system

Abstract

We provide the system. [Solution] A processing method using artificial intelligence that generates cooking procedures based on user preferences and available ingredients, A presentation means for presenting the generated cooking procedure to the user, A display means that provides visual support to the user during cooking, A data processing means that receives user feedback and updates the database, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] For consumers who want to enjoy multinational cuisine and restaurants, it is a problem that it is difficult to obtain recipes for foreign cuisine and learn appropriate cooking procedures. Also, there is a current situation where it is difficult to find recipes suitable for the available ingredients and improve cooking skills. This hinders the introduction of foreign cuisine and consequently restricts the diversity of food cultures.

Means for Solving the Problems

[0005] This invention provides a system that uses artificial intelligence to generate cooking procedures based on the user's preferences and available ingredients. The system presents the generated cooking procedures to the user and provides visual support. It also receives user feedback and updates its database to optimize future recipe suggestions. This makes it possible to create an environment where people can easily enjoy international cuisine.

[0006] A "user" is an individual or group that uses an information processing system to obtain recipes and then cooks.

[0007] "Preferences" refer to the tastes and characteristics of food that users particularly enjoy.

[0008] "Ingredients" refers to the food and seasonings used in cooking according to a recipe.

[0009] "Cooking procedure" refers to the set of steps and methods necessary to complete a dish.

[0010] "Artificial intelligence" refers to programs or systems created to solve specific problems by utilizing data processing capabilities.

[0011] "Presentation method" refers to the methods and tools used to convey generated information to the user.

[0012] "Display means" refers to screens or devices that provide users with visual guidance or support.

[0013] "Feedback" refers to information provided by users, including their experiences, evaluations, and suggestions for improvement.

[0014] "Data processing means" refers to methods and technologies for analyzing collected data and updating or improving information. [Brief explanation of the drawing]

[0015] [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.

MODE FOR CARRYING OUT THE INVENTION

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] 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, and the like.

[0021] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is a system that makes it easy for users to enjoy international cuisine based on their preferences and available ingredients. The system mainly consists of three elements: a server, a terminal, and a user, each playing a specific role.

[0037] The server uses artificial intelligence to generate personalized cooking instructions based on data submitted by the user. Specifically, the server analyzes the user's preferences and ingredient information and identifies the optimal recipe from a database of international cuisines. It then generates the necessary cooking steps based on the identified recipe and outputs them in a format suitable for the user.

[0038] The terminal is a device that provides users with cooking instructions sent from the server. The terminal displays the cooking instructions in a visually easy-to-understand format and provides supplementary information, including videos and images, to support each step of the cooking process. This allows users to smoothly proceed through the actual cooking process.

[0039] Users receive recipes and cooking instructions from the server via their device and cook according to the instructions. After cooking is complete, users input their experience as feedback into their device, which is then sent to the server. The server analyzes this feedback and uses it to update its database and train its artificial intelligence model, thereby improving the accuracy of subsequent recipe suggestions.

[0040] For example, if a user enters "I want to make a spicy chicken dish" into the terminal, the server will generate a recipe for spicy chicken curry based on that information. The terminal will then display an ingredient list and cooking instructions according to the generated recipe, and will also provide videos showing the intermediate steps of the cooking process at appropriate times.

[0041] Thus, the present invention is realized in a form that provides a cooking experience tailored to the user's needs and further enables improvement of the entire system through continuous learning.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user enters their preferences and the ingredients they use into the device. The device receives this information, organizes the data, and then prepares to send it to the server.

[0045] Step 2:

[0046] The device sends data about user preferences and materials to the server. The server receives this data and proceeds with analysis.

[0047] Step 3:

[0048] The server uses an artificial intelligence model to analyze the data it receives. The analysis results are then used to generate appropriate international cuisine recipes.

[0049] Step 4:

[0050] The server organizes the generated recipes and sends them to the user's terminal as instructions in an easy-to-understand format.

[0051] Step 5:

[0052] The terminal presents the user with cooking instructions provided by the server and displays videos or images to assist the cooking process as needed.

[0053] Step 6:

[0054] The user cooks while referring to the device. Once cooking is complete, the user enters feedback about the cooking experience into the device.

[0055] Step 7:

[0056] The device sends user feedback to the server. The server receives the feedback and uses it to update the database and train artificial intelligence models.

[0057] (Example 1)

[0058] 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."

[0059] Providing users with concise and effective cooking instructions tailored to their diverse preferences and available ingredients is challenging. Furthermore, efficiently utilizing user feedback to improve the overall accuracy of the system's suggestions is also difficult. Additionally, improving the user's cooking experience through visual support during the cooking process is another challenge.

[0060] 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.

[0061] In this invention, the server includes information processing means for analyzing user preferences and available ingredients and creating cooking procedures using a generative AI model; presentation means for visually presenting the generated cooking procedures to the user; and display means for providing videos or images as visual support when the user is cooking. This makes it possible to provide cooking procedures tailored to the user, improve the accuracy of suggestions by utilizing feedback, and enhance the user's cooking experience.

[0062] A "user" refers to an individual who uses the system, inputting information about their preferences and ingredients, and receiving cooking instructions.

