provides the following information:

[1748] Favorite food: Chicken

[1749] Favorite taste: Spicy food

[1750] Health Goals: High protein, low fat

[1751] Please let the server analyze this and suggest the best dish to serve, and also provide ways to improve the appearance if special ingredients are used.

[1752] This system allows users to enjoy individually optimized meals and special ingredients in a comfortable environment, and also allows them to track the entire process from order to delivery in real time and provide feedback.

[1753] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1754] Step 1:

[1755] Using a smartphone app, users input information such as their preferences, allergies, health status, target nutrients, seasoning, appearance, texture, and cooking method. This data is collected in real time by the device and sent to the server. The input includes various pieces of user information, and this is the output data sent to the server.

[1756] Step 2:

[1757] The server analyzes the received user data and uses a generative AI model (e.g., TensorFlow or PyTorch) to generate the optimal cooking profile for the user. This profile is stored in a database. The input is the user data, and the output is the cooking profile as the analysis result. Data processing involves suggesting optimal dishes based on preferences and health information.

[1758] Step 3:

[1759] A user opens a smartphone app and orders food. The order details are sent to the server by the device. The input is the food order information, and the output is the transmission of the order data to the server. The operation is the user performing the ordering operation.

[1760] Step 4:

[1761] The server receives the order and generates the optimal cooking method and portion size by comparing it with existing food profiles. It also generates visual correction prompts as needed and sends them to the terminal. The input is the order data, and the output is the cooking method, portion size, and visual correction prompts. Data calculations involve matching the profile with the order information and generating visual correction data.

[1762] Step 5:

[1763] The terminal receives the visual correction prompt text and displays it on the visual correction device (e.g., a VR headset). The input is the visual correction prompt text, and the output is the data displayed on the visual device. The operation is that the terminal interacts with the device to display the visual data.

[1764] Step 6:

[1765] The server sends the generated dish data to a cooking facility or affiliated restaurant to request cooking. The input is a dish profile and order data, and the output is cooking instructions to the cooking facility. The operation is that the server sends the data, and the cooking facility receives it and starts cooking.

[1766] Step 7:

[1767] The server tracks the cooking process and delivery status of the food and displays the information on the terminal in real time. The input is the progress of cooking and delivery, and the output is real-time status notification. The operation is to collect progress information and notify the user terminal.

[1768] Step 8:

[1769] After receiving the dish, the user provides a rating and feedback through a smartphone app. The input is the rating and feedback for the dish, and the output is the rating data sent to the server. The operation is for the user to fill out the rating form in the app and submit it.

[1770] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1771] MODE FOR CARRYING OUT THE INVENTION

[1772] This system provides individually optimized meals by utilizing detailed personal data such as the user's food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, cooking method, etc. It also incorporates an emotion engine that recognizes the user's emotions and reflects them in real time, providing an even more personalized dining experience.

[1773] Below, the program processing of this system will be explained in natural language, with concrete examples.

[1774] User information registration and analysis

[1775] 1. User: Logs in to the system and enters information about their food preferences, allergy information, health condition, target nutrients, preferred seasoning, appearance, texture, and cooking method. Specifically, the information is entered in the form of answering questions from the application. For example, "User A" enters information such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[1776] 2. Terminal: Receives the information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[1777] 3. Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, it generates the appropriate food type and cooking method based on the user information, and generates a recommended food profile such as "spicy grilled chicken."

[1778] Introducing the Emotion Engine

[1779] 4. User: Logs in to the system and allows the emotion engine to recognize emotions. Specifically, the emotion engine allows the camera and microphone to analyze facial expressions and voice.

[1780] 5. Device: Acquires emotion data in real time and sends it to the server. Specifically, the device analyzes the user's facial expressions and voice using a camera and microphone, and sends the emotion recognition data to the server in real time.

[1781] 6. Server: Analyzes the received emotion data and reflects it in the cooking profile. Specifically, if User A seems to be "enjoying" the food, the server adjusts the spiciness slightly, for example.

