system

Abstract

We provide the system. [Solution] A means for generating cooking recipes based on the user's health information and goals, A display means for visually displaying the cooking process, A feedback mechanism that receives user feedback and improves the accuracy of recipes, An information processing system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, the need for preparing a healthy diet is increasing, but many people find it difficult to practice a balanced diet due to lack of time and anxiety about cooking skills. Furthermore, it may be difficult to select recipes according to individual health conditions and diet goals, and there is a demand for a system that allows users to easily enjoy healthy cooking.

Means for Solving the Problems

[0005] This invention provides an information processing system that enables users to easily prepare healthy and nutritionally balanced meals through a generation means that generates optimal cooking recipes based on the user's health information and goals, and a display means that visually displays the cooking process. Furthermore, by including a feedback means that receives user feedback and improves the accuracy of the recipes, the system continuously improves the cooking experience to meet individual needs and supports an optimized diet for the user.

[0006] "User" refers to an individual or legal entity that uses an information processing system, and specifically means someone who inputs health information or cooking-related information.

[0007] "Health information" refers to individual information related to a user's diet, such as their physical condition, allergies, and dietary restrictions.

[0008] A "goal" refers to a specific set of achievements that a user has set for purposes such as maintaining health, losing weight, or building muscle.

[0009] "Generation means" refers to a method that includes software or hardware for generating the optimal cooking recipe based on user input information.

[0010] A "cooking recipe" refers to a document or dataset containing a series of steps, instructions, and information about the necessary ingredients for cooking.

[0011] "Display means" refers to display devices and display technologies used to provide information to users visually.

[0012] "Feedback methods" refer to techniques for receiving user feedback and opinions after use, and using that information to improve the system.

[0013] An "information processing system" refers to a comprehensive computing environment designed to provide optimal services and functions based on user input. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

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

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention constructs an information processing system that proposes optimal cooking recipes based on a user's health information and individual dietary goals. In this system, the user first inputs their health information and dietary preferences through an application. This prepares the system to provide recipes tailored to the user's specific needs.

[0036] The server uses AI generation to create cooking recipes tailored to individual needs, based on information received from the user. This generation process considers the user's health goals and allergy information, selecting nutritionally balanced recipes. The generated recipes are then transferred from the server to the user's device and provided to them there.

[0037] The device uses image AI to display a finished dish and cooking instructions to visually support the user's selected recipe. This makes it easier for the user to understand the cooking process concretely and intuitively. The device also manages cooking time and provides step-by-step notifications as needed, helping the user to cook efficiently.

[0038] After trying a provided recipe, users send feedback through their device. The server collects this feedback and uses it to improve future recipe suggestions and the system. For example, if a user requests a "high-protein, low-calorie dinner," the system suggests a salad recipe using chicken breast and plenty of vegetables, and provides the necessary cooking instructions with images and videos.

[0039] In this way, this system provides comprehensive support to enable users to easily prepare healthy meals and enrich their dining experience.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user launches the application and enters their personal health information, dietary preferences, allergy information, and goals (e.g., weight management, muscle building). This information is temporarily stored on the device and later sent to the server.

[0043] Step 2:

[0044] The device sends user input information to the server. The server analyzes the received information and prepares to make optimal meal selections based on the user's profile.

[0045] Step 3:

[0046] The server searches the database for recipe information and uses AI to select candidate recipes that match the user's health information and goals. Nutritional balance, calorie content, and allergy information are considered during this process.

[0047] Step 4:

[0048] The server generates a recipe and sends it to the device. The device receives this recipe information and uses image AI to prepare a visual representation of the finished dish and cooking instructions for the user.

[0049] Step 5:

[0050] The device displays recipe details to the user and visually guides them through the cooking process with videos and images. It also provides notifications to the user regarding cooking time management and important points in the process as needed.

[0051] Step 6:

[0052] The user completes the cooking process and provides feedback on the results to the application. The device collects this information and prepares to send it to the server.

[0053] Step 7:

[0054] The server receives feedback from users, analyzes that data, and uses it to improve the recipe generation algorithm and database. This improves the user experience for future visits.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] To maintain a healthy diet, it's crucial to offer recipes tailored to individual health conditions and dietary preferences, but achieving this effortlessly is difficult. Furthermore, without visual aids, it's challenging to grasp the cooking process and the final product. Additionally, there's a lack of mechanisms to effectively utilize user feedback to improve recipe accuracy.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes a generation means that generates recipes based on the user's health information and goals using content generation technology, a display means that visualizes the generated recipes and displays cooking steps using image generation technology, and a feedback means that receives user feedback and improves the accuracy of the recipes using content generation technology. This enables recipe suggestions and visual support tailored to individual needs, and also realizes continuous recipe optimization utilizing user feedback.

[0060] "Generation means" refers to a technology that uses content generation technology to create individual cooking recipes based on the user's health information and goals.

[0061] "Display means" refers to a technology that visualizes the cooking process of a generated recipe and provides it to the user in a visual format using image generation technology.

[0062] A "feedback method" is a technology that collects opinions and evaluations from users and uses content generation technology to improve the accuracy and quality of recipes based on that feedback.

[0063] "Ingredient selection support means" refers to technology that selects appropriate ingredients based on ingredient information entered by the user.

[0064] A "time management method" is a technology that sets cooking times based on a generated recipe and notifies the user of the start and end times of the cooking process.

[0065] This invention is an information processing system that provides recipes based on the user's health information and dietary goals. The system mainly consists of a server and terminals, and generates recipes using a generative AI model.

[0066] The server receives health information and food preferences entered by the user through their terminal. This information includes the user's nutritional goals and allergy information. A generative AI model is then used to analyze the received information and generate a cooking recipe best suited to the user. In this process, the AI ​​algorithm considers the user's individual needs and generates a nutritionally balanced menu. The AI ​​model used for generation is assumed to be a general generative AI, known as an example of a language model, and uses prompts tailored to the user's needs. An example of a prompt might be, "The user wants a high-protein, low-calorie dinner. Please suggest a recipe using chicken."

[0067] The generated recipe is sent from the server to the terminal. The terminal utilizes image generation AI to provide visual support for the received recipe. This image generation AI provides visual explanations of the cooking process and images of the finished product, helping the user to follow the cooking process more easily. The terminal also has a notification function related to the cooking process, assisting with managing cooking time and the order of steps, enabling the user to cook efficiently and effectively.

[0068] Furthermore, users send feedback to the server via their device after cooking. This feedback is stored on the server and used to improve future recipe suggestions and the system. This enables continuous system improvement and increased user satisfaction.

[0069] Thus, by combining a generative AI model with user feedback, the present invention provides personalized recipes and cooking support, thereby promoting a healthy eating experience for users.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The user inputs their health information, food preferences, and specific dietary goals (e.g., "high protein, low calorie") through an application on their device. The device organizes this information in a database format and prepares to send it to the server in the next step.

[0073] Step 2:

[0074] The terminal transmits information entered by the user to the server via a secure communication protocol. The server temporarily stores the received data and prepares it for analysis. Here, the input is user information, and the output is an analyzable dataset.

[0075] Step 3:

[0076] The server uses a generative AI model to analyze the received user information dataset and generate prompt messages. Based on these prompt messages, it generates a cooking recipe best suited to the user. In this data processing process, the AI ​​considers the user's health status and dietary goals. The generated recipe is the output.

[0077] Step 4:

[0078] The server sends the generated recipe to the terminal. The terminal visualizes the recipe using image generation AI. The terminal generates images and videos to intuitively show the user the cooking steps and the finished product, and outputs them to the user.

[0079] Step 5:

[0080] The user checks the displayed recipe and then actually cooks the meal. The terminal manages the cooking time and provides progress notifications to inform the user of the cooking process. The terminal outputs time management notifications to inform the user of the start and end times of each step.

[0081] Step 6:

[0082] Users provide feedback on the recipes they try to the server via their device. The server collects this feedback and uses it to improve future recipe suggestions and enhance the system. Here, the input is user feedback, and the output is new data points for improvement.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] To generate optimal cooking procedures for health management, assist users in selecting ingredients that meet their specific health goals, and improve the in-store shopping experience, there is a need for efficient guidance methods. In such a system, the challenge is to enable users to easily find and purchase necessary ingredients and intuitively understand the cooking process, thereby improving their eating habits.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes a generation means for generating cooking procedures based on the user's health information and goals, a display means for visually displaying the cooking process, a feedback means for receiving user feedback and improving the accuracy of the cooking procedures, and a guidance means for presenting and guiding users to ingredients within the target facility. This enables the user to efficiently purchase ingredients that match their health goals and to proceed smoothly with cooking.

[0088] "Health information" refers to data related to the user's physical condition and health, including weight, height, allergy information, and nutritional goals.

[0089] "Cooking instructions" refer to information that outlines each step in preparing a specific dish, including instructions such as the necessary ingredients, the order of cooking, time, and temperature.

[0090] A "generation means" is a device or software that has the function of creating new cooking procedures by using algorithms or generative models based on input data.

[0091] "Display means" refers to devices or software that provide information to users visually, such as screens or displays, which serve to show cooking procedures.

[0092] A "feedback mechanism" is a device or software used to collect user reactions and opinions and to improve the system's performance or the cooking procedures it provides based on them.

[0093] A "guidance tool" is a device or software that provides navigation or instructions in the real world to help a user physically find a specific ingredient.

[0094] The system implementing this invention consists of a server, smart glasses as the user's portable terminal, and a cloud service. Processing begins when the user puts on the smart glasses and launches the application.

[0095] The server receives health information and goal data sent by the user and uses a generative AI model to generate cooking instructions that match the user's health goals. User data includes weight, allergy information, and nutritional goals. The generated cooking instructions are sent to smart glasses via the cloud. This system uses generative AI such as OpenAI® GPT-4®.

[0096] Smart glasses visually display received cooking instructions using augmented reality, directly to the user's eyes. This display utilizes a display device and AR software. The display works in conjunction with a guidance function to enable users to efficiently select ingredients in physical stores. Users can find the necessary ingredients and locate them on the shelves by following the displayed prompts.

[0097] Furthermore, the system includes a feature that allows users to provide feedback after completing the cooking steps. The server stores this feedback information and uses it to improve the accuracy of future recipe suggestions. The information obtained from the feedback is also used to train the generative AI model, enabling suggestions that are better suited to the user's preferences.

[0098] As a concrete example, if a user requests a "low-fat, high-fiber weekend lunch," the AI ​​will generate a salad recipe centered around tofu and vegetables, and the store guidance function will inform the user that "tofu is on shelf A21." This prompt helps the user smoothly purchase the necessary ingredients.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server receives health information and goal data sent by the user. Input data includes the user's weight, allergy information, and nutritional goals. Based on this data, the server invokes a generative AI model to generate optimal cooking procedures tailored to the user's health goals. In this process, the AI ​​model analyzes the data and outputs a recipe optimized for each user.

[0102] Step 2:

[0103] The server sends the generated cooking instructions to the user's smart glasses via the cloud. The transmitted data consists of cooking instructions and ingredient lists formatted for visual display. The device receives this information and processes it for display. Specifically, it converts it into data that can be appropriately overlaid onto the user's field of view using AR technology.

[0104] Step 3:

[0105] Users visually confirm augmented reality cooking instructions within a physical store through smart glasses. When a user enters a store and searches for necessary ingredients, the smart glasses guide them to the location of the ingredients in real time. Prompts are used to provide specific guidance to the user, such as "Tofu is on shelf A21." This is a process that displays automatically generated guidance on the screen based on the displayed data.

[0106] Step 4:

[0107] After shopping, users perform the cooking steps at home or elsewhere. Once cooking is complete, they send feedback via their device. This feedback includes evaluations of the dish's quality, preferred seasoning, and time management. This feedback information is collected by a server and used to optimize future recipe suggestions. The server then uses this feedback to update its AI model, resulting in further improvements in accuracy.