[0063] "Preferences" refer to choices and tendencies based on the user's likes and desires, and serve as criteria when making individual food selections.

[0064] "Available ingredients" refers to the specific foods and seasonings that users can use in cooking, and is an important factor in selecting a dish.

[0065] A "generative AI model" refers to a set of algorithms and programs that utilize artificial intelligence to generate optimal cooking procedures from a database.

[0066] "Information processing means" refers to a function within a system that analyzes data from users and generates cooking procedures and aggregates and analyzes feedback.

[0067] "Presentation means" refers to methods or interfaces for communicating generated cooking instructions to the user, primarily by presenting information through the terminal screen.

[0068] "Display means" refers to a function that displays videos or images to provide visual support for cooking procedures, and is used to improve the user's cooking experience.

[0069] "Feedback" refers to the evaluations and opinions that users input about the results of their cooking, and it is information that helps improve the system.

[0070] A "database" is part of a system that stores and manages recipe information and related data for multinational cuisine, and is used to generate cooking procedures.

[0071] This invention provides a system that offers users the optimal cooking procedure based on their diverse preferences and the ingredients available at home. This system consists of three elements: a server, a terminal, and a user, each playing a specific role.

[0072] The server receives prompt messages from the user and suggests dishes based on their content. Specifically, the server utilizes a generative AI model to search and generate the optimal recipe from a broad database of international cuisines, based on the user's preferences and ingredient information. This process is expected to utilize programming languages ​​such as Python and Java®, as well as AI libraries such as TENSORFLOW® and PyTorch. The server then sends the generated cooking instructions to the terminal.

[0073] The terminal's role is to present cooking instructions sent from the server to the user. The terminal utilizes interfaces such as a display and speaker to provide the cooking instructions to the user in a visually easy-to-understand format using text, images, and videos. It also has a function to display videos that show each step of the recipe at the right time, allowing the user to proceed smoothly with the cooking process.

[0074] Users perform the actual cooking based on information received through their device. After cooking is complete, users enter feedback about their cooking experience into the device. This feedback is sent to the server and used to improve future suggestions.

[0075] For example, if a user enters a prompt message such as "I want to make a spicy chicken dish" into their device, the server can generate an optimal spicy chicken curry recipe based on this message and send it to the device along with cooking instructions and an ingredient list. In this way, it is possible to provide users with individually customized cooking suggestions.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user inputs prompt messages and information about the ingredients they possess through the terminal. For example, they might input the prompt message, "I want to make a spicy chicken dish." Based on this input, the terminal generates a dataset and sends that data to the server.

[0079] Step 2:

[0080] The server analyzes the user's prompt message and ingredient information received from the terminal. It processes the received data to identify the user's preferences and constraints. Based on the results of this analysis, it extracts relevant recipe candidates from a multinational cuisine database.

[0081] Step 3:

[0082] The server uses the extracted recipe candidates to optimize them using a generation AI model. Specifically, the AI ​​model evaluates and selects the recipe candidate that best matches the user's preferences. Based on the selected recipe, it generates specific cooking instructions. Each step in these instructions includes necessary ingredient information and timing.

[0083] Step 4:

[0084] The server sends the generated cooking instructions and ingredient list back to the terminal. The terminal visually displays the received information to the user. To make cooking easier for the user, the instructions are organized clearly and supplementary media such as videos and images may be added.

[0085] Step 5:

[0086] The user cooks according to the cooking instructions provided on the device. Once cooking is complete, they input the results and experience as feedback into the device, generating feedback data.

[0087] Step 6:

[0088] The device sends user feedback data to the server. The server analyzes this feedback, updates the database information, and uses it as training data for the generated AI model. This process allows the system to continuously improve the accuracy of subsequent recipe suggestions.

[0089] (Application Example 1)

[0090] 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."

[0091] In modern society, there is a need for systems that easily suggest dishes tailored to individual preferences and health conditions, and that support the cooking process. However, existing solutions struggle to efficiently incorporate diverse user preferences and available ingredients into recipe suggestions, and they lack sufficient support for beginners to understand the cooking process. In particular, there is a lack of continuous improvement of systems based on user feedback, making it difficult to adapt to individual needs. Innovative methods are needed to solve these problems.

[0092] 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.

[0093] In this invention, the server includes information processing means for generating cooking procedures based on the user's preferences and available ingredients; display means for presenting the generated cooking procedures to the user; information display means for providing visual support to the user during cooking; information processing means for receiving feedback from the user and updating the information storage location; and means for suggesting the most suitable dish based on the user's preference data. This enables the efficient provision of recipes tailored to individual user needs, visual support for the cooking process, and system improvements based on feedback.

[0094] A "user" is an individual who utilizes the system, providing information about their preferences and available ingredients, and cooking based on the provided recipes.

[0095] "Preferences" refer to information that represents the tastes and cooking styles that users enjoy, and are important input data for the system to generate the optimal cooking procedure for each individual user.

[0096] "Available ingredients" refer to the ingredients the user currently has on hand, and the system uses these as a basis to suggest realistic recipes.

[0097] "Cooking procedure" refers to the specific steps and processes for completing a dish, which are generated by the system based on the user's preferences and ingredients.