[1782] User Orders

[1783] 7. User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[1784] 8. Terminal: Sends the user's order details to the server. Specifically, the order details of the dishes selected by the user and the profile data are sent to the server.

[1785] 9. Server: Based on the user's profile data, the server generates the optimal cooking method and portion sizes. It also references the recipe database and checks the availability of ingredients. Specifically, it checks the recipe and availability of "spicy grilled chicken."

[1786] Improving appearance with VR technology

[1787] 10. Server: When special ingredients such as insects are included, AI is used to generate data to correct visually undesirable aspects of the VR content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[1788] 11. Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[1789] 12. User: Through the VR device, users can visually enjoy the improved appearance of food. Specifically, users can wear a VR headset and enjoy eating insect steaks that look like beef steaks without any hesitation.

[1790] Food delivery and tracking

[1791] 13. Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[1792] 14. Device: The device tracks the food preparation process and delivery status in real time and displays them to the user. Specifically, the device displays the cooking progress and the delivery person's location information on the application to notify the user.

[1793] 15. User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[1794] In this way, a personalized and optimized dining experience is provided through a series of processes, from user input to generating the optimal dish, visual improvement, emotional reflection, and delivery.

[1795] The processing flow will be explained below.

[1796] Step 1:

[1797] User: Logs into the system and enters information about their food preferences, allergy information, health condition, target nutrients, seasoning, appearance, texture, and cooking method. Specifically, the information is entered in the form of answering questions from the application. For example, "User A" enters information such as "I like chicken," "I like spicy food," and "I like low-fat, high-protein foods."

[1798] Step 2:

[1799] Terminal: Receives information entered by the user in real time and sends it to the server. Specifically, once the information is entered, the terminal automatically aggregates it and sends the data to the server in encrypted form.

[1800] Step 3:

[1801] Server: Analyzes the received user data and uses AI to generate the optimal cooking profile for each user, which is then stored in a database. Specifically, it runs an algorithm that generates the appropriate food type and cooking method based on user information, generating a recommended food profile such as "spicy grilled chicken."

[1802] Step 4:

[1803] User: Allows the system to use the emotion engine. Specifically, the user sets the system to allow the emotion engine to analyze facial expressions and voice using the camera and microphone.

[1804] Step 5:

[1805] Device: Acquires emotion data in real time and sends it to the server. Specifically, the device analyzes the user's facial expressions and voice using a camera and microphone, and sends the emotion recognition data to the server in real time.

[1806] Step 6:

[1807] Server: Analyzes the received emotion data and reflects it in the cooking profile. Specifically, if User A seems to be "enjoying" the food, the server adjusts the spiciness a little.

[1808] Step 7:

[1809] User: Logs in to the system again and places an order for food. Specifically, the user selects the desired food from the list of dishes suggested by the application and clicks the "Order" button.

[1810] Step 8:

[1811] Terminal: Sends the user's order details to the server. Specifically, it sends the order details of the dishes selected by the user along with the profile data to the server.

[1812] Step 9:

[1813] Server: Generates optimal cooking methods and portion sizes based on the user's profile data and emotional data. It also references the recipe database and checks the availability of ingredients. Specifically, it checks the recipe and inventory for "spicy grilled chicken."

[1814] Step 10:

[1815] Server: When special ingredients such as insects are included in the VR content, AI is used to generate data to correct visually undesirable aspects of the content. Specifically, it generates a 3D model and video data to make the insect steak look like a beef steak.

[1816] Step 11:

[1817] Terminal: The data sent from the server is displayed on the VR headset. Specifically, the actual cooking is synchronized with the VR device, and visually improved images are displayed to the user in real time.

[1818] Step 12:

[1819] Users can visually enjoy the improved appearance of food through a VR device. Specifically, users can wear a VR headset and enjoy eating insect steaks that look like beef steaks without any hesitation.