[0108] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0109] This invention combines an information processing system that provides optimal cooking recipes according to the user's health information and goals with an emotion engine that recognizes the user's emotions, thereby aiming to suggest recipes that match the user's emotional state and improve the user experience. The system is configured as follows.

[0110] First, the user enters their health information, food preferences, allergies, and individual goals such as weight loss or muscle building. The server receives this information and uses a generative AI to generate recipes best suited to the user.

[0111] In this process, the emotion engine recognizes the user's current emotional state. The emotion engine can determine emotions through facial recognition technology and voice analysis. This emotional information is sent to the server and used to select and adjust recipes.

[0112] The device provides users with individually optimized recipes sent from the server. An emotion engine can, for example, suggest relaxing recipes or cooking videos if the user is feeling stressed. It also enhances the cooking experience by adjusting the content and timing of cooking process notifications according to the user's emotions.

[0113] The user begins cooking and follows the visual guide provided by the device. After cooking is complete, the user provides feedback. This feedback is collected on the server and used to improve future recipe suggestions.

[0114] For example, if a user with an allergy to a specific ingredient is feeling down, the emotional engine can analyze their emotions and suggest a safe recipe that incorporates ingredients to improve their mood.

[0115] In this way, the present invention aims to improve the quality of life through food provision by integrating emotion recognition technology to more individually optimize the user experience.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The user launches the application and enters health information, allergy information, and individual goals. This data is stored on the device and later transferred to the server.

[0119] Step 2:

[0120] The emotion engine is activated and recognizes the user's emotional state. This recognition is based on facial expressions and voice analysis via the camera and microphone. The emotional data obtained here is sent from the device to the server with the user's consent.

[0121] Step 3:

[0122] The server receives health information and emotional data from the user. Based on this, the server uses a generative AI to generate recipes that are tailored to the user's health condition and emotional state. For example, if the user needs to relax, the server will select a recipe using ingredients that have a calming effect.

[0123] Step 4:

[0124] The server generates recipe information and sends it to the terminal. Based on the received information, the terminal visually provides the user with images or videos showing the finished dish and the cooking process.

[0125] Step 5:

[0126] The device customizes notifications and instructions for the cooking process based on the results of the emotion engine's analysis. For example, if the user is feeling anxious, the notifications will be adjusted to provide time for relaxation during the process.

[0127] Step 6:

[0128] The user cooks according to the specified recipe. During the cooking process, they utilize the provided visual guides and notifications to ensure smooth progress.

[0129] Step 7:

[0130] After cooking is complete, the user provides feedback on their cooking experience. The device collects this feedback and sends it to the server.

[0131] Step 8:

[0132] The server uses the received feedback and the results of the emotion engine's analysis to improve future recipe suggestions and service content. This allows the system to provide suggestions that are more tailored to the user.

[0133] (Example 2)

[0134] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0135] Modern people, leading busy lives, often struggle to choose appropriate foods to maintain healthy lifestyle habits. Furthermore, they may lack recipe suggestions tailored to their individual preferences when enjoying cooking, and the process of selecting ingredients can be cumbersome. Additionally, there is a lack of support to efficiently prepare and manage meals. This project aims to address these challenges and achieve a better diet.

[0136] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0137] In this invention, the server includes a generation means for generating cooking methods based on the user's hygiene information and objectives, a display means for visually displaying the cooking procedure, an emotion recognition means for analyzing the user's feelings and adjusting the suggested cooking method based on the analysis results, and a feedback means for receiving opinions provided by the user and improving the accuracy of the cooking method. This makes it possible to provide users with healthy and taste-appropriate cooking methods and efficiently support the cooking process.

[0138] The "generation method" refers to a function that automatically constructs cooking methods based on the user's hygiene status and individual goals.

[0139] A "display means" is a function that allows users to visually confirm the steps and procedures of cooking.

[0140] "Emotion recognition means" refers to a function that analyzes the user's feelings and appropriately adjusts the cooking method provided according to the analysis results.

[0141] A "feedback mechanism" is a function that receives opinions and evaluations from users and uses them to improve the accuracy of cooking methods.

[0142] "Selection method" refers to a function that assists users in choosing the most suitable ingredients based on the ingredient information they input.

[0143] A "timekeeping device" is a function that sets cooking time based on the generated cooking method and notifies the user of the timing.

[0144] To implement this invention, an information processing system having the following configuration is required. First, the user uses a terminal to input their hygiene information, food preferences, allergy factors, and individual goals such as dieting or muscle building. A general-purpose computer or smart device is used for this information input. The terminal then transmits this information to a server.

[0145] The server uses generative AI based on the information it receives to generate the most suitable cooking method for the user. A specific example of this generative AI is an AI model that excels at text generation. The generative AI analyzes prompt sentences tailored to the user's purpose and constructs an appropriate recipe based on the results. An example of a prompt sentence is: "I am currently feeling stressed and would like to relax. My allergy is to XX. Please recommend a recipe based on this."

[0146] Furthermore, the server analyzes the user's emotions using emotion recognition technology. By processing facial images and audio data acquired using cameras and microphones, it understands the user's emotional state and reflects this in the selection and adjustment of recipes. This utilizes facial recognition technology and voice analysis software.

[0147] The device receives personalized recipes sent from the server and presents them to the user visually. The information provided includes cooking instructions, ingredients, cooking videos, and even adjustments based on sentiment analysis results.

[0148] The user begins cooking based on the provided recipe and guide. The device utilizes a timing system and displays notifications and alarms to support the progress of the cooking process. In this way, it provides an environment where the user can cook without stress.

[0149] After cooking is complete, users provide feedback through their devices. This feedback is collected on a server and used to generate more accurate recipes for future use.

[0150] For example, if a user enters "I feel bloated and unwell today. I want to make a dish that will lighten my mood without using nuts, which I'm allergic to," the server will generate a "light yet nutritious salad recipe" and provide it to the user via their device. This entire process makes it possible to provide information tailored to individual needs and feelings in order to improve the user experience.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] Users input their health information, food preferences, allergens, and personal goals into the device. The device then transmits this information as data to the server. This input data serves as the foundation for generating optimal cooking methods for the user. Specifically, it includes input about individual nutritional needs and food restrictions.

[0154] Step 2:

[0155] The server analyzes the received user information. Using a generative AI model, it generates prompt sentences and devises appropriate cooking methods based on them. The server generates prompt sentences using input data, inputs them into the AI ​​model, and generates and outputs cooking method data. Specifically, the server analyzes health data and optimizes nutritional value and food selection.

[0156] Step 3:

[0157] The server analyzes the user's emotions using emotion recognition technology. It uses facial images and voice data sent from the terminal to analyze the emotional data with facial recognition technology and voice analysis software. Based on the analysis results, it selects and adjusts recipes and sends the results to the terminal. Specifically, it personalizes the suggestions by taking into account emotional information obtained from facial expressions and tone of voice.

[0158] Step 4:

[0159] The device receives individually optimized recipes sent from the server and displays them to the user. Based on the entered recipe information, it provides cooking instructions, ingredient lists, and cooking videos, offering a visual guide to the user. Specifically, the device organizes the recipe in a user-friendly format and provides timely alerts for the cooking process as needed.

[0160] Step 5:

[0161] The user begins cooking based on instructions from the device. The device uses a timing system to provide notifications and alarms for each cooking step, supporting the progress of the cooking. This includes specific actions to indicate the start and end times of cooking.

[0162] Step 6:

[0163] After cooking is complete, the user enters feedback through a terminal. The terminal sends this feedback to a server, where data is collected. The server analyzes this feedback and uses it to improve the generated AI model. Specifically, it collects user satisfaction and areas for improvement, and uses this data to reflect in future suggestions.

[0164] (Application Example 2)

[0165] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0166] Conventional recipe generation systems have a challenge in providing an optimal cooking experience tailored to the user's emotions, as they do not take into account the user's emotional state when making suggestions. Furthermore, even when recipe generation based on health information and goals is common, it fails to adequately respond to the user's instantaneous emotional changes. In addition, there is the problem that user feedback is not accurately reflected due to changes in emotions.

[0167] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0168] In this invention, the server includes a generation means for generating cooking methods based on the user's health and goals, an output means for visually displaying the cooking process, an opinion collection means for receiving user feedback and improving the accuracy of the cooking methods, and an emotion recognition means for analyzing the user's emotional state and proposing an optimized cooking method accordingly. This makes it possible to propose cooking recipes that take the user's emotional state into consideration, thereby providing a more satisfying experience.

[0169] A "generation method" is a system that automatically generates the optimal cooking method based on the user's health information and goals.

[0170] "Output means" refers to devices or software that visually display the cooking process to the user in an easy-to-understand manner.

[0171] "Methods for gathering feedback" refer to methods for receiving evaluations and requests from users and using them to improve the quality and precision of cooking methods.

[0172] "Emotion recognition means" refers to technologies that use facial recognition technology and voice analysis to determine the user's emotional state and utilize that information to optimize cooking methods.

[0173] This system combines various technological elements to provide the optimal cooking method based on the user's health information and dietary goals. When a user inputs personal information (health status, food preferences, diet goals, etc.) using a smartphone or other device, the server uses a generative AI model to generate a cooking method based on that information. At this time, an emotion recognition system analyzes the user's current emotions and reflects them in the cooking method.

[0174] Specifically, the user scans their facial expressions using their device's camera or expresses their emotions verbally. This activates a facial recognition library and a voice analysis library, and the emotional data is sent to a server. The server processes this data and generates an optimal cooking method so that the dining experience is tailored to the user's current emotions. The generated cooking method is output to the user's device and provided as a visual step-by-step guide. The user follows the steps to prepare the dish and then provides feedback. The server collects this feedback and uses it to further optimize the cooking method.

[0175] The hardware used includes a smartphone camera and microphone for emotion analysis. Additionally, OpenCV is used for facial recognition, Google Cloud Speech-to-Text for speech analysis, and OpenAI technology is utilized as a generative AI model for recipe generation.

[0176] As a concrete example, consider a case where a user feels "tired from work and wants to relax." The system recognizes this emotion and suggests relaxing dishes such as herbal tea or a nutritionally balanced salad. An example of a prompt to the generative AI model might be: "Based on the user's health condition and their desire to relax, please suggest a recipe using healthy and relaxing ingredients."

[0177] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0178] Step 1:

[0179] The user launches a smartphone application and enters health information, food preferences, goals, etc. This inputs the user's basic data, which is then transmitted from the device to the server.

[0180] Step 2:

[0181] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice. Based on this input, the emotion recognition system uses a facial recognition library (e.g., OpenCV) and a speech analysis library (e.g., Google Cloud Speech-to-Text) to analyze the user's emotional state and sends the data to the server.

[0182] Step 3:

[0183] The server receives data on the user's health, goals, and emotional state, and uses a generative AI model (e.g., OpenAI GPT-3®) to generate an appropriate recipe. During this generation process, the recipe content is adjusted according to the user's emotions. The output is the cooking recipe best suited to the user's emotions.

[0184] Step 4:

[0185] Once a recipe is generated, the server sends that information to the terminal. The terminal visually displays the received recipe, clearly showing the cooking procedure to the user. The user then begins cooking according to these instructions.

[0186] Step 5:

[0187] After cooking is complete, the user provides feedback through the application. The device sends this feedback to the server, which collects it to improve the recipe generation process in the future.

[0188] At each step, the input data is processed appropriately, and the data flow is managed to generate the output results necessary for the next step.