[0098] "Artificial intelligence" is a technology that analyzes data provided by users and generates optimal cooking procedures, and is a component of a system that gives the system the ability to make personalized suggestions.

[0099] "Information processing means" refers to the means used on a server to perform data analysis and recipe generation based on user preferences and ingredients.

[0100] "Display means" refers to the means by which a system presents the generated cooking procedure to the user, and includes technologies and devices that provide information visually.

[0101] "Information display means" refers to display functions that provide visual support to users during cooking, and play a role in helping users understand through videos and images.

[0102] "Feedback" refers to information that users report to the system about their actual cooking experiences and impressions, contributing to the continuous improvement of the system.

[0103] An "information storage location" is a place where a system stores user feedback and preference data, providing storage functionality for subsequent data analysis and recommendations.

[0104] "Optimal dish suggestions" refers to suggesting dishes that are likely to satisfy the user the most, based on the user's preferences, available ingredients, and past feedback.

[0105] The system for carrying out the present invention comprises information processing means, display means, information display means, and feedback processing means. The server performs analysis using a generative AI model based on preference and usable material data received from the user. The hardware used here includes a server utilizing cloud computing, and the software uses machine learning frameworks such as TensorFlow and PyTorch for data analysis.

[0106] The server generates the optimal recipe that best matches the user's preferences and sends it to the device. At this time, specific cooking instructions and a list of ingredients are presented to the user through a display. The device has an application developed with React Native installed, allowing the user to intuitively understand and follow the cooking instructions.

[0107] Furthermore, videos and images are provided to offer visual support at each step of the cooking process as means of displaying information. This visual content is retrieved through services such as the Cooking API and delivered to users in a timely manner.

[0108] As a feedback processing mechanism, the terminal is equipped with a function to collect user feedback on their cooking experience and return it to the server. This feedback is stored in an information storage location and contributes to improving the generated AI model through continuous learning.

[0109] For example, if a user inputs "I want to try making a complex dish using my favorite spices," the server will suggest a recipe (e.g., Indian-style spice curry) that matches the user's preferences and ingredients, and the app will guide the user through the cooking process with a video. Another example of a prompt for the generating AI model would be: "Generate the optimal recipe based on the user's ingredients (chicken, tomatoes, spices) and preferences (spicy). Provide step-by-step instructions with image guides."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user enters their preferences and available ingredients using a terminal. The entered data is sent to the server in text format. It is crucial that the user's preferences and ingredient information are accurately captured in this step.

[0113] Step 2:

[0114] The server analyzes the received user data and generates personalized recipes using a generative AI model. Based on the input data (user preferences and ingredients), the AI ​​selects the optimal cooking procedure and outputs it as a recipe. Here, TensorFlow and PyTorch are used to analyze the AI ​​model.

[0115] Step 3:

[0116] The server sends the generated recipe information to the terminal. This output includes specific cooking instructions and ingredient lists. Data transmission to the terminal occurs over the network and is based on a communication protocol (e.g., HTTP).

[0117] Step 4:

[0118] The device visually presents the received recipe to the user. The outputted cooking instructions are displayed through the interface of an app built with React Native. They are displayed step-by-step for easy user understanding.

[0119] Step 5:

[0120] The device utilizes information display means to present users with videos and images based on cooking procedures. This involves data processing to retrieve relevant content using the Cooking API. The output consists of videos and images to provide visual support to the user at the appropriate time.

[0121] Step 6:

[0122] After cooking is complete, the user enters their feedback into the terminal. This user feedback is sent to the server as a new dataset. This feedback data is saved in text format.

[0123] Step 7:

[0124] The server updates the information storage location and improves the accuracy of the generated AI model based on the feedback data. This process analyzes the feedback and incorporates it into the AI ​​model's learning process. The output is an improvement in the model's accuracy in future recipe suggestions.

[0125] 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.

[0126] This invention combines a system that generates personalized cooking instructions tailored to the user's preferences and available ingredients, and provides visual assistance, with an emotion engine that recognizes the user's emotions. The system consists of three elements: a server, a terminal, and a user, each element responsible for a different function.

[0127] The server has the ability to generate personalized recipes for international cuisine using artificial intelligence based on data collected from users. First, the server receives information about the user's preferences and ingredients sent from the terminal and analyzes it. Using the results of this analysis, it selects an appropriate recipe from a database of dishes and further adjusts it to create cooking instructions optimized for the user.

[0128] The terminal plays the role of visually presenting cooking instructions sent from the server to the user. This involves not only displaying recipes but also presenting videos and images according to each step of the cooking process, making it easier for the user to visually understand the procedure. The terminal is also equipped with an emotion engine that can analyze the user's facial expressions and voice to recognize emotions. This emotion information is used to dynamically adjust the presentation of the cooking instructions.

[0129] Users connect to the server via their device and cook according to the provided recipes and cooking procedures. During cooking, their emotions are recognized through the device, and support is provided according to their psychological state at that time. Once cooking is complete, users input their experience and feedback into the device. This feedback and collected emotional data are sent to the server, and the system's overall database and artificial intelligence model are updated, improving the accuracy of future recipe suggestions.