[1820] Step 13:

[1821] Server: Sends the generated dish data to the kitchen or partner restaurant and requests cooking. Specifically, it sends detailed cooking instructions and order details to the partner restaurant's system and requests that cooking begin.

[1822] Step 14:

[1823] Device: The device tracks the cooking process and delivery status in real time and displays them to the user. Specifically, the device displays information about the cooking process and the delivery person's location on the application to notify the user.

[1824] Step 15:

[1825] User: Tracks the waiting time for the ordered food to arrive and provides ratings and feedback through the application after it arrives. Specifically, the user checks the delivery progress and enters a rating on the taste and service after the food arrives.

[1826] Example 2

[1827] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1828] Conventional systems have difficulty providing meals based on a user's individual preferences and health status, and lack a way to reflect these preferences in real time. Furthermore, they lack a way to improve the appearance of meals that contain ingredients that are visually undesirable. These issues must be resolved to provide users with a more personalized dining experience.

[1829] The identification process 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 analyzing received data, generating an optimal dish profile for the user using a generative AI model, and saving the profile in a database; means for incorporating an emotion engine that recognizes the user's emotions and reflecting the user's emotion data in the user profile; and means for transmitting the generated dish data to a cooking location or affiliated restaurant and requesting cooking. This makes it possible to provide individually optimized dishes, adjust the dishes in real time based on the user's emotions, and provide visually appealing ingredients.

[1830] A "user" is a person who orders food by inputting information about personal preferences, allergy information, health information, nutritional goals, taste, appearance, texture, and cooking method into the system.

[1831] "Preferences" refers to personal preferences such as the ingredients, characteristics of dishes, and seasonings that the user prefers.

[1832] "Allergy information" is information about allergens to which a user has an allergy, and is used to avoid allergic reactions to specific food ingredients or components.

[1833] "Health Information" is information related to a user's health status and health goals, including intake restrictions and recommendations for certain nutrients.

[1834] "Nutrition goal" refers to the nutrient balance or target value that a user wishes to achieve.

[1835] "Taste" is information that indicates the type and intensity of flavors that a user prefers.

[1836] "Appearance" refers to the visual characteristics that a user expects from a dish.

[1837] "Texture" refers to information about the physical texture, hardness, and softness that a user feels when eating food.

[1838] "Cooking methods" refers to information about the means, techniques, and procedures for preparing food, including specific cooking techniques such as baking, boiling, and steaming.

[1839] "Terminal means" refers to a device through which a user inputs information and transmits the information to a server in real time.

[1840] "Server means" refers to a system device that analyzes the received data, generates a cooking profile that is optimal for the user, and stores it in a database.

[1841] "Generative AI model" refers to an artificial intelligence mechanism that analyzes received user data and generates an optimal cooking profile.

[1842] An "emotion engine" refers to a system that has the ability to recognize a user's emotions, analyze that data, and reflect it in a cooking profile.

[1843] "VR Device" means a device that uses virtual reality technology to enable a user to visually enhance the appearance of food being served.

[1844] "Cooking location" refers to the location where the food is actually cooked based on the generated cooking profile.

[1845] "Partner restaurant" refers to a restaurant that partners with the system and provides food to users.

[1846] MODE FOR CARRYING OUT THE INVENTION

[1847] This system provides individually optimized meals by utilizing detailed personal data such as the user's preferences, allergy information, health information, nutritional goals, taste, appearance, texture, cooking method, etc. Furthermore, by incorporating an emotion engine that recognizes the user's emotions and reflects them in real time, it provides an even more personalized dining experience.

[1848] User information registration and analysis

[1849] Users log in to the system and enter information about their preferences, allergies, health information, nutritional goals, preferred seasonings, appearance, texture, and cooking methods. Input is done by answering questions within the application. For example, a user might enter information such as "I like chicken," "I like spicy food," and "Low fat, high protein."

[1850] The device receives the information entered by the user in real time and sends it to the server. The data is encrypted using the Transport Layer Security (TLS) protocol and safely transferred to the server.