[0189] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0190] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0191] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0192] [Second Embodiment]

[0193] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0194] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0195] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0196] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0197] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0198] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0199] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0200] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0201] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0202] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0203] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0204] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0205] This invention constructs an information processing system that proposes optimal cooking recipes based on a user's health information and individual dietary goals. In this system, the user first inputs their health information and dietary preferences through an application. This prepares the system to provide recipes tailored to the user's specific needs.

[0206] The server uses AI generation to create cooking recipes tailored to individual needs, based on information received from the user. This generation process considers the user's health goals and allergy information, selecting nutritionally balanced recipes. The generated recipes are then transferred from the server to the user's device and provided to them there.

[0207] The device uses image AI to display a finished dish and cooking instructions to visually support the user's selected recipe. This makes it easier for the user to understand the cooking process concretely and intuitively. The device also manages cooking time and provides step-by-step notifications as needed, helping the user to cook efficiently.

[0208] After trying a provided recipe, users send feedback through their device. The server collects this feedback and uses it to improve future recipe suggestions and the system. For example, if a user requests a "high-protein, low-calorie dinner," the system suggests a salad recipe using chicken breast and plenty of vegetables, and provides the necessary cooking instructions with images and videos.

[0209] In this way, this system provides comprehensive support to enable users to easily prepare healthy meals and enrich their dining experience.

[0210] The following describes the processing flow.

[0211] Step 1:

[0212] The user launches the application and enters their personal health information, dietary preferences, allergy information, and goals (e.g., weight management, muscle building). This information is temporarily stored on the device and later sent to the server.

[0213] Step 2:

[0214] The device sends user input information to the server. The server analyzes the received information and prepares to make optimal meal selections based on the user's profile.

[0215] Step 3:

[0216] The server searches the database for recipe information and uses AI to select candidate recipes that match the user's health information and goals. Nutritional balance, calorie content, and allergy information are considered during this process.

[0217] Step 4:

[0218] The server generates a recipe and sends it to the device. The device receives this recipe information and uses image AI to prepare a visual representation of the finished dish and cooking instructions for the user.

[0219] Step 5:

[0220] The device displays recipe details to the user and visually guides them through the cooking process with videos and images. It also provides notifications to the user regarding cooking time management and important points in the process as needed.

[0221] Step 6:

[0222] The user completes the cooking process and provides feedback on the results to the application. The device collects this information and prepares to send it to the server.

[0223] Step 7:

[0224] The server receives feedback from users, analyzes that data, and uses it to improve the recipe generation algorithm and database. This improves the user experience for future visits.

[0225] (Example 1)

[0226] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0227] To maintain a healthy diet, it's crucial to offer recipes tailored to individual health conditions and dietary preferences, but achieving this effortlessly is difficult. Furthermore, without visual aids, it's challenging to grasp the cooking process and the final product. Additionally, there's a lack of mechanisms to effectively utilize user feedback to improve recipe accuracy.

[0228] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0229] In this invention, the server includes a generation means that generates recipes based on the user's health information and goals using content generation technology, a display means that visualizes the generated recipes and displays cooking steps using image generation technology, and a feedback means that receives user feedback and improves the accuracy of the recipes using content generation technology. This enables recipe suggestions and visual support tailored to individual needs, and also realizes continuous recipe optimization utilizing user feedback.

[0230] "Generation means" refers to a technology that uses content generation technology to create individual cooking recipes based on the user's health information and goals.

[0231] "Display means" refers to a technology that visualizes the cooking process of a generated recipe and provides it to the user in a visual format using image generation technology.

[0232] A "feedback method" is a technology that collects opinions and evaluations from users and uses content generation technology to improve the accuracy and quality of recipes based on that feedback.

[0233] "Ingredient selection support means" refers to technology that selects appropriate ingredients based on ingredient information entered by the user.

[0234] A "time management method" is a technology that sets cooking times based on a generated recipe and notifies the user of the start and end times of the cooking process.

[0235] This invention is an information processing system that provides recipes based on the user's health information and dietary goals. The system mainly consists of a server and terminals, and generates recipes using a generative AI model.

[0236] The server receives health information and food preferences entered by the user through their terminal. This information includes the user's nutritional goals and allergy information. A generative AI model is then used to analyze the received information and generate a cooking recipe best suited to the user. In this process, the AI ​​algorithm considers the user's individual needs and generates a nutritionally balanced menu. The AI ​​model used for generation is assumed to be a general generative AI, known as an example of a language model, and uses prompts tailored to the user's needs. An example of a prompt might be, "The user wants a high-protein, low-calorie dinner. Please suggest a recipe using chicken."

[0237] The generated recipe is sent from the server to the terminal. The terminal utilizes image generation AI to provide visual support for the received recipe. This image generation AI provides visual explanations of the cooking process and images of the finished product, helping the user to follow the cooking process more easily. The terminal also has a notification function related to the cooking process, assisting with managing cooking time and the order of steps, enabling the user to cook efficiently and effectively.

[0238] Furthermore, users send feedback to the server via their device after cooking. This feedback is stored on the server and used to improve future recipe suggestions and the system. This enables continuous system improvement and increased user satisfaction.

[0239] Thus, by combining a generative AI model with user feedback, the present invention provides personalized recipes and cooking support, thereby promoting a healthy eating experience for users.

[0240] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0241] Step 1:

[0242] The user inputs their health information, food preferences, and specific dietary goals (e.g., "high protein, low calorie") through an application on their device. The device organizes this information in a database format and prepares to send it to the server in the next step.

[0243] Step 2:

[0244] The terminal transmits information entered by the user to the server via a secure communication protocol. The server temporarily stores the received data and prepares it for analysis. Here, the input is user information, and the output is an analyzable dataset.

[0245] Step 3:

[0246] The server uses a generative AI model to analyze the received user information dataset and generate prompt messages. Based on these prompt messages, it generates a cooking recipe best suited to the user. In this data processing process, the AI ​​considers the user's health status and dietary goals. The generated recipe is the output.

[0247] Step 4:

[0248] The server sends the generated recipe to the terminal. The terminal visualizes the recipe using image generation AI. The terminal generates images and videos to intuitively show the user the cooking steps and the finished product, and outputs them to the user.

[0249] Step 5:

[0250] The user checks the displayed recipe and then actually cooks the meal. The terminal manages the cooking time and provides progress notifications to inform the user of the cooking process. The terminal outputs time management notifications to inform the user of the start and end times of each step.

[0251] Step 6:

[0252] Users provide feedback on the recipes they try to the server via their device. The server collects this feedback and uses it to improve future recipe suggestions and enhance the system. Here, the input is user feedback, and the output is new data points for improvement.

[0253] (Application Example 1)

[0254] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] To generate optimal cooking procedures for health management, assist users in selecting ingredients that meet their specific health goals, and improve the in-store shopping experience, there is a need for efficient guidance methods. In such a system, the challenge is to enable users to easily find and purchase necessary ingredients and intuitively understand the cooking process, thereby improving their eating habits.

[0256] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0257] In this invention, the server includes a generation means for generating cooking procedures based on the user's health information and goals, a display means for visually displaying the cooking process, a feedback means for receiving user feedback and improving the accuracy of the cooking procedures, and a guidance means for presenting and guiding users to ingredients within the target facility. This enables the user to efficiently purchase ingredients that match their health goals and to proceed smoothly with cooking.

[0258] "Health information" refers to data related to the user's physical condition and health, including weight, height, allergy information, and nutritional goals.

[0259] "Cooking instructions" refer to information that outlines each step in preparing a specific dish, including instructions such as the necessary ingredients, the order of cooking, time, and temperature.

[0260] A "generation means" is a device or software that has the function of creating new cooking procedures by using algorithms or generative models based on input data.

[0261] "Display means" refers to devices or software that provide information to users visually, such as screens or displays, which serve to show cooking procedures.

[0262] A "feedback mechanism" is a device or software used to collect user reactions and opinions and improve the system's performance or the cooking procedures it provides based on them.

[0263] A "guidance tool" is a device or software that provides navigation or instructions in the real world to help a user physically find a specific ingredient.

[0264] The system implementing this invention consists of a server, smart glasses as the user's portable terminal, and a cloud service. Processing begins when the user puts on the smart glasses and launches the application.

[0265] The server receives health information and goal data sent from the user and uses a generative AI model to generate cooking instructions that match the user's health goals. User data includes weight, allergy information, and nutritional goals. The generated cooking instructions are sent to smart glasses via the cloud. This system uses generative AI such as OpenAI GPT-4.

[0266] Smart glasses visually display received cooking instructions using augmented reality, directly to the user's eyes. This display utilizes a display device and AR software. The display works in conjunction with a guidance function to enable users to efficiently select ingredients in physical stores. Users can find the necessary ingredients and locate them on the shelves by following the displayed prompts.

[0267] Furthermore, the system includes a feature that allows users to provide feedback after completing the cooking steps. The server stores this feedback information and uses it to improve the accuracy of future recipe suggestions. The information obtained from the feedback is also used to train the generative AI model, enabling suggestions that are better suited to the user's preferences.

[0268] As a concrete example, if a user requests a "low-fat, high-fiber weekend lunch," the AI ​​will generate a salad recipe centered around tofu and vegetables, and the store guidance function will inform the user that "tofu is on shelf A21." This prompt helps the user smoothly purchase the necessary ingredients.

[0269] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0270] Step 1:

[0271] The server receives health information and goal data sent by the user. Input data includes the user's weight, allergy information, and nutritional goals. Based on this data, the server invokes a generative AI model to generate optimal cooking procedures tailored to the user's health goals. In this process, the AI ​​model analyzes the data and outputs a recipe optimized for each user.

[0272] Step 2:

[0273] The server sends the generated cooking instructions to the user's smart glasses via the cloud. The transmitted data consists of cooking instructions and ingredient lists formatted for visual display. The device receives this information and processes it for display. Specifically, it converts it into data that can be appropriately overlaid onto the user's field of view using AR technology.

[0274] Step 3:

[0275] Users visually confirm augmented reality cooking instructions within a physical store through smart glasses. When a user enters a store and searches for necessary ingredients, the smart glasses guide them to the location of the ingredients in real time. Prompts are used to provide specific guidance to the user, such as "Tofu is on shelf A21." This is a process that displays automatically generated guidance on the screen based on the displayed data.

[0276] Step 4:

[0277] After shopping, users perform the cooking steps at home or elsewhere. Once cooking is complete, they send feedback via their device. This feedback includes evaluations of the dish's quality, preferred seasoning, and time management. This feedback information is collected by a server and used to optimize future recipe suggestions. The server then uses this feedback to update its AI model, resulting in further improvements in accuracy.

[0278] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0279] This invention combines an information processing system that provides optimal cooking recipes according to the user's health information and goals with an emotion engine that recognizes the user's emotions, thereby aiming to suggest recipes that match the user's emotional state and improve the user experience. The system is configured as follows.

[0280] First, the user enters their health information, food preferences, allergies, and individual goals such as weight loss or muscle building. The server receives this information and uses a generative AI to generate recipes best suited to the user.

[0281] In this process, the emotion engine recognizes the user's current emotional state. The emotion engine can determine emotions through facial recognition technology and voice analysis. This emotional information is sent to the server and used to select and adjust recipes.

[0282] The device provides users with individually optimized recipes sent from the server. An emotion engine can, for example, suggest relaxing recipes or cooking videos if the user is feeling stressed. It also enhances the cooking experience by adjusting the content and timing of cooking process notifications according to the user's emotions.

[0283] The user begins cooking and follows the visual guide provided by the device. After cooking is complete, the user provides feedback. This feedback is collected on the server and used to improve future recipe suggestions.

[0284] For example, when a user with an allergy to a specific food ingredient is emotionally down, based on the emotion analysis by the emotion engine, a safe recipe utilizing food ingredients for improving the mood can be proposed.