[0130] For example, if a user requests a "light meal for a busy day" and enters the ingredients into the device, the server generates a suitable recipe. If the user shows signs of fatigue during cooking, the device displays advice prompting them to take breaks at each step, helping to smooth the cooking process. In this way, incorporating an emotion engine improves user satisfaction and makes the cooking experience more personal and comfortable.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user enters their preferences and available ingredients into the device. The device receives this information and prepares to send the data to the server.

[0134] Step 2:

[0135] The server receives information about preferences and ingredients sent from the terminal. Based on this, the server uses artificial intelligence to generate a cooking recipe.

[0136] Step 3:

[0137] The server organizes the recipes it generates and sends the optimal cooking procedure to the user's terminal. This procedure is prepared as a detailed instruction manual.

[0138] Step 4:

[0139] The terminal displays the cooking instructions received from the server and provides visual support to make them easy for the user to understand. Videos or images illustrating each step of the cooking process are presented as needed.

[0140] Step 5:

[0141] The device uses its built-in emotion engine to analyze the user's emotions from their facial expressions and voice. The analysis results are used to understand the user's psychological state.

[0142] Step 6:

[0143] As the user proceeds with cooking, the device dynamically adjusts how the cooking instructions are presented based on recognized emotional information. For example, if the user is experiencing high stress levels, it provides relaxing visuals and sounds.

[0144] Step 7:

[0145] Once the user completes the cooking process, the device collects feedback from the user. This feedback may include comments on the cooking experience and suggestions for improvement.

[0146] Step 8:

[0147] The device sends collected feedback and emotional data to the server. The server analyzes this data and updates its database and artificial intelligence models to make future recipe suggestions more accurate.

[0148] (Example 2)

[0149] 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".

[0150] In modern life, there is a demand for personalized cooking instructions for individual users. However, providing cooking instructions that adapt to user preferences, available ingredients, and even emotional states is not easy. Conventional recipe systems struggle to provide dynamic support based on user emotions or to optimize future suggestions based on feedback from users' own cooking experiences. Therefore, a new system is needed to provide a better personalized cooking experience.

[0151] 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.

[0152] In this invention, the server includes intelligent processing means for generating cooking procedures based on the user's preferences and available ingredients, means for analyzing the user's emotions using emotion recognition means, and data processing means for receiving feedback from the user and updating the data set. This makes it possible to provide the user with personalized cooking procedures, provide appropriate support according to the user's emotional state, and optimize future suggestions based on the feedback.

[0153] "Intelligent processing means" refers to a function that utilizes artificial intelligence to generate appropriate cooking procedures based on the user's preferences and available ingredients.

[0154] The "presentation means" is a function that immediately provides the generated cooking procedure to the user, allowing the user to easily obtain cooking information.

[0155] The "emotion recognition means" is a function that analyzes the user's emotional state through their facial expressions and voice, and dynamically adjusts the support provided during cooking.

[0156] "Display means" refers to a function that provides visual instructions to the user, with the aim of clearly presenting cooking procedures in the form of images or videos.

[0157] The "data processing means" is a function that collects user feedback and updates the overall system data set to improve the accuracy of future cooking suggestions.

[0158] "Video" refers to a media format that uses images or a combination of images and audio to visually explain cooking procedures.

[0159] "Feedback" refers to evaluations and opinions provided by users based on their cooking experience, and is information used to improve the system in the future.

[0160] This invention is a system that enables users to obtain personalized cooking instructions based on their preferences and available ingredients. The system consists of three elements: a server, a terminal, and a user, each performing a different function to carry out the invention.

[0161] The server utilizes an artificial intelligence model as an intelligent processing tool. Ideally, this model should be a generative AI model excelling in natural language processing and data analysis. Specifically, the server receives information about the user's preferences and available ingredients, and selects the optimal recipe from a recipe database. During this process, it adjusts the recipe according to the user's requests, generating personalized cooking instructions. For example, for a user who prefers spicy food, the server might add a spicy twist.

[0162] The device functions as both a presentation tool and an emotion recognition tool. As a presentation tool, it visually provides the user with cooking instructions sent from the server, displaying each step clearly using images and videos. As an emotion recognition tool, its built-in camera and microphone analyze the user's facial expressions and voice to determine their emotions. If the user is having trouble or feeling fatigued during cooking, this information is recognized by the device, and appropriate assistance is provided.

[0163] Users connect to the server via their device and cook according to the generated cooking instructions. During cooking, user feedback and emotional data are collected by the device and sent to the server. The server then uses this feedback to update its database and AI model, improving the accuracy of suggestions for future users.

[0164] As a concrete example, if a user requests a "meal perfect for a quick lunch" and enters the ingredients into their device, the server will create a prompt to generate a suitable recipe. An example of a prompt message to the generating AI model might be: "The user wants a light meal for a busy day, and has chicken, lemon, and garlic on hand. Please generate an appropriate recipe based on this information."

[0165] This system allows users to enjoy a personalized cooking experience and receive support tailored to their emotional state at the time.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The user enters their cooking preferences and available ingredients into the terminal. The terminal collects the entered information as data and sends it to the server. This information includes specific data about the user's preferences and the types and quantities of ingredients used.