[1851] The server decrypts the received data using AES (Advanced Encryption Standard) and launches a generative AI model for analysis. The generative AI model analyzes the user's preferences and health information and generates a corresponding food profile. For example, the user may be suggested "spicy grilled chicken." The generated profile is then stored in a database.

[1852] Introducing the Emotion Engine

[1853] After logging in to the system, the user authorizes the use of the emotion engine by selecting "Allow emotion recognition" within the application. This activates the device's camera and microphone to collect the user's facial expressions and voice data.

[1854] The device sends the collected emotional data to the server in real time. The server analyzes the received emotional data and reflects it in the cooking profile. For example, if the user is "having fun," the server may adjust the spiciness a little.

[1855] User Orders

[1856] The user selects the desired dish from the list of dishes suggested by the application and clicks the "Order" button. The device encrypts the selected dish profile and order data and sends it to the server.

[1857] The server analyzes the received order data and generates the optimal cooking method and portion size. It also references the recipe database to check the availability of the necessary ingredients. For example, it checks the specific steps and ingredients for cooking "spicy grilled chicken."

[1858] Improving appearance with VR technology

[1859] When special ingredients are included, the server uses AI to generate VR content that corrects visually undesirable aspects. For example, it generates a 3D model and video data to make an insect steak look like a beef steak, and sends them to the device.

[1860] The device displays the data sent from the server on the VR headset, providing the user with visually improved ingredients, allowing the user to visually enjoy the cooking process through the VR headset.

[1861] Food delivery and tracking

[1862] The server sends the generated dish data to the cooking location or partner restaurant, requests cooking, and sends detailed cooking instructions and order details to request the start of cooking.

[1863] The device notifies the user in real time about the cooking process and delivery status, and the progress and location of the delivery person are displayed in the application.

[1864] Users can track the progress of their order until it arrives in the app, and provide feedback after it arrives by entering ratings and adding comments about the taste, quantity, appearance, and service.

[1865] Examples of specific examples and prompts

[1866] As a concrete example, we explain the process of a user entering information such as "I like chicken," "I like spicy food," and "Low fat, high protein" to order "spicy grilled chicken." We also include a process for users to visually improve their cooking using VR.

[1867] Example prompts to input to a generative AI model:

[1868] The user entered "nut allergy" as their allergy information, "on a diet" as their health information, "spicy" as their preferred seasoning, and "colorful" as their appearance preference. Based on this, please suggest low-calorie, high-protein menus. Also, if the dish looks like edible insects, please generate a 3D model to make it look like beefsteak, and further optimize it based on the user's emotional data.

[1869] As described above, the present invention is a system that utilizes detailed personal data and real-time emotional data of users to create and serve individually optimized dishes.

[1870] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1871] Step 1:

[1872] Users log in to the system and enter information about their personal preferences, allergy information, health information, nutritional goals, taste, appearance, texture, and cooking method. For example, they enter information in the form of answering questions in the application, providing information such as "I like chicken," "I like spicy food," and "Low fat, high protein." The entered information is sent to the terminal.

[1873] input:

[1874] Personal information that users enter into the application

[1875] output:

[1876] User personal data sent to the device

[1877] Step 2:

[1878] The terminal receives the information entered by the user in real time, encrypts the data, for example, using the Transport Layer Security (TLS) protocol, and then transmits the encrypted data to the server.

[1879] input:

[1880] User's personal data

[1881] output:

[1882] Encrypted user data is sent to the server

[1883] Step 3:

[1884] The server receives the data sent from the device and uses AES (Advanced Encryption Standard) to decrypt it. The decrypted user data is then input into a generative AI model, which analyzes the data. As a result of the analysis, an optimal cooking profile is generated based on the user's preferences and health information. For example, based on information such as "I like chicken" and "I like spicy food," "spicy grilled chicken" is suggested.

[1885] input:

[1886] Encrypted user data

[1887] output:

[1888] Decrypted user data

[1889] Food profile as analysis result

[1890] Step 4:

[1891] The server stores the generated cooking profile in a database, which contains user-specific information linked to the user ID.