[0285] In this way, the present invention aims to optimize the user experience more individually by integrating emotion recognition technology and enhance the quality of life through food provision.

[0286] The following describes the processing flow.

[0287] Step 1:

[0288] The user launches the application and inputs health information, allergy information, and individual goals. These data are stored by the terminal and later transferred to the server.

[0289] Step 2:

[0290] The emotion engine is launched to recognize the user's emotional state. This recognition is performed based on facial expressions and voice analysis via a camera and a microphone. The emotion data obtained here is sent from the terminal to the server after obtaining the user's consent.

[0291] Step 3:

[0292] The server receives the health information and emotion data from the user. Based on this, the server uses the generation AI to generate a recipe that suits the user's health condition and responds to the user's emotion. For example, when the user needs relaxation, a recipe using ingredients with a calming effect is selected.

[0293] Step 4:

[0294] The server transmits the generated recipe information to the terminal. Based on the received information, the terminal visually provides the user with a completed picture of the dish and cooking steps in the form of images or videos.

[0295] Step 5:

[0296] The device customizes notifications and instructions for the cooking process based on the results of the emotion engine's analysis. For example, if the user is feeling anxious, the notifications will be adjusted to provide time for relaxation during the process.

[0297] Step 6:

[0298] The user cooks according to the specified recipe. During the cooking process, they utilize the provided visual guides and notifications to ensure smooth progress.

[0299] Step 7:

[0300] After cooking is complete, the user provides feedback on their cooking experience. The device collects this feedback and sends it to the server.

[0301] Step 8:

[0302] The server uses the received feedback and the results of the emotion engine's analysis to improve future recipe suggestions and service content. This allows the system to provide suggestions that are more tailored to the user.

[0303] (Example 2)

[0304] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0305] Modern people, leading busy lives, often struggle to choose appropriate foods to maintain healthy lifestyle habits. Furthermore, they may lack recipe suggestions tailored to their individual preferences when enjoying cooking, and the process of selecting ingredients can be cumbersome. Additionally, there is a lack of support to efficiently prepare and manage meals. This project aims to address these challenges and achieve a better diet.

[0306] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 2 is realized by the following means.

[0307] In this invention, the server includes a generation means for generating a cooking method based on information regarding the user's hygiene and purpose, a display means for visually displaying the cooking procedure, an emotion recognition means for analyzing the user's mood and adjusting the proposed cooking method based on the analysis result, and a feedback means for receiving opinions provided by the user and improving the accuracy of the cooking method. Thereby, it becomes possible to provide a healthy and tasteful cooking method optimized for the user and efficiently support the cooking process.

[0308] The "generation means" is a function for automatically constructing a cooking method based on the user's hygiene status and individual goals.

[0309] The "display means" is a function that enables the user to visually confirm the cooking procedure and steps.

[0310] The "emotion recognition means" is a function for analyzing the user's mood and appropriately adjusting the cooking method provided according to the analysis result.

[0311] The "feedback means" is a function for receiving opinions and evaluations from the user and improving the accuracy of the cooking method based on them.

[0312] The "selection means" is a function for assisting in selecting the optimal ingredients based on the ingredient information input by the user.

[0313] The "timing means" is a function for setting the cooking time based on the generated cooking method and notifying the user of the timing.

[0314] To implement this invention, an information processing system having the following configuration is required. First, the user uses a terminal to input their hygiene information, food preferences, allergy factors, and individual goals such as dieting or muscle building. A general-purpose computer or smart device is used for this information input. The terminal then transmits this information to a server.

[0315] The server uses generative AI based on the information it receives to generate the most suitable cooking method for the user. A specific example of this generative AI is an AI model that excels at text generation. The generative AI analyzes prompt sentences tailored to the user's purpose and constructs an appropriate recipe based on the results. An example of a prompt sentence is: "I am currently feeling stressed and would like to relax. My allergy is to XX. Please recommend a recipe based on this."

[0316] Furthermore, the server analyzes the user's emotions using emotion recognition technology. By processing facial images and audio data acquired using cameras and microphones, it understands the user's emotional state and reflects this in the selection and adjustment of recipes. This utilizes facial recognition technology and voice analysis software.

[0317] The device receives personalized recipes sent from the server and presents them to the user visually. The information provided includes cooking instructions, ingredients, cooking videos, and even adjustments based on sentiment analysis results.

[0318] The user begins cooking based on the provided recipe and guide. The device utilizes a timing system and displays notifications and alarms to support the progress of the cooking process. In this way, it provides an environment where the user can cook without stress.

[0319] After cooking is complete, users provide feedback through their devices. This feedback is collected on a server and used to generate more accurate recipes for future use.

[0320] For example, if a user enters "I feel bloated and unwell today. I want to make a dish that will lighten my mood without using nuts, which I'm allergic to," the server will generate a "light yet nutritious salad recipe" and provide it to the user via their device. This entire process makes it possible to provide information tailored to individual needs and feelings in order to improve the user experience.

[0321] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0322] Step 1:

[0323] Users input their health information, food preferences, allergies, and personal goals into the device. The device then transmits this information as data to the server. This input data serves as the foundation for generating optimal cooking methods for the user. Specifically, it includes input about individual nutritional needs and food restrictions.

[0324] Step 2:

[0325] The server analyzes the received user information. Using a generative AI model, it generates prompt sentences and devises appropriate cooking methods based on them. The server generates prompt sentences using input data, inputs them into the AI ​​model, and generates and outputs cooking method data. Specifically, the server analyzes health data and optimizes nutritional value and food selection.

[0326] Step 3:

[0327] The server analyzes the user's emotions using emotion recognition technology. It uses facial images and voice data sent from the terminal to analyze the emotional data with facial recognition technology and voice analysis software. Based on the analysis results, it selects and adjusts recipes and sends the results to the terminal. Specifically, it personalizes the suggestions by taking into account emotional information obtained from facial expressions and tone of voice.

[0328] Step 4:

[0329] The device receives individually optimized recipes sent from the server and displays them to the user. Based on the entered recipe information, it provides cooking instructions, ingredient lists, and cooking videos, offering a visual guide to the user. Specifically, the device organizes the recipe in a user-friendly format and provides timely alerts for the cooking process as needed.

[0330] Step 5:

[0331] The user begins cooking based on instructions from the device. The device uses a timing system to provide notifications and alarms for each cooking step, supporting the progress of the cooking. This includes specific actions to indicate the start and end times of cooking.

[0332] Step 6:

[0333] After cooking is complete, the user enters feedback through a terminal. The terminal sends this feedback to a server, where data is collected. The server analyzes this feedback and uses it to improve the generated AI model. Specifically, it collects user satisfaction and areas for improvement, and uses this data to reflect in future suggestions.

[0334] (Application Example 2)

[0335] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0336] Conventional recipe generation systems have a challenge in providing an optimal cooking experience tailored to the user's emotions, as they do not take into account the user's emotional state when making suggestions. Furthermore, even when recipe generation based on health information and goals is common, it fails to adequately respond to the user's instantaneous emotional changes. In addition, there is the problem that user feedback is not accurately reflected due to changes in emotions.

[0337] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0338] In this invention, the server includes a generation means for generating cooking methods based on the user's health and goals, an output means for visually displaying the cooking process, an opinion collection means for receiving user feedback and improving the accuracy of the cooking methods, and an emotion recognition means for analyzing the user's emotional state and proposing an optimized cooking method accordingly. This makes it possible to propose cooking recipes that take the user's emotional state into consideration, thereby providing a more satisfying experience.

[0339] A "generation method" is a system that automatically generates the optimal cooking method based on the user's health information and goals.

[0340] "Output means" refers to devices or software that visually display the cooking process to the user in an easy-to-understand manner.

[0341] "Methods for gathering feedback" refer to methods for receiving evaluations and requests from users and using them to improve the quality and precision of cooking methods.

[0342] "Emotion recognition means" refers to technologies that use facial recognition technology and voice analysis to determine the user's emotional state and utilize that information to optimize cooking methods.

[0343] This system combines various technological elements to provide the optimal cooking method based on the user's health information and dietary goals. When a user inputs personal information (health status, food preferences, diet goals, etc.) using a smartphone or other device, the server uses a generative AI model to generate a cooking method based on that information. At this time, an emotion recognition system analyzes the user's current emotions and reflects them in the cooking method.

[0344] Specifically, the user scans their facial expressions using their device's camera or expresses their emotions verbally. This activates a facial recognition library and a voice analysis library, and the emotional data is sent to a server. The server processes this data and generates an optimal cooking method so that the dining experience is tailored to the user's current emotions. The generated cooking method is output to the user's device and provided as a visual step-by-step guide. The user follows the steps to prepare the dish and then provides feedback. The server collects this feedback and uses it to further optimize the cooking method.

[0345] The hardware used includes a smartphone camera and microphone for emotion analysis. Additionally, OpenCV is used for facial recognition, Google Cloud Speech-to-Text for speech analysis, and OpenAI technology is utilized as a generative AI model for recipe generation.

[0346] As a concrete example, consider a case where a user feels "tired from work and wants to relax." The system recognizes this emotion and suggests relaxing dishes such as herbal tea or a nutritionally balanced salad. An example of a prompt to the generative AI model might be: "Based on the user's health condition and their desire to relax, please suggest a recipe using healthy and relaxing ingredients."

[0347] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0348] Step 1:

[0349] The user launches a smartphone application and enters health information, food preferences, goals, etc. This inputs the user's basic data, which is then transmitted from the device to the server.

[0350] Step 2:

[0351] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice. Based on this input, the emotion recognition system uses a facial recognition library (e.g., OpenCV) and a speech analysis library (e.g., Google Cloud Speech-to-Text) to analyze the user's emotional state and sends the data to the server.

[0352] Step 3:

[0353] The server receives data on the user's health, goals, and emotional state, and uses a generative AI model (e.g., OpenAI GPT-3) to generate an appropriate recipe. During this generation process, the recipe content is adjusted according to the user's emotions. The output is the cooking recipe best suited to the user's emotions.

[0354] Step 4:

[0355] Once a recipe is generated, the server sends that information to the terminal. The terminal visually displays the received recipe, clearly showing the cooking procedure to the user. The user then begins cooking according to these instructions.

[0356] Step 5:

[0357] After cooking is complete, the user provides feedback through the application. The device sends this feedback to the server, which collects it to improve the recipe generation process in the future.

[0358] At each step, the input data is processed appropriately, and the data flow is managed to generate the output results necessary for the next step.

[0359] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0360] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0361] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0362] [Third Embodiment]

[0363] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0364] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0365] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0366] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0367] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0368] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0369] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0370] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0371] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0372] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0373] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0374] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0375] This invention constructs an information processing system that proposes optimal cooking recipes based on a user's health information and individual dietary goals. In this system, the user first inputs their health information and dietary preferences through an application. This prepares the system to provide recipes tailored to the user's specific needs.

[0376] The server uses AI generation to create cooking recipes tailored to individual needs, based on information received from the user. This generation process considers the user's health goals and allergy information, selecting nutritionally balanced recipes. The generated recipes are then transferred from the server to the user's device and provided to them there.

[0377] The device uses image AI to display a finished dish and cooking instructions to visually support the user's selected recipe. This makes it easier for the user to understand the cooking process concretely and intuitively. The device also manages cooking time and provides step-by-step notifications as needed, helping the user to cook efficiently.

[0378] After trying a provided recipe, users send feedback through their device. The server collects this feedback and uses it to improve future recipe suggestions and the system. For example, if a user requests a "high-protein, low-calorie dinner," the system suggests a salad recipe using chicken breast and plenty of vegetables, and provides the necessary cooking instructions with images and videos.

[0379] In this way, this system provides comprehensive support to enable users to easily prepare healthy meals and enrich their dining experience.

[0380] The following describes the processing flow.