[0169] Step 2:

[0170] The server receives data from the terminal and analyzes it using a generative AI model, which is an intelligent processing tool. Specifically, it interprets the input information using natural language processing and searches for appropriate recipes in the database. In this process, the model selects recipes based on the entered keywords and customizes them to be optimal for the user. As a result, personalized cooking instructions are obtained and sent to the terminal.

[0171] Step 3:

[0172] The terminal receives personalized cooking instructions sent from the server and functions as a presentation tool. Specifically, it visually presents the cooking instructions to the user step by step using images and videos. This process allows the user to easily check the steps.

[0173] Step 4:

[0174] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice, functioning as a means of emotion recognition. If the user appears confused or tired, the device analyzes this data and decides whether to provide any additional explanations or advice. Based on this information, the device dynamically adjusts its display to support the user.

[0175] Step 5:

[0176] The user prepares the dish according to the provided cooking instructions. After cooking is complete, the user enters feedback into the device. This feedback includes the clarity of the cooking instructions, satisfaction with the recipe, and areas for improvement.

[0177] Step 6:

[0178] The device sends user feedback and emotional data collected during cooking to a server. The server uses this data to update its database and generative AI model through data processing. This update improves the accuracy of future recipe suggestions and provides a better, more personalized cooking experience.

[0179] (Application Example 2)

[0180] 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".

[0181] Conventional cooking support systems lacked personalized support tailored to individual user preferences and emotions, resulting in a lower quality cooking experience. In particular, they lacked mechanisms to alleviate emotional stress and fatigue during the cooking process, highlighting the need to improve user satisfaction.

[0182] 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.

[0183] In this invention, the server includes processing means using artificial intelligence to generate cooking procedures based on the user's preferences and available items, emotion analysis means for recognizing emotions and adjusting support during cooking, and data processing means for collecting user feedback and forming data to update the storage device. This enables highly personalized cooking support that responds to the user's individual needs and emotions.

[0184] A "user" is an individual who uses the system to engage in cooking activities and receives support tailored to their individual preferences and feelings.

[0185] "Preferences" refer to the personal tendencies of a user's preferred tastes, cooking methods, and ingredient choices.

[0186] "Items" refer to all items that the user possesses, including ingredients and cooking utensils that can be used for cooking.

[0187] "Cooking procedure" refers to the specific steps and processes that a user takes to perform a cooking task, and these constitute the overall flow of the cooking process.

[0188] "Artificial intelligence" is a computer technology that has the ability to generate cooking procedures based on the user's preferences and available items, and to adjust them appropriately.

[0189] "Emotion analysis means" refers to technology that analyzes the user's facial expressions and voice to determine the user's emotions, enabling appropriate adjustments to cooking assistance.

[0190] "Feedback" refers to information and data provided by users after using a system, based on suggestions for system improvements or additional information.

[0191] "Data formation methods" refer to processes and technologies used to update databases based on collected information and feedback, thereby improving the accuracy and efficiency of the system.

[0192] A "storage device" is a digital data storage device that holds collected data and generated information, and allows it to be retrieved as needed.

[0193] The system of this invention combines artificial intelligence and emotion analysis technology to improve the user's cooking experience. The server receives information about the user's preferences and available items, and generates cooking instructions based on this information. This process utilizes a generative AI model in the cloud. Specifically, the server analyzes data from the user, selects the optimal procedure from a multinational cuisine recipe database, and then individually adjusts it to generate a personalized recipe.

[0194] The terminal visually presents cooking instructions received from the server to the user. This includes displaying videos and still images for each step of the cooking process, allowing the user to easily follow along with the instructions. The terminal also incorporates emotion analysis capabilities, using a camera and microphone to detect the user's facial expressions and voice, and analyzing their emotions in real time. If the user is experiencing stress, appropriate breaks or encouraging messages will be displayed.

[0195] The specific hardware used includes humanoid robots and tablet devices, and software such as OpenCV and Google® Cloud Speech-to-Text is used for emotion analysis. As an example, when a user enters "I would like a fun cooking experience" into their device, the server provides a recipe corresponding to that request, and the device displays "You're doing great!" when it detects the user's positive emotions.

[0196] Examples of prompts for a generative AI model include: "Detect emotions from the user's facial expressions and voice, and suggest appropriate advice and breaks to ensure the cooking process proceeds smoothly."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The user inputs information about their preferences and available items into the terminal. The terminal then sends this information to the server. The input includes the types of ingredients and dishes selected by the user, and the output is data sent to the server.

[0200] Step 2:

[0201] The server analyzes the information received from the terminal and generates appropriate cooking instructions using a generative AI model. Here, user preference data is used as input, and personalized cooking instructions are generated as output. The specific generative AI model determines a multinational cuisine recipe that suits the user's preferences.

[0202] Step 3:

[0203] The server generates the cooking instructions and sends them to the terminal. The input is the generated cooking instructions, and the output is the transfer of information to the terminal.

[0204] Step 4:

[0205] The terminal visually presents the cooking instructions received from the server to the user. The terminal displays the cooking instructions step by step using videos and still images. This allows the user to understand the cooking instructions more intuitively through visual information.