[1892] input:

[1893] Analyzed food profile

[1894] output:

[1895] Cuisine profiles stored in a database

[1896] Step 5:

[1897] The user allows the use of the emotion engine by selecting "Allow emotion recognition" within the application.

[1898] input:

[1899] User Permissions

[1900] output:

[1901] Starting emotion recognition

[1902] Step 6:

[1903] The device activates the camera and microphone to collect the user's facial expressions and voice data, and transmits the collected emotional data to the server in real time.

[1904] input:

[1905] User facial expression data

[1906] User voice data

[1907] output:

[1908] Emotion data sent to the server in real time

[1909] Step 7:

[1910] The server analyzes the received emotion data and reflects it in the cooking profile. For example, if the user seems to be enjoying the food, it may adjust the spiciness a little.

[1911] input:

[1912] Emotional Data

[1913] output:

[1914] Tailored food profiles

[1915] Step 8:

[1916] The user selects the desired dish from the list of dishes suggested by the application and clicks the "Order" button. The device encrypts the selected dish profile and order data and sends it to the server.

[1917] input:

[1918] User's food selection

[1919] Order Data

[1920] output:

[1921] Encrypted order data

[1922] Step 9:

[1923] The server analyzes the received order data and generates the optimal cooking method and portion size. It also references the recipe database to check the availability of the necessary ingredients. For example, it checks the specific steps and ingredients for cooking "spicy grilled chicken."

[1924] input:

[1925] Encrypted Order Data

[1926] Recipe Database

[1927] output:

[1928] Best cooking method and portion size

[1929] Step 10:

[1930] The server sends the generated dish data to the cooking location or partner restaurant, requests cooking, and sends detailed cooking instructions and order details to request the start of cooking.

[1931] input:

[1932] Order details

[1933] Cooking method data

[1934] output:

[1935] Cooking location or cooking request data for partner restaurants

[1936] Step 11:

[1937] The device notifies the user in real time about the cooking process and delivery status, for example by displaying the progress and the delivery person's location in the application.

[1938] input:

[1939] Cooking progress information

[1940] Delivery status information

[1941] output:

[1942] User Notification

[1943] Step 12:

[1944] Users can check the progress of their order through the application until it arrives. After the food arrives, they can provide feedback and ratings through the application. They can enter ratings for taste, quantity, appearance, and service, and add comments.

[1945] input:

[1946] Progress Information

[1947] Evaluation items

[1948] output:

[1949] Ratings and Feedback Data

[1950] Step 13:

[1951] When special ingredients such as insects are included, the server uses AI to generate VR content that corrects visually undesirable aspects. For example, it generates a 3D model and video data to make an insect steak look like a beef steak, and sends them to the device.

[1952] input:

[1953] Special food information

[1954] output:

[1955] Generated VR content

[1956] Step 14:

[1957] The device displays the data sent from the server on the VR headset, providing the user with visually improved ingredients, allowing the user to visually enjoy the cooking process through the VR headset.

[1958] input:

[1959] Generated VR content

[1960] output:

[1961] VR display to the user

[1962] (Application example 2)

[1963] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1964] Conventional personalized meal delivery systems suggest dishes based on a user's ingredient preferences, allergy information, health status, cooking methods, etc., but because they do not take the user's emotions into account, the suggested dishes may not be optimal. Furthermore, dishes containing visually undesirable ingredients may reduce the user's appetite. Therefore, a system that further personalizes the dining experience and stimulates the user's appetite is needed.

[1965] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input information regarding personal food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method; terminal means for receiving the input information in real time and transmitting it to the server; means for analyzing the received data, using artificial intelligence to generate a cooking profile optimal for the user, and storing it in a database; and terminal means for recognizing the user's emotions and reflecting them in the cooking profile. This enables more personalized cooking suggestions that take into account the user's emotions in addition to their preferences and health status. Furthermore, visually undesirable parts can be corrected using a virtual reality device, thereby maintaining or improving the user's appetite.