[0381] Step 1:

[0382] The user launches the application and enters their personal health information, dietary preferences, allergy information, and goals (e.g., weight management, muscle building). This information is temporarily stored on the device and later sent to the server.

[0383] Step 2:

[0384] The device sends user input information to the server. The server analyzes the received information and prepares to make optimal meal selections based on the user's profile.

[0385] Step 3:

[0386] The server searches the database for recipe information and uses AI to select candidate recipes that match the user's health information and goals. Nutritional balance, calorie content, and allergy information are considered during this process.

[0387] Step 4:

[0388] The server generates a recipe and sends it to the device. The device receives this recipe information and uses image AI to prepare a visual representation of the finished dish and cooking instructions for the user.

[0389] Step 5:

[0390] The device displays recipe details to the user and visually guides them through the cooking process with videos and images. It also provides notifications to the user regarding cooking time management and important points in the process as needed.

[0391] Step 6:

[0392] The user completes the cooking process and provides feedback on the results to the application. The device collects this information and prepares to send it to the server.

[0393] Step 7:

[0394] The server receives feedback from users, analyzes that data, and uses it to improve the recipe generation algorithm and database. This improves the user experience for future visits.

[0395] (Example 1)

[0396] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0397] To maintain a healthy diet, it's crucial to offer recipes tailored to individual health conditions and dietary preferences, but achieving this effortlessly is difficult. Furthermore, without visual aids, it's challenging to grasp the cooking process and the final product. Additionally, there's a lack of mechanisms to effectively utilize user feedback to improve recipe accuracy.

[0398] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0399] In this invention, the server includes a generation means that generates recipes based on the user's health information and goals using content generation technology, a display means that visualizes the generated recipes and displays cooking steps using image generation technology, and a feedback means that receives user feedback and improves the accuracy of the recipes using content generation technology. This enables recipe suggestions and visual support tailored to individual needs, and also realizes continuous recipe optimization utilizing user feedback.

[0400] "Generation means" refers to a technology that uses content generation technology to create individual cooking recipes based on the user's health information and goals.

[0401] "Display means" refers to a technology that visualizes the cooking process of a generated recipe and provides it to the user in a visual format using image generation technology.

[0402] A "feedback method" is a technology that collects opinions and evaluations from users and uses content generation technology to improve the accuracy and quality of recipes based on that feedback.

[0403] "Ingredient selection support means" refers to technology that selects appropriate ingredients based on ingredient information entered by the user.

[0404] A "time management method" is a technology that sets cooking times based on a generated recipe and notifies the user of the start and end times of the cooking process.

[0405] This invention is an information processing system that provides recipes based on the user's health information and dietary goals. The system mainly consists of a server and terminals, and generates recipes using a generative AI model.

[0406] The server receives health information and food preferences entered by the user through their terminal. This information includes the user's nutritional goals and allergy information. A generative AI model is then used to analyze the received information and generate a cooking recipe best suited to the user. In this process, the AI ​​algorithm considers the user's individual needs and generates a nutritionally balanced menu. The AI ​​model used for generation is assumed to be a general generative AI, known as an example of a language model, and uses prompts tailored to the user's needs. An example of a prompt might be, "The user wants a high-protein, low-calorie dinner. Please suggest a recipe using chicken."

[0407] The generated recipe is sent from the server to the terminal. The terminal utilizes image generation AI to provide visual support for the received recipe. This image generation AI provides visual explanations of the cooking process and images of the finished product, helping the user to follow the cooking process more easily. The terminal also has a notification function related to the cooking process, assisting with managing cooking time and the order of steps, enabling the user to cook efficiently and effectively.

[0408] Furthermore, users send feedback to the server via their device after cooking. This feedback is stored on the server and used to improve future recipe suggestions and the system. This enables continuous system improvement and increased user satisfaction.

[0409] Thus, by combining a generative AI model with user feedback, the present invention provides personalized recipes and cooking support, thereby promoting a healthy eating experience for users.

[0410] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0411] Step 1:

[0412] The user inputs their health information, food preferences, and specific dietary goals (e.g., "high protein, low calorie") through an application on their device. The device organizes this information in a database format and prepares to send it to the server in the next step.

[0413] Step 2:

[0414] The terminal transmits information entered by the user to the server via a secure communication protocol. The server temporarily stores the received data and prepares it for analysis. Here, the input is user information, and the output is an analyzable dataset.

[0415] Step 3:

[0416] The server uses a generative AI model to analyze the received user information dataset and generate prompt messages. Based on these prompt messages, it generates a cooking recipe best suited to the user. In this data processing process, the AI ​​considers the user's health status and dietary goals. The generated recipe is the output.

[0417] Step 4:

[0418] The server sends the generated recipe to the terminal. The terminal visualizes the recipe using image generation AI. The terminal generates images and videos to intuitively show the user the cooking steps and the finished product, and outputs them to the user.

[0419] Step 5:

[0420] The user checks the displayed recipe and then actually cooks the meal. The terminal manages the cooking time and provides progress notifications to inform the user of the cooking process. The terminal outputs time management notifications to inform the user of the start and end times of each step.

[0421] Step 6:

[0422] Users provide feedback on the recipes they try to the server via their device. The server collects this feedback and uses it to improve future recipe suggestions and enhance the system. Here, the input is user feedback, and the output is new data points for improvement.

[0423] (Application Example 1)

[0424] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0425] To generate optimal cooking procedures for health management, assist users in selecting ingredients that meet their specific health goals, and improve the in-store shopping experience, there is a need for efficient guidance methods. In such a system, the challenge is to enable users to easily find and purchase necessary ingredients and intuitively understand the cooking process, thereby improving their eating habits.

[0426] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0427] In this invention, the server includes a generation means for generating cooking procedures based on the user's health information and goals, a display means for visually displaying the cooking process, a feedback means for receiving user feedback and improving the accuracy of the cooking procedures, and a guidance means for presenting and guiding users to ingredients within the target facility. This enables the user to efficiently purchase ingredients that match their health goals and to proceed smoothly with cooking.

[0428] "Health information" refers to data related to the user's physical condition and health, including weight, height, allergy information, and nutritional goals.

[0429] "Cooking instructions" refer to information that outlines each step in preparing a specific dish, including instructions such as the necessary ingredients, the order of cooking, time, and temperature.

[0430] A "generation means" is a device or software that has the function of creating new cooking procedures by using algorithms or generative models based on input data.

[0431] "Display means" refers to devices or software that provide information to users visually, such as screens or displays, which serve to show cooking procedures.

[0432] A "feedback mechanism" is a device or software used to collect user reactions and opinions and improve the system's performance or the cooking procedures it provides based on them.

[0433] A "guidance tool" is a device or software that provides navigation or instructions in the real world to help a user physically find a specific ingredient.

[0434] The system implementing this invention consists of a server, smart glasses as the user's portable terminal, and a cloud service. Processing begins when the user puts on the smart glasses and launches the application.

[0435] The server receives health information and goal data sent from the user and uses a generative AI model to generate cooking instructions that match the user's health goals. User data includes weight, allergy information, and nutritional goals. The generated cooking instructions are sent to smart glasses via the cloud. This system uses generative AI such as OpenAI GPT-4.

[0436] Smart glasses visually display received cooking instructions using augmented reality, directly to the user's eyes. This display utilizes a display device and AR software. The display works in conjunction with a guidance function to enable users to efficiently select ingredients in physical stores. Users can find the necessary ingredients and locate them on the shelves by following the displayed prompts.

[0437] Furthermore, the system includes a feature that allows users to provide feedback after completing the cooking steps. The server stores this feedback information and uses it to improve the accuracy of future recipe suggestions. The information obtained from the feedback is also used to train the generative AI model, enabling suggestions that are better suited to the user's preferences.

[0438] As a concrete example, if a user requests a "low-fat, high-fiber weekend lunch," the AI ​​will generate a salad recipe centered around tofu and vegetables, and the store guidance function will inform the user that "tofu is on shelf A21." This prompt helps the user smoothly purchase the necessary ingredients.

[0439] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0440] Step 1:

[0441] The server receives health information and goal data sent by the user. Input data includes the user's weight, allergy information, and nutritional goals. Based on this data, the server invokes a generative AI model to generate optimal cooking procedures tailored to the user's health goals. In this process, the AI ​​model analyzes the data and outputs a recipe optimized for each user.

[0442] Step 2:

[0443] The server sends the generated cooking instructions to the user's smart glasses via the cloud. The transmitted data consists of cooking instructions and ingredient lists formatted for visual display. The device receives this information and processes it for display. Specifically, it converts it into data that can be appropriately overlaid onto the user's field of view using AR technology.

[0444] Step 3:

[0445] Users visually confirm augmented reality cooking instructions within a physical store through smart glasses. When a user enters a store and searches for necessary ingredients, the smart glasses guide them to the location of the ingredients in real time. Prompts are used to provide specific guidance to the user, such as "Tofu is on shelf A21." This is a process that displays automatically generated guidance on the screen based on the displayed data.

[0446] Step 4:

[0447] After shopping, users perform the cooking steps at home or elsewhere. Once cooking is complete, they send feedback via their device. This feedback includes evaluations of the dish's quality, preferred seasoning, and time management. This feedback information is collected by a server and used to optimize future recipe suggestions. The server then uses this feedback to update its AI model, resulting in further improvements in accuracy.

[0448] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0449] This invention combines an information processing system that provides optimal cooking recipes according to the user's health information and goals with an emotion engine that recognizes the user's emotions, thereby aiming to suggest recipes that match the user's emotional state and improve the user experience. The system is configured as follows.

[0450] First, the user enters their health information, food preferences, allergies, and individual goals such as weight loss or muscle building. The server receives this information and uses a generative AI to generate recipes best suited to the user.

[0451] In this process, the emotion engine recognizes the user's current emotional state. The emotion engine can determine emotions through facial recognition technology and voice analysis. This emotional information is sent to the server and used to select and adjust recipes.

[0452] The device provides users with individually optimized recipes sent from the server. An emotion engine can, for example, suggest relaxing recipes or cooking videos if the user is feeling stressed. It also enhances the cooking experience by adjusting the content and timing of cooking process notifications according to the user's emotions.

[0453] The user begins cooking and follows the visual guide provided by the device. After cooking is complete, the user provides feedback. This feedback is collected on the server and used to improve future recipe suggestions.

[0454] For example, if a user with an allergy to a specific ingredient is feeling down, the emotional engine can analyze their emotions and suggest a safe recipe that incorporates ingredients to improve their mood.

[0455] In this way, the present invention aims to improve the quality of life through food provision by integrating emotion recognition technology to more individually optimize the user experience.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The user launches the application and enters health information, allergy information, and individual goals. This data is stored on the device and later transferred to the server.

[0459] Step 2:

[0460] The emotion engine is activated and recognizes the user's emotional state. This recognition is based on facial expressions and voice analysis via the camera and microphone. The emotional data obtained here is sent from the device to the server with the user's consent.

[0461] Step 3:

[0462] The server receives health information and emotional data from the user. Based on this, the server uses a generative AI to generate recipes that are tailored to the user's health condition and emotional state. For example, if the user needs to relax, the server will select a recipe using ingredients that have a calming effect.

[0463] Step 4:

[0464] The server generates recipe information and sends it to the terminal. Based on the received information, the terminal visually provides the user with images or videos showing the finished dish and the cooking process.

[0465] Step 5:

[0466] The device customizes notifications and instructions for the cooking process based on the results of the emotion engine's analysis. For example, if the user is feeling anxious, the notifications will be adjusted to provide time for relaxation during the process.

[0467] Step 6:

[0468] The user cooks according to the specified recipe. During the cooking process, they utilize the provided visual guides and notifications to ensure smooth progress.