[0206] Step 5:

[0207] The device analyzes the user's emotions in real time using its camera and microphone. Input includes the user's facial expressions and voice data, which are then used by the emotion analysis system to perform data calculations. The output is emotionally relevant feedback.

[0208] Step 6:

[0209] The device displays advice and messages tailored to the user's emotions based on the analysis results. For example, if the user shows a tired expression, it will display a message encouraging them to take a break. This makes the cooking experience more responsive to the user's psychological state.

[0210] Step 7:

[0211] After the user completes cooking, they enter feedback into the terminal. This includes evaluations of the cooking experience and the quality of the finished product. The feedback is processed by a data generation system and sent to the server, so it can be used to improve future suggestions.

[0212] Step 8:

[0213] The server receives feedback from users and updates the database. The input is feedback data, and the output is an adjusted cooking procedure for the next cooking session. This process improves the overall accuracy of the system and allows it to accommodate diverse user preferences.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention is a system that makes it easy for users to enjoy international cuisine based on their preferences and available ingredients. The system mainly consists of three elements: a server, a terminal, and a user, each playing a specific role.

[0231] The server uses artificial intelligence to generate personalized cooking instructions based on data submitted by the user. Specifically, the server analyzes the user's preferences and ingredient information and identifies the optimal recipe from a database of international cuisines. It then generates the necessary cooking steps based on the identified recipe and outputs them in a format suitable for the user.

[0232] The terminal is a device that provides users with cooking instructions sent from the server. The terminal displays the cooking instructions in a visually easy-to-understand format and provides supplementary information, including videos and images, to support each step of the cooking process. This allows users to smoothly proceed through the actual cooking process.

[0233] Users receive recipes and cooking instructions from the server via their device and cook according to the instructions. After cooking is complete, users input their experience as feedback into their device, which is then sent to the server. The server analyzes this feedback and uses it to update its database and train its artificial intelligence model, thereby improving the accuracy of subsequent recipe suggestions.

[0234] For example, if a user enters "I want to make a spicy chicken dish" into the terminal, the server will generate a recipe for spicy chicken curry based on that information. The terminal will then display an ingredient list and cooking instructions according to the generated recipe, and will also provide videos showing the intermediate steps of the cooking process at appropriate times.

[0235] Thus, the present invention is realized in a form that provides a cooking experience tailored to the user's needs and further enables improvement of the entire system through continuous learning.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The user enters their preferences and the ingredients they use into the device. The device receives this information, organizes the data, and then prepares to send it to the server.

[0239] Step 2:

[0240] The device sends data about user preferences and materials to the server. The server receives this data and proceeds with analysis.

[0241] Step 3:

[0242] The server uses an artificial intelligence model to analyze the data it receives. The analysis results are then used to generate appropriate international cuisine recipes.

[0243] Step 4:

[0244] The server organizes the generated recipes and sends them to the user's terminal as instructions in an easy-to-understand format.

[0245] Step 5:

[0246] The terminal presents the user with cooking instructions provided by the server and displays videos or images to assist the cooking process as needed.

[0247] Step 6:

[0248] The user cooks while referring to the device. Once cooking is complete, the user enters feedback about the cooking experience into the device.

[0249] Step 7:

[0250] The device sends user feedback to the server. The server receives the feedback and uses it to update the database and train artificial intelligence models.

[0251] (Example 1)

[0252] 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."

[0253] Providing users with concise and effective cooking instructions tailored to their diverse preferences and available ingredients is challenging. Furthermore, efficiently utilizing user feedback to improve the overall accuracy of the system's suggestions is also difficult. Additionally, improving the user's cooking experience through visual support during the cooking process is another challenge.

[0254] 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.

[0255] In this invention, the server includes information processing means for analyzing user preferences and available ingredients and creating cooking procedures using a generative AI model; presentation means for visually presenting the generated cooking procedures to the user; and display means for providing videos or images as visual support when the user is cooking. This makes it possible to provide cooking procedures tailored to the user, improve the accuracy of suggestions by utilizing feedback, and enhance the user's cooking experience.

[0256] A "user" refers to an individual who uses the system, inputting information about their preferences and ingredients, and receiving cooking instructions.

[0257] "Preferences" refer to choices and tendencies based on the user's likes and desires, and serve as criteria when making individual food selections.

[0258] "Available ingredients" refers to the specific foods and seasonings that users can use in cooking, and is an important factor in selecting a dish.

[0259] A "generative AI model" refers to a set of algorithms and programs that utilize artificial intelligence to generate optimal cooking procedures from a database.

[0260] "Information processing means" refers to a function within a system that analyzes data from users and generates cooking procedures and aggregates and analyzes feedback.

[0261] "Presentation means" refers to methods or interfaces for communicating generated cooking instructions to the user, primarily by presenting information through the terminal screen.

[0262] "Display means" refers to a function that displays videos or images to provide visual support for cooking procedures, and is used to improve the user's cooking experience.

[0263] "Feedback" refers to the evaluations and opinions that users input about the results of their cooking, and it is information that helps improve the system.

[0264] A "database" is part of a system that stores and manages recipe information and related data for multinational cuisine, and is used to generate cooking procedures.