[1966] A "user" is an individual who uses the system to provide information such as food preferences, allergy information, and health conditions, and receives the most suitable meal.

[1967] "Ingredient preferences" is information about specific ingredients, cooking methods, and seasonings that the user prefers.

[1968] "Allergy information" is information about specific food ingredients to which the user is allergic.

[1969] "Health status" refers to data related to the user's health, such as blood pressure and cholesterol levels.

[1970] A "nutrient target" is a specific nutritional goal that a user wishes to achieve.

[1971] "Seasoning" is information about the flavor of food and the use of spices according to the user's preferences.

[1972] "Appearance" refers to information about the visual elements that users desire in a dish, such as presentation and color.

[1973] "Texture" refers to information about a user's preferences regarding the mouthfeel and texture of a dish.

[1974] "Cooking method" is information about the cooking method preferred by the user, such as baking, boiling, frying, etc.

[1975] A "terminal" is hardware that allows a user to input information and send it to a server.

[1976] A "server" is a computer system that analyzes data received from users and generates an optimal cooking profile.

[1977] "Artificial intelligence" is the technology used to analyze the received data and generate the optimal cooking profile for the user.

[1978] An "emotion-recognizing device" is a combination of hardware and software that analyzes a user's facial expressions and voice and collects emotional data.

[1979] A "cuisine profile" is a data set that details the cuisine that is best suited to a user.

[1980] A "virtual reality device" is a device that incorporates virtual reality technology used to correct visual imperfections.

[1981] A "visually objectionable portion" refers to an ingredient or part of a dish that a user finds visually objectionable.

[1982] To implement this invention, three main components are used: a user, a terminal, and a server. The following describes these components and their respective operations in detail.

[1983] Enter user information

[1984] Users use devices such as smartphones or head-mounted displays to input information about their food preferences, allergies, health conditions, target nutrients, seasonings, appearance, texture, and cooking methods. The device, equipped with an emotion engine, obtains emotional data in real time from the user's facial expressions and voice.

[1985] Receiving and analyzing data

[1986] The device sends the input information and emotional data in an encrypted format to the server. The server analyzes the received user data and uses a generative AI model to generate an optimal cooking profile for each user, which is then stored in a database. The generative AI model selects dishes based on the user's ingredient preferences and health status.

[1987] Emotion-based food adjustment

[1988] When data is acquired by the emotion engine, the server analyzes it and reflects it in the cooking profile. For example, if the user is "having fun," the server can adjust the spiciness and seasoning of the food based on that emotion.

[1989] Visual improvements

[1990] If the server detects ingredients that are visually objectionable to the user, it generates data to correct the problem using a virtual reality device. This data is sent to the user's device, and the user can view the corrected visual data through a VR headset. Specifically, it generates a 3D model and video data to make a dish containing insects look like beefsteak.

[1991] Food Serving

[1992] The server then sends the created dish profile to the kitchen or partner restaurant to request cooking. The cooking process and delivery status are displayed to the user in real time via the device. The user can track the waiting time and provide feedback after the food arrives.

[1993] The specific hardware and software used

[1994] Devices: Smartphone, head-mounted display, VR headset (emotion data analysis and user information input)

[1995] Server: Data analysis server, database (for generating cooking profiles and storing data)

[1996] Software: Generative AI model (AI data analysis), emotion recognition engine (emotion data analysis)

[1997] Examples and prompts

[1998] As a specific example, the following case will be described where the user likes chicken, likes spicy food, and has recently been feeling "happy."

[1999] Example prompt sentence:

[2000] {

[2001] "user_id": "userA",

[2002] "preferences": {

[2003] "meat": "chicken",

[2004] "spiciness": "high",

[2005] "diet": "low-fat, high-protein"

[2006] },

[2007] "allergies": ["gluten"],

[2008] "health": {"blood_pressure": "normal", "cholesterol": "low"},

[2009] "emotion": "happy"

[2010] }

[2011] In this way, a personalized dining experience can be provided to the user.