[0469] Step 7:

[0470] After cooking is complete, the user provides feedback on their cooking experience. The device collects this feedback and sends it to the server.

[0471] Step 8:

[0472] The server uses the received feedback and the results of the emotion engine's analysis to improve future recipe suggestions and service content. This allows the system to provide suggestions that are more tailored to the user.

[0473] (Example 2)

[0474] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0475] Modern people, leading busy lives, often struggle to choose appropriate foods to maintain healthy lifestyle habits. Furthermore, they may lack recipe suggestions tailored to their individual preferences when enjoying cooking, and the process of selecting ingredients can be cumbersome. Additionally, there is a lack of support to efficiently prepare and manage meals. This project aims to address these challenges and achieve a better diet.

[0476] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0477] In this invention, the server includes a generation means for generating cooking methods based on the user's hygiene information and objectives, a display means for visually displaying the cooking procedure, an emotion recognition means for analyzing the user's feelings and adjusting the suggested cooking method based on the analysis results, and a feedback means for receiving opinions provided by the user and improving the accuracy of the cooking method. This makes it possible to provide users with healthy and taste-appropriate cooking methods and efficiently support the cooking process.

[0478] The "generation method" refers to a function that automatically constructs cooking methods based on the user's hygiene status and individual goals.

[0479] A "display means" is a function that allows users to visually confirm the steps and procedures of cooking.

[0480] "Emotion recognition means" refers to a function that analyzes the user's feelings and appropriately adjusts the cooking method provided according to the analysis results.

[0481] A "feedback mechanism" is a function that receives opinions and evaluations from users and uses them to improve the accuracy of cooking methods.

[0482] "Selection method" refers to a function that assists users in choosing the most suitable ingredients based on the ingredient information they input.

[0483] A "timekeeping device" is a function that sets cooking time based on the generated cooking method and notifies the user of the timing.

[0484] To implement this invention, an information processing system having the following configuration is required. First, the user uses a terminal to input their hygiene information, food preferences, allergy factors, and individual goals such as dieting or muscle building. A general-purpose computer or smart device is used for this information input. The terminal then transmits this information to a server.

[0485] The server uses generative AI based on the information it receives to generate the most suitable cooking method for the user. A specific example of this generative AI is an AI model that excels at text generation. The generative AI analyzes prompt sentences tailored to the user's purpose and constructs an appropriate recipe based on the results. An example of a prompt sentence is: "I am currently feeling stressed and would like to relax. My allergy is to XX. Please recommend a recipe based on this."

[0486] Furthermore, the server analyzes the user's emotions using emotion recognition technology. By processing facial images and audio data acquired using cameras and microphones, it understands the user's emotional state and reflects this in the selection and adjustment of recipes. This utilizes facial recognition technology and voice analysis software.

[0487] The device receives personalized recipes sent from the server and presents them to the user visually. The information provided includes cooking instructions, ingredients, cooking videos, and even adjustments based on sentiment analysis results.

[0488] The user begins cooking based on the provided recipe and guide. The device utilizes a timing system and displays notifications and alarms to support the progress of the cooking process. In this way, it provides an environment where the user can cook without stress.

[0489] After cooking is complete, users provide feedback through their devices. This feedback is collected on a server and used to generate more accurate recipes for future use.

[0490] For example, if a user enters "I feel bloated and unwell today. I want to make a dish that will lighten my mood without using nuts, which I'm allergic to," the server will generate a "light yet nutritious salad recipe" and provide it to the user via their device. This entire process makes it possible to provide information tailored to individual needs and feelings in order to improve the user experience.

[0491] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0492] Step 1:

[0493] Users input their health information, food preferences, allergies, and personal goals into the device. The device then transmits this information as data to the server. This input data serves as the foundation for generating optimal cooking methods for the user. Specifically, it includes input about individual nutritional needs and food restrictions.

[0494] Step 2:

[0495] The server analyzes the received user information. Using a generative AI model, it generates prompt sentences and devises appropriate cooking methods based on them. The server generates prompt sentences using input data, inputs them into the AI ​​model, and generates and outputs cooking method data. Specifically, the server analyzes health data and optimizes nutritional value and food selection.

[0496] Step 3:

[0497] The server analyzes the user's emotions using emotion recognition technology. It uses facial images and voice data sent from the terminal to analyze the emotional data with facial recognition technology and voice analysis software. Based on the analysis results, it selects and adjusts recipes and sends the results to the terminal. Specifically, it personalizes the suggestions by taking into account emotional information obtained from facial expressions and tone of voice.

[0498] Step 4:

[0499] The device receives individually optimized recipes sent from the server and displays them to the user. Based on the entered recipe information, it provides cooking instructions, ingredient lists, and cooking videos, offering a visual guide to the user. Specifically, the device organizes the recipe in a user-friendly format and provides timely alerts for the cooking process as needed.

[0500] Step 5:

[0501] The user begins cooking based on instructions from the device. The device uses a timing system to provide notifications and alarms for each cooking step, supporting the progress of the cooking. This includes specific actions to indicate the start and end times of cooking.

[0502] Step 6:

[0503] After cooking is complete, the user enters feedback through a terminal. The terminal sends this feedback to a server, where data is collected. The server analyzes this feedback and uses it to improve the generated AI model. Specifically, it collects user satisfaction and areas for improvement, and uses this data to reflect in future suggestions.

[0504] (Application Example 2)

[0505] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0506] Conventional recipe generation systems have a challenge in providing an optimal cooking experience tailored to the user's emotions, as they do not take into account the user's emotional state when making suggestions. Furthermore, even when recipe generation based on health information and goals is common, it fails to adequately respond to the user's instantaneous emotional changes. In addition, there is the problem that user feedback is not accurately reflected due to changes in emotions.

[0507] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0508] In this invention, the server includes a generation means for generating cooking methods based on the user's health and goals, an output means for visually displaying the cooking process, an opinion collection means for receiving user feedback and improving the accuracy of the cooking methods, and an emotion recognition means for analyzing the user's emotional state and proposing an optimized cooking method accordingly. This makes it possible to propose cooking recipes that take the user's emotional state into consideration, thereby providing a more satisfying experience.

[0509] A "generation method" is a system that automatically generates the optimal cooking method based on the user's health information and goals.

[0510] "Output means" refers to devices or software that visually display the cooking process to the user in an easy-to-understand manner.

[0511] "Methods for gathering feedback" refer to methods for receiving evaluations and requests from users and using them to improve the quality and precision of cooking methods.

[0512] "Emotion recognition means" refers to technologies that use facial recognition technology and voice analysis to determine the user's emotional state and utilize that information to optimize cooking methods.

[0513] This system combines various technological elements to provide the optimal cooking method based on the user's health information and dietary goals. When a user inputs personal information (health status, food preferences, diet goals, etc.) using a smartphone or other device, the server uses a generative AI model to generate a cooking method based on that information. At this time, an emotion recognition system analyzes the user's current emotions and reflects them in the cooking method.

[0514] Specifically, the user scans their facial expressions using their device's camera or expresses their emotions verbally. This activates a facial recognition library and a voice analysis library, and the emotional data is sent to a server. The server processes this data and generates an optimal cooking method so that the dining experience is tailored to the user's current emotions. The generated cooking method is output to the user's device and provided as a visual step-by-step guide. The user follows the steps to prepare the dish and then provides feedback. The server collects this feedback and uses it to further optimize the cooking method.

[0515] The hardware used includes a smartphone camera and microphone for emotion analysis. Additionally, OpenCV is used for facial recognition, Google Cloud Speech-to-Text for speech analysis, and OpenAI technology is utilized as a generative AI model for recipe generation.

[0516] As a concrete example, consider a case where a user feels "tired from work and wants to relax." The system recognizes this emotion and suggests relaxing dishes such as herbal tea or a nutritionally balanced salad. An example of a prompt to the generative AI model might be: "Based on the user's health condition and their desire to relax, please suggest a recipe using healthy and relaxing ingredients."

[0517] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0518] Step 1:

[0519] The user launches a smartphone application and enters health information, food preferences, goals, etc. This inputs the user's basic data, which is then transmitted from the device to the server.

[0520] Step 2:

[0521] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice. Based on this input, the emotion recognition system uses a facial recognition library (e.g., OpenCV) and a speech analysis library (e.g., Google Cloud Speech-to-Text) to analyze the user's emotional state and sends the data to the server.

[0522] Step 3:

[0523] The server receives data on the user's health, goals, and emotional state, and uses a generative AI model (e.g., OpenAI GPT-3) to generate an appropriate recipe. During this generation process, the recipe content is adjusted according to the user's emotions. The output is the cooking recipe best suited to the user's emotions.

[0524] Step 4:

[0525] Once a recipe is generated, the server sends that information to the terminal. The terminal visually displays the received recipe, clearly showing the cooking procedure to the user. The user then begins cooking according to these instructions.

[0526] Step 5:

[0527] After cooking is complete, the user provides feedback through the application. The device sends this feedback to the server, which collects it to improve the recipe generation process in the future.

[0528] At each step, the input data is processed appropriately, and the data flow is managed to generate the output results necessary for the next step.

[0529] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0530] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0531] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0532] [Fourth Embodiment]

[0533] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0534] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0535] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0536] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0537] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0538] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0539] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0540] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0541] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0542] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0543] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0544] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0545] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0546] This invention constructs an information processing system that proposes optimal cooking recipes based on a user's health information and individual dietary goals. In this system, the user first inputs their health information and dietary preferences through an application. This prepares the system to provide recipes tailored to the user's specific needs.

[0547] The server uses AI generation to create cooking recipes tailored to individual needs, based on information received from the user. This generation process considers the user's health goals and allergy information, selecting nutritionally balanced recipes. The generated recipes are then transferred from the server to the user's device and provided to them there.

[0548] The device uses image AI to display a finished dish and cooking instructions to visually support the user's selected recipe. This makes it easier for the user to understand the cooking process concretely and intuitively. The device also manages cooking time and provides step-by-step notifications as needed, helping the user to cook efficiently.

[0549] After trying a provided recipe, users send feedback through their device. The server collects this feedback and uses it to improve future recipe suggestions and the system. For example, if a user requests a "high-protein, low-calorie dinner," the system suggests a salad recipe using chicken breast and plenty of vegetables, and provides the necessary cooking instructions with images and videos.

[0550] In this way, this system provides comprehensive support to enable users to easily prepare healthy meals and enrich their dining experience.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] The user launches the application and enters their personal health information, dietary preferences, allergy information, and goals (e.g., weight management, muscle building). This information is temporarily stored on the device and later sent to the server.

[0554] Step 2:

[0555] The device sends user input information to the server. The server analyzes the received information and prepares to make optimal meal selections based on the user's profile.

[0556] Step 3:

[0557] The server searches the database for recipe information and uses AI to select candidate recipes that match the user's health information and goals. Nutritional balance, calorie content, and allergy information are considered during this process.

[0558] Step 4:

[0559] The server generates a recipe and sends it to the device. The device receives this recipe information and uses image AI to prepare a visual representation of the finished dish and cooking instructions for the user.

[0560] Step 5:

[0561] The device displays recipe details to the user and visually guides them through the cooking process with videos and images. It also provides notifications to the user regarding cooking time management and important points in the process as needed.

[0562] Step 6:

[0563] The user completes the cooking process and provides feedback on the results to the application. The device collects this information and prepares to send it to the server.

[0564] Step 7:

[0565] The server receives feedback from users, analyzes that data, and uses it to improve the recipe generation algorithm and database. This improves the user experience for future visits.

[0566] (Example 1)

[0567] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0568] To maintain a healthy diet, it's crucial to offer recipes tailored to individual health conditions and dietary preferences, but achieving this effortlessly is difficult. Furthermore, without visual aids, it's challenging to grasp the cooking process and the final product. Additionally, there's a lack of mechanisms to effectively utilize user feedback to improve recipe accuracy.