[0265] This invention provides a system that offers users the optimal cooking procedure based on their diverse preferences and the ingredients available at home. This system consists of three elements: a server, a terminal, and a user, each playing a specific role.

[0266] The server receives prompt messages from the user and suggests dishes based on their content. Specifically, the server utilizes a generative AI model to search and generate the optimal recipe from a broad database of international cuisines, based on the user's preferences and ingredient information. This process is expected to utilize programming languages ​​such as Python and Java, as well as AI libraries such as TensorFlow and PyTorch. The server then sends the generated cooking instructions to the terminal.

[0267] The terminal's role is to present cooking instructions sent from the server to the user. The terminal utilizes interfaces such as a display and speaker to provide the cooking instructions to the user in a visually easy-to-understand format using text, images, and videos. It also has a function to display videos that show each step of the recipe at the right time, allowing the user to proceed smoothly with the cooking process.

[0268] Users perform the actual cooking based on information received through their device. After cooking is complete, users enter feedback about their cooking experience into the device. This feedback is sent to the server and used to improve future suggestions.

[0269] For example, if a user enters a prompt message such as "I want to make a spicy chicken dish" into their device, the server can generate an optimal spicy chicken curry recipe based on this message and send it to the device along with cooking instructions and an ingredient list. In this way, it is possible to provide users with individually customized cooking suggestions.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The user inputs prompt messages and information about the ingredients they possess through the terminal. For example, they might input the prompt message, "I want to make a spicy chicken dish." Based on this input, the terminal generates a dataset and sends that data to the server.

[0273] Step 2:

[0274] The server analyzes the user's prompt message and ingredient information received from the terminal. It processes the received data to identify the user's preferences and constraints. Based on the results of this analysis, it extracts relevant recipe candidates from a multinational cuisine database.

[0275] Step 3:

[0276] The server optimizes the extracted recipe candidates using the generated AI model. Specifically, the AI model evaluates and selects the recipe candidate that best matches the user's preferences among the recipe candidates. Based on the selected recipe, specific cooking procedures are generated. Each step of this procedure includes the necessary ingredient information and timing.

[0277] Step 4:

[0278] The server sends back the generated cooking procedures and ingredient list to the terminal. The terminal visually displays the received information to the user. To make it easier for the user to cook, the procedures are clearly organized and auxiliary media such as videos and images are also added.

[0279] Step 5:

[0280] The user cooks according to the cooking procedures provided from the terminal. After the cooking is completed, the results and experiences are input into the terminal as feedback to generate feedback data.

[0281] Step 6:

[0282] The terminal sends the feedback data from the user to the server. The server analyzes this feedback, updates the information in the database, and utilizes it as learning data for the generated AI model. Through this process, the system can continuously improve the accuracy of subsequent recipe proposals.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In modern society, there is a demand for a system that can easily propose recipes according to individual preferences and health conditions and support the cooking process. However, existing solutions have problems such as difficulty in efficiently incorporating diverse user preferences and available ingredients into recipe proposals, and insufficient support for beginners to understand the cooking process. In particular, there is a lack of continuous improvement of the system that makes use of user feedback, making it difficult to adapt to individual needs. An innovative method to solve such problems is needed.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes information processing means for generating a cooking procedure based on user preferences and available ingredients, display means for presenting the generated cooking procedure to the user, information display means for providing visual support during the user's cooking, information processing means for receiving feedback from the user and updating the information storage place, and means for proposing an optimal recipe based on the user's preference data. As a result, it is possible to efficiently provide recipes that meet individual user needs, visually support the cooking process, and improve the system by making use of feedback.

[0288] A "user" is an individual who uses the system, provides information about their own preferences and available ingredients, and cooks based on the provided recipes.

[0289] "Preference" is information representing the taste and cooking direction that the user likes, and is an important input data for the system to generate an optimal cooking procedure individually.

[0290] "Available ingredients" are the food ingredients that the user currently has on hand, and the system uses this as an element to make realistic recipe proposals.

[0291] "Cooking procedure" refers to the specific steps and processes for completing a dish, which are generated by the system based on the user's preferences and ingredients.

[0292] "Artificial intelligence" is a technology that analyzes data provided by users and generates optimal cooking procedures, and is a component of a system that gives the system the ability to make personalized suggestions.

[0293] "Information processing means" refers to the means used on a server to perform data analysis and recipe generation based on user preferences and ingredients.

[0294] "Display means" refers to the means by which a system presents the generated cooking procedure to the user, and includes technologies and devices that provide information visually.

[0295] "Information display means" refers to display functions that provide visual support to users during cooking, and play a role in helping users understand through videos and images.

[0296] "Feedback" refers to information that users report to the system about their actual cooking experiences and impressions, contributing to the continuous improvement of the system.

[0297] An "information storage location" is a place where a system stores user feedback and preference data, providing storage functionality for subsequent data analysis and recommendations.

[0298] "Optimal dish suggestions" refers to suggesting dishes that are likely to satisfy the user the most, based on the user's preferences, available ingredients, and past feedback.

[0299] The system for carrying out the present invention comprises information processing means, display means, information display means, and feedback processing means. The server performs analysis using a generative AI model based on preference and usable material data received from the user. The hardware used here includes a server utilizing cloud computing, and the software uses machine learning frameworks such as TensorFlow and PyTorch for data analysis.