[2012] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2013] Step 1:

[2014] The user uses a smartphone or head-mounted display to input information such as food preferences, allergy information, health status, target nutrients, seasoning, appearance, texture, and cooking method. In addition, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data. This information is then input into the device.

[2015] Input: User's personal data and emotional data

[2016] Output: Real-time encrypted user data

[2017] Step 2:

[2018] The device receives the input information in real time and transmits it in encrypted form to the server, where the device aggregates, encrypts, and transmits the data securely.

[2019] Input: User's encrypted data

[2020] Output: Encrypted data sent to the server

[2021] Step 3:

[2022] The server analyzes the received data and uses a generative AI model to generate an optimal cooking profile for each user, which is then stored in a database. Here, the AI ​​determines the type of food, cooking method, nutrient balance, etc. based on the user's preferences and health status.

[2023] Input: Encrypted user data

[2024] Output: Food profiles stored in a database

[2025] Step 4:

[2026] The user's emotional data is sent to the server in real time, where it is analyzed. Based on the analysis results, the emotional data is reflected in the cooking profile. For example, if the user appears to be "having fun," the spiciness of the food may be slightly increased, and other adjustments may be made in real time.

[2027] Input: Emotion data

[2028] Output: Adjusted cooking profile

[2029] Step 5:

[2030] The server determines the recommended dishes, cooking methods, and portion sizes based on the user's profile data, and generates visual correction data for the virtual reality device: a 3D model and video data that makes the insect appear like a beefsteak.

[2031] Input: Food profiles stored in the database

[2032] Output: Vision correction data for virtual reality devices

[2033] Step 6:

[2034] The device receives the visual correction data sent from the server and transmits it to the virtual reality device. When the user puts on the VR headset, the visually improved dish is displayed.

[2035] Input: Vision correction data

[2036] Output: Image displayed on a virtual reality device

[2037] Step 7:

[2038] The server sends the created dish profile to the kitchen or partner restaurant and requests cooking. Here, the server sends detailed cooking instructions and order details and requests the start of cooking.

[2039] Input: Cuisine Profile

[2040] Output: The kitchen or partner restaurant that received the cooking instructions

[2041] Step 8:

[2042] The device displays the cooking process and delivery status to the user in real time, and the user can check the cooking status and delivery person's location through the application.

[2043] Input: Cooking progress, delivery information

[2044] Output: Real-time information displayed on the user's device

[2045] Step 9:

[2046] Users wait for their food to arrive and then provide feedback and ratings on the food and service through the app, which collects data to improve the service.

[2047] Input: Food rating, feedback

[2048] Output: Evaluation data and feedback stored on the server

[2049] Through these steps, a series of processes is realized, from user input to generating the optimal dish, visual improvement, reflection of emotions, and finally serving it.

[2050] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2051] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2052] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2053] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2054] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2055] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2056] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2057] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2058] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2059] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2060] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2061] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2062] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2063] 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.

[2064] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2065] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2066] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a ...

Claims

1. A means for a user to input information about personal food preferences, allergy information, health conditions, target nutrients, seasoning, appearance, texture, and cooking method; a terminal means for receiving input information in real time and transmitting it to a server; A server means for analyzing the received data, generating a cooking profile that is optimal for the user using AI, and storing the profile in a database; A system including:

2. a server means for transmitting the user's order details to a server and generating the optimal cooking method and portion size based on the user's profile data; A terminal means for displaying the data transmitted from the server on a VR device and improving the appearance; The system of claim 1 further comprising:

3. a server means for transmitting the generated dish data to a kitchen or a partner restaurant and requesting cooking; a terminal means for displaying the cooking process and delivery status to the user in real time; A terminal means for a user to track the waiting time of the food and provide a rating or feedback after the food arrives; The system of claim 1 further comprising:

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