[0569] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0570] In this invention, the server includes a generation means that generates recipes based on the user's health information and goals using content generation technology, a display means that visualizes the generated recipes and displays cooking steps using image generation technology, and a feedback means that receives user feedback and improves the accuracy of the recipes using content generation technology. This enables recipe suggestions and visual support tailored to individual needs, and also realizes continuous recipe optimization utilizing user feedback.

[0571] "Generation means" refers to a technology that uses content generation technology to create individual cooking recipes based on the user's health information and goals.

[0572] "Display means" refers to a technology that visualizes the cooking process of a generated recipe and provides it to the user in a visual format using image generation technology.

[0573] A "feedback method" is a technology that collects opinions and evaluations from users and uses content generation technology to improve the accuracy and quality of recipes based on that feedback.

[0574] "Ingredient selection support means" refers to technology that selects appropriate ingredients based on ingredient information entered by the user.

[0575] A "time management method" is a technology that sets cooking times based on a generated recipe and notifies the user of the start and end times of the cooking process.

[0576] This invention is an information processing system that provides recipes based on the user's health information and dietary goals. The system mainly consists of a server and terminals, and generates recipes using a generative AI model.

[0577] The server receives health information and food preferences entered by the user through their terminal. This information includes the user's nutritional goals and allergy information. A generative AI model is then used to analyze the received information and generate a cooking recipe best suited to the user. In this process, the AI ​​algorithm considers the user's individual needs and generates a nutritionally balanced menu. The AI ​​model used for generation is assumed to be a general generative AI, known as an example of a language model, and uses prompts tailored to the user's needs. An example of a prompt might be, "The user wants a high-protein, low-calorie dinner. Please suggest a recipe using chicken."

[0578] The generated recipe is sent from the server to the terminal. The terminal utilizes image generation AI to provide visual support for the received recipe. This image generation AI provides visual explanations of the cooking process and images of the finished product, helping the user to follow the cooking process more easily. The terminal also has a notification function related to the cooking process, assisting with managing cooking time and the order of steps, enabling the user to cook efficiently and effectively.

[0579] Furthermore, users send feedback to the server via their device after cooking. This feedback is stored on the server and used to improve future recipe suggestions and the system. This enables continuous system improvement and increased user satisfaction.

[0580] Thus, by combining a generative AI model with user feedback, the present invention provides personalized recipes and cooking support, thereby promoting a healthy eating experience for users.

[0581] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0582] Step 1:

[0583] The user inputs their health information, food preferences, and specific dietary goals (e.g., "high protein, low calorie") through an application on their device. The device organizes this information in a database format and prepares to send it to the server in the next step.

[0584] Step 2:

[0585] The terminal transmits information entered by the user to the server via a secure communication protocol. The server temporarily stores the received data and prepares it for analysis. Here, the input is user information, and the output is an analyzable dataset.

[0586] Step 3:

[0587] The server uses a generative AI model to analyze the received user information dataset and generate prompt messages. Based on these prompt messages, it generates a cooking recipe best suited to the user. In this data processing process, the AI ​​considers the user's health status and dietary goals. The generated recipe is the output.

[0588] Step 4:

[0589] The server sends the generated recipe to the terminal. The terminal visualizes the recipe using image generation AI. The terminal generates images and videos to intuitively show the user the cooking steps and the finished product, and outputs them to the user.

[0590] Step 5:

[0591] The user checks the displayed recipe and then actually cooks the meal. The terminal manages the cooking time and provides progress notifications to inform the user of the cooking process. The terminal outputs time management notifications to inform the user of the start and end times of each step.

[0592] Step 6:

[0593] Users provide feedback on the recipes they try to the server via their device. The server collects this feedback and uses it to improve future recipe suggestions and enhance the system. Here, the input is user feedback, and the output is new data points for improvement.

[0594] (Application Example 1)

[0595] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0596] To generate optimal cooking procedures for health management, assist users in selecting ingredients that meet their specific health goals, and improve the in-store shopping experience, there is a need for efficient guidance methods. In such a system, the challenge is to enable users to easily find and purchase necessary ingredients and intuitively understand the cooking process, thereby improving their eating habits.

[0597] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0598] In this invention, the server includes a generation means for generating cooking procedures based on the user's health information and goals, a display means for visually displaying the cooking process, a feedback means for receiving user feedback and improving the accuracy of the cooking procedures, and a guidance means for presenting and guiding users to ingredients within the target facility. This enables the user to efficiently purchase ingredients that match their health goals and to proceed smoothly with cooking.

[0599] "Health information" refers to data related to the user's physical condition and health, including weight, height, allergy information, and nutritional goals.

[0600] "Cooking instructions" refer to information that outlines each step in preparing a specific dish, including instructions such as the necessary ingredients, the order of cooking, time, and temperature.

[0601] A "generation means" is a device or software that has the function of creating new cooking procedures by using algorithms or generative models based on input data.

[0602] "Display means" refers to devices or software that provide information to users visually, such as screens or displays, which serve to show cooking procedures.

[0603] A "feedback mechanism" is a device or software used to collect user reactions and opinions and improve the system's performance or the cooking procedures it provides based on them.

[0604] A "guidance tool" is a device or software that provides navigation or instructions in the real world to help a user physically find a specific ingredient.

[0605] The system implementing this invention consists of a server, smart glasses as the user's portable terminal, and a cloud service. Processing begins when the user puts on the smart glasses and launches the application.

[0606] The server receives health information and goal data sent from the user and uses a generative AI model to generate cooking instructions that match the user's health goals. User data includes weight, allergy information, and nutritional goals. The generated cooking instructions are sent to smart glasses via the cloud. This system uses generative AI such as OpenAI GPT-4.

[0607] Smart glasses visually display received cooking instructions using augmented reality, directly to the user's eyes. This display utilizes a display device and AR software. The display works in conjunction with a guidance function to enable users to efficiently select ingredients in physical stores. Users can find the necessary ingredients and locate them on the shelves by following the displayed prompts.

[0608] Furthermore, the system includes a feature that allows users to provide feedback after completing the cooking steps. The server stores this feedback information and uses it to improve the accuracy of future recipe suggestions. The information obtained from the feedback is also used to train the generative AI model, enabling suggestions that are better suited to the user's preferences.

[0609] As a concrete example, if a user requests a "low-fat, high-fiber weekend lunch," the AI ​​will generate a salad recipe centered around tofu and vegetables, and the store guidance function will inform the user that "tofu is on shelf A21." This prompt helps the user smoothly purchase the necessary ingredients.

[0610] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0611] Step 1:

[0612] The server receives health information and goal data sent by the user. Input data includes the user's weight, allergy information, and nutritional goals. Based on this data, the server invokes a generative AI model to generate optimal cooking procedures tailored to the user's health goals. In this process, the AI ​​model analyzes the data and outputs a recipe optimized for each user.

[0613] Step 2:

[0614] The server sends the generated cooking instructions to the user's smart glasses via the cloud. The transmitted data consists of cooking instructions and ingredient lists formatted for visual display. The device receives this information and processes it for display. Specifically, it converts it into data that can be appropriately overlaid onto the user's field of view using AR technology.

[0615] Step 3:

[0616] Users visually confirm augmented reality cooking instructions within a physical store through smart glasses. When a user enters a store and searches for necessary ingredients, the smart glasses guide them to the location of the ingredients in real time. Prompts are used to provide specific guidance to the user, such as "Tofu is on shelf A21." This is a process that displays automatically generated guidance on the screen based on the displayed data.

[0617] Step 4:

[0618] After shopping, users perform the cooking steps at home or elsewhere. Once cooking is complete, they send feedback via their device. This feedback includes evaluations of the dish's quality, preferred seasoning, and time management. This feedback information is collected by a server and used to optimize future recipe suggestions. The server then uses this feedback to update its AI model, resulting in further improvements in accuracy.

[0619] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0620] This invention combines an information processing system that provides optimal cooking recipes according to the user's health information and goals with an emotion engine that recognizes the user's emotions, thereby aiming to suggest recipes that match the user's emotional state and improve the user experience. The system is configured as follows.

[0621] First, the user enters their health information, food preferences, allergies, and individual goals such as weight loss or muscle building. The server receives this information and uses a generative AI to generate recipes best suited to the user.

[0622] In this process, the emotion engine recognizes the user's current emotional state. The emotion engine can determine emotions through facial recognition technology and voice analysis. This emotional information is sent to the server and used to select and adjust recipes.

[0623] The device provides users with individually optimized recipes sent from the server. An emotion engine can, for example, suggest relaxing recipes or cooking videos if the user is feeling stressed. It also enhances the cooking experience by adjusting the content and timing of cooking process notifications according to the user's emotions.

[0624] The user begins cooking and follows the visual guide provided by the device. After cooking is complete, the user provides feedback. This feedback is collected on the server and used to improve future recipe suggestions.

[0625] For example, if a user with an allergy to a specific ingredient is feeling down, the emotional engine can analyze their emotions and suggest a safe recipe that incorporates ingredients to improve their mood.

[0626] In this way, the present invention aims to improve the quality of life through food provision by integrating emotion recognition technology to more individually optimize the user experience.

[0627] The following describes the processing flow.

[0628] Step 1:

[0629] The user launches the application and enters health information, allergy information, and individual goals. This data is stored on the device and later transferred to the server.

[0630] Step 2:

[0631] The emotion engine is activated and recognizes the user's emotional state. This recognition is based on facial expressions and voice analysis via the camera and microphone. The emotional data obtained here is sent from the device to the server with the user's consent.

[0632] Step 3:

[0633] The server receives health information and emotional data from the user. Based on this, the server uses a generative AI to generate recipes that are tailored to the user's health condition and emotional state. For example, if the user needs to relax, the server will select a recipe using ingredients that have a calming effect.

[0634] Step 4:

[0635] The server generates recipe information and sends it to the terminal. Based on the received information, the terminal visually provides the user with images or videos showing the finished dish and the cooking process.

[0636] Step 5:

[0637] The device customizes notifications and instructions for the cooking process based on the results of the emotion engine's analysis. For example, if the user is feeling anxious, the notifications will be adjusted to provide time for relaxation during the process.

[0638] Step 6:

[0639] The user cooks according to the specified recipe. During the cooking process, they utilize the provided visual guides and notifications to ensure smooth progress.

[0640] Step 7:

[0641] After cooking is complete, the user provides feedback on their cooking experience. The device collects this feedback and sends it to the server.

[0642] Step 8:

[0643] The server uses the received feedback and the results of the emotion engine's analysis to improve future recipe suggestions and service content. This allows the system to provide suggestions that are more tailored to the user.

[0644] (Example 2)

[0645] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] Modern people, leading busy lives, often struggle to choose appropriate foods to maintain healthy lifestyle habits. Furthermore, they may lack recipe suggestions tailored to their individual preferences when enjoying cooking, and the process of selecting ingredients can be cumbersome. Additionally, there is a lack of support to efficiently prepare and manage meals. This project aims to address these challenges and achieve a better diet.

[0647] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0648] In this invention, the server includes a generation means for generating cooking methods based on the user's hygiene information and objectives, a display means for visually displaying the cooking procedure, an emotion recognition means for analyzing the user's feelings and adjusting the suggested cooking method based on the analysis results, and a feedback means for receiving opinions provided by the user and improving the accuracy of the cooking method. This makes it possible to provide users with healthy and taste-appropriate cooking methods and efficiently support the cooking process.

[0649] The "generation method" refers to a function that automatically constructs cooking methods based on the user's hygiene status and individual goals.

[0650] A "display means" is a function that allows users to visually confirm the steps and procedures of cooking.