[0300] The server generates the optimal recipe that best matches the user's preferences and sends it to the device. At this time, specific cooking instructions and a list of ingredients are presented to the user through a display. The device has an application developed with React Native installed, allowing the user to intuitively understand and follow the cooking instructions.

[0301] Furthermore, videos and images are provided to offer visual support at each step of the cooking process as means of displaying information. This visual content is retrieved through services such as the Cooking API and delivered to users in a timely manner.

[0302] As a feedback processing mechanism, the terminal is equipped with a function to collect user feedback on their cooking experience and return it to the server. This feedback is stored in an information storage location and contributes to improving the generated AI model through continuous learning.

[0303] For example, if a user inputs "I want to try making a complex dish using my favorite spices," the server will suggest a recipe (e.g., Indian-style spice curry) that matches the user's preferences and ingredients, and the app will guide the user through the cooking process with a video. Another example of a prompt for the generating AI model would be: "Generate the optimal recipe based on the user's ingredients (chicken, tomatoes, spices) and preferences (spicy). Provide step-by-step instructions with image guides."

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The user uses the terminal to input preferences and available materials. The input data is sent to the server in text-based form. In this step, it is important to accurately obtain the user's preferences and material information.

[0307] Step 2:

[0308] The server analyzes the received user data and generates a personalized recipe using a generated AI model. Based on the input data (user preferences and materials), the AI selects the optimal cooking procedure and outputs this as a recipe. Here, the analysis of the AI model is performed using TensorFlow or PyTorch.

[0309] Step 3:

[0310] The server sends the generated recipe information to the terminal. The output here is a specific cooking procedure and ingredient list. The data transmission to the terminal is performed through the network and is based on a communication protocol (e.g., HTTP).

[0311] Step 4:

[0312] The terminal visually presents the received recipe to the user. The output cooking procedure is displayed via the interface of an app created with React Native. It is displayed step by step so that the user can easily understand.

[0313] Step 5:

[0314] The device utilizes information display means to present users with videos and images based on cooking procedures. This involves data processing to retrieve relevant content using the Cooking API. The output consists of videos and images to provide visual support to the user at the appropriate time.

[0315] Step 6:

[0316] After cooking is complete, the user enters their feedback into the terminal. This user feedback is sent to the server as a new dataset. This feedback data is saved in text format.

[0317] Step 7:

[0318] The server updates the information storage location and improves the accuracy of the generated AI model based on the feedback data. This process analyzes the feedback and incorporates it into the AI ​​model's learning process. The output is an improvement in the model's accuracy in future recipe suggestions.

[0319] 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.

[0320] This invention combines a system that generates personalized cooking instructions tailored to the user's preferences and available ingredients, and provides visual assistance, with an emotion engine that recognizes the user's emotions. The system consists of three elements: a server, a terminal, and a user, each element responsible for a different function.

[0321] The server has the ability to generate personalized recipes for international cuisine using artificial intelligence based on data collected from users. First, the server receives information about the user's preferences and ingredients sent from the terminal and analyzes it. Using the results of this analysis, it selects an appropriate recipe from a database of dishes and further adjusts it to create cooking instructions optimized for the user.

[0322] The terminal plays the role of visually presenting cooking instructions sent from the server to the user. This involves not only displaying recipes but also presenting videos and images according to each step of the cooking process, making it easier for the user to visually understand the procedure. The terminal is also equipped with an emotion engine that can analyze the user's facial expressions and voice to recognize emotions. This emotion information is used to dynamically adjust the presentation of the cooking instructions.

[0323] Users connect to the server via their device and cook according to the provided recipes and cooking procedures. During cooking, their emotions are recognized through the device, and support is provided according to their psychological state at that time. Once cooking is complete, users input their experience and feedback into the device. This feedback and collected emotional data are sent to the server, and the system's overall database and artificial intelligence model are updated, improving the accuracy of future recipe suggestions.

[0324] For example, if a user requests a "light meal for a busy day" and enters the ingredients into the device, the server generates a suitable recipe. If the user shows signs of fatigue during cooking, the device displays advice prompting them to take breaks at each step, helping to smooth the cooking process. In this way, incorporating an emotion engine improves user satisfaction and makes the cooking experience more personal and comfortable.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The user enters their preferences and available ingredients into the device. The device receives this information and prepares to send the data to the server.

[0328] Step 2:

[0329] The server receives information about preferences and ingredients sent from the terminal. Based on this, the server uses artificial intelligence to generate a cooking recipe.

[0330] Step 3:

[0331] The server organizes the recipes it generates and sends the optimal cooking procedure to the user's terminal. This procedure is prepared as a detailed instruction manual.

[0332] Step 4:

[0333] The terminal displays the cooking instructions received from the server and provides visual support to make them easy for the user to understand. Videos or images illustrating each step of the cooking process are presented as needed.

[0334] Step 5:

[0335] The device uses its built-in emotion engine to analyze the user's emotions from their facial expressions and voice. The analysis results are used to understand the user's psychological state.

[0336] Step 6:

[0337] As the user proceeds