[0651] "Emotion recognition means" refers to a function that analyzes the user's feelings and appropriately adjusts the cooking method provided according to the analysis results.

[0652] A "feedback mechanism" is a function that receives opinions and evaluations from users and uses them to improve the accuracy of cooking methods.

[0653] "Selection method" refers to a function that assists users in choosing the most suitable ingredients based on the ingredient information they input.

[0654] A "timekeeping device" is a function that sets cooking time based on the generated cooking method and notifies the user of the timing.

[0655] To implement this invention, an information processing system having the following configuration is required. First, the user uses a terminal to input their hygiene information, food preferences, allergy factors, and individual goals such as dieting or muscle building. A general-purpose computer or smart device is used for this information input. The terminal then transmits this information to a server.

[0656] The server uses generative AI based on the information it receives to generate the most suitable cooking method for the user. A specific example of this generative AI is an AI model that excels at text generation. The generative AI analyzes prompt sentences tailored to the user's purpose and constructs an appropriate recipe based on the results. An example of a prompt sentence is: "I am currently feeling stressed and would like to relax. My allergy is to XX. Please recommend a recipe based on this."

[0657] Furthermore, the server analyzes the user's emotions using emotion recognition technology. By processing facial images and audio data acquired using cameras and microphones, it understands the user's emotional state and reflects this in the selection and adjustment of recipes. This utilizes facial recognition technology and voice analysis software.

[0658] The device receives personalized recipes sent from the server and presents them to the user visually. The information provided includes cooking instructions, ingredients, cooking videos, and even adjustments based on sentiment analysis results.

[0659] The user begins cooking based on the provided recipe and guide. The device utilizes a timing system and displays notifications and alarms to support the progress of the cooking process. In this way, it provides an environment where the user can cook without stress.

[0660] After cooking is complete, users provide feedback through their devices. This feedback is collected on a server and used to generate more accurate recipes for future use.

[0661] For example, if a user enters "I feel bloated and unwell today. I want to make a dish that will lighten my mood without using nuts, which I'm allergic to," the server will generate a "light yet nutritious salad recipe" and provide it to the user via their device. This entire process makes it possible to provide information tailored to individual needs and feelings in order to improve the user experience.

[0662] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0663] Step 1:

[0664] Users input their health information, food preferences, allergies, and personal goals into the device. The device then transmits this information as data to the server. This input data serves as the foundation for generating optimal cooking methods for the user. Specifically, it includes input about individual nutritional needs and food restrictions.

[0665] Step 2:

[0666] The server analyzes the received user information. Using a generative AI model, it generates prompt sentences and devises appropriate cooking methods based on them. The server generates prompt sentences using input data, inputs them into the AI ​​model, and generates and outputs cooking method data. Specifically, the server analyzes health data and optimizes nutritional value and food selection.

[0667] Step 3:

[0668] The server analyzes the user's emotions using emotion recognition technology. It uses facial images and voice data sent from the terminal to analyze the emotional data with facial recognition technology and voice analysis software. Based on the analysis results, it selects and adjusts recipes and sends the results to the terminal. Specifically, it personalizes the suggestions by taking into account emotional information obtained from facial expressions and tone of voice.

[0669] Step 4:

[0670] The device receives individually optimized recipes sent from the server and displays them to the user. Based on the entered recipe information, it provides cooking instructions, ingredient lists, and cooking videos, offering a visual guide to the user. Specifically, the device organizes the recipe in a user-friendly format and provides timely alerts for the cooking process as needed.

[0671] Step 5:

[0672] The user begins cooking based on instructions from the device. The device uses a timing system to provide notifications and alarms for each cooking step, supporting the progress of the cooking. This includes specific actions to indicate the start and end times of cooking.

[0673] Step 6:

[0674] After cooking is complete, the user enters feedback through a terminal. The terminal sends this feedback to a server, where data is collected. The server analyzes this feedback and uses it to improve the generated AI model. Specifically, it collects user satisfaction and areas for improvement, and uses this data to reflect in future suggestions.

[0675] (Application Example 2)

[0676] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0677] Conventional recipe generation systems have a challenge in providing an optimal cooking experience tailored to the user's emotions, as they do not take into account the user's emotional state when making suggestions. Furthermore, even when recipe generation based on health information and goals is common, it fails to adequately respond to the user's instantaneous emotional changes. In addition, there is the problem that user feedback is not accurately reflected due to changes in emotions.

[0678] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0679] In this invention, the server includes a generation means for generating cooking methods based on the user's health and goals, an output means for visually displaying the cooking process, an opinion collection means for receiving user feedback and improving the accuracy of the cooking methods, and an emotion recognition means for analyzing the user's emotional state and proposing an optimized cooking method accordingly. This makes it possible to propose cooking recipes that take the user's emotional state into consideration, thereby providing a more satisfying experience.

[0680] A "generation method" is a system that automatically generates the optimal cooking method based on the user's health information and goals.

[0681] "Output means" refers to devices or software that visually display the cooking process to the user in an easy-to-understand manner.

[0682] "Methods for gathering feedback" refer to methods for receiving evaluations and requests from users and using them to improve the quality and precision of cooking methods.

[0683] "Emotion recognition means" refers to technologies that use facial recognition technology and voice analysis to determine the user's emotional state and utilize that information to optimize cooking methods.

[0684] This system combines various technological elements to provide the optimal cooking method based on the user's health information and dietary goals. When a user inputs personal information (health status, food preferences, diet goals, etc.) using a smartphone or other device, the server uses a generative AI model to generate a cooking method based on that information. At this time, an emotion recognition system analyzes the user's current emotions and reflects them in the cooking method.

[0685] Specifically, the user scans their facial expressions using their device's camera or expresses their emotions verbally. This activates a facial recognition library and a voice analysis library, and the emotional data is sent to a server. The server processes this data and generates an optimal cooking method so that the dining experience is tailored to the user's current emotions. The generated cooking method is output to the user's device and provided as a visual step-by-step guide. The user follows the steps to prepare the dish and then provides feedback. The server collects this feedback and uses it to further optimize the cooking method.

[0686] The hardware used includes a smartphone camera and microphone for emotion analysis. Additionally, OpenCV is used for facial recognition, Google Cloud Speech-to-Text for speech analysis, and OpenAI technology is utilized as a generative AI model for recipe generation.

[0687] As a concrete example, consider a case where a user feels "tired from work and wants to relax." The system recognizes this emotion and suggests relaxing dishes such as herbal tea or a nutritionally balanced salad. An example of a prompt to the generative AI model might be: "Based on the user's health condition and their desire to relax, please suggest a recipe using healthy and relaxing ingredients."

[0688] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0689] Step 1:

[0690] The user launches a smartphone application and enters health information, food preferences, goals, etc. This inputs the user's basic data, which is then transmitted from the device to the server.

[0691] Step 2:

[0692] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice. Based on this input, the emotion recognition system uses a facial recognition library (e.g., OpenCV) and a speech analysis library (e.g., Google Cloud Speech-to-Text) to analyze the user's emotional state and sends the data to the server.

[0693] Step 3:

[0694] The server receives data on the user's health, goals, and emotional state, and uses a generative AI model (e.g., OpenAI GPT-3) to generate an appropriate recipe. During this generation process, the recipe content is adjusted according to the user's emotions. The output is the cooking recipe best suited to the user's emotions.

[0695] Step 4:

[0696] Once a recipe is generated, the server sends that information to the terminal. The terminal visually displays the received recipe, clearly showing the cooking procedure to the user. The user then begins cooking according to these instructions.

[0697] Step 5:

[0698] After cooking is complete, the user provides feedback through the application. The device sends this feedback to the server, which collects it to improve the recipe generation process in the future.

[0699] At each step, the input data is processed appropriately, and the data flow is managed to generate the output results necessary for the next step.

[0700] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0701] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0702] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0703] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0704] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0705] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0706] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0707] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0708] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0709] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0710] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0711] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0712] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0713] 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.

[0714] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0715] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0716] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0717] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0718] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0719] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0720] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0721] The following is further disclosed regarding the embodiments described above.

[0722] (Claim 1)

[0723] A means for generating cooking recipes based on the user's health information and goals,

[0724] A display means for visually displaying the cooking process,

[0725] A feedback mechanism that receives user feedback and improves the accuracy of recipes,

[0726] An information processing system that includes this.

[0727] (Claim 2)

[0728] The information processing system according to claim 1, further comprising a means for selecting ingredients that assists in selecting the optimal ingredients based on ingredient information entered by the user.

[0729] (Claim 3)

[0730] The information processing system according to claim 1, further comprising a timer means for setting cooking time based on a generated recipe and notifying the user of the start and end times of the cooking process.

[0731] "Example 1"

[0732] (Claim 1)

[0733] A generation means that generates recipes based on the user's health information and goals using content generation technology,

[0734] A display means that visualizes the generated recipe and uses image generation technology to display the cooking process,

[0735] A feedback mechanism that receives user feedback and uses content generation technology to improve the accuracy of recipes,

[0736] A system that includes this.

[0737] (Claim 2)

[0738] The system according to claim 1, further comprising a means for selecting the optimal ingredients based on ingredient information entered by the user.

[0739] (Claim 3)

[0740] The system according to claim 1, further comprising time management means for setting cooking time based on a generated recipe and notifying the user of the start and end times of the process.

[0741] "Application Example 1"

[0742] (Claim 1)

[0743] A generation means for generating cooking procedures based on the user's health information and goals,

[0744] A display means for visually displaying the cooking process,

[0745] A feedback mechanism that receives user feedback and improves the accuracy of cooking procedures,

[0746] A means of providing information and guidance on ingredients within the target facility,

[0747] An information processing system that includes this.

[0748] (Claim 2)

[0749] The information processing system according to claim 1, further comprising a selection means that assists in selecting the optimal ingredients based on ingredient information entered by the user.

[0750] (Claim 3)

[0751] The information processing system according to claim 1, further comprising time management means for setting work time based on the generated cooking procedure and notifying the user of the start and end times of the cooking process.

[0752] "Example 2 of combining an emotion engine"

[0753] (Claim 1)

[0754] A generation means for generating cooking methods based on user hygiene information and purpose,

[0755] A display means for visually showing the cooking procedure,

[0756] An emotion recognition means that analyzes the user's feelings and adjusts the suggested cooking method based on the analysis results,

[0757] A feedback mechanism that receives opinions from users to improve the accuracy of cooking methods,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, further comprising a selection means for assisting the user in selecting the most suitable ingredients based on ingredient information entered by the user.

[0761] (Claim 3)

[0762] The system according to claim 1, further comprising timing means for setting cooking time based on the generated cooking method and notifying the user of the start and end times of the cooking process.

[0763] "Application example 2 when combining with an emotional engine"

[0764] (Claim 1)

[0765] A generation means for generating cooking methods based on the user's health and goals,

[0766] An output means for visually displaying the cooking process,

[0767] A means of collecting user feedback to improve the accuracy of cooking methods,

[0768] An emotion recognition means that analyzes the user's emotional state and proposes an optimized cooking method accordingly,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, further comprising a material selection means that assists in selecting the optimal material based on material information entered by the user.

[0772] (Claim 3)

[0773] The system according to claim 1, further comprising timing means for setting cooking time based on the generated cooking method and notifying the user of the start and end times of the cooking process. [Explanation of symbols]

[0774] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating cooking recipes based on the user's health information and goals, A display means for visually displaying the cooking process, A feedback mechanism that receives user feedback and improves the accuracy of recipes, An information processing system that includes this.

2. The information processing system according to claim 1, further comprising a means for selecting ingredients that assists in selecting the optimal ingredients based on ingredient information entered by the user.

3. The information processing system according to claim 1, further comprising a timer means for setting cooking time based on a generated recipe and notifying the user of the start and end times of the cooking process.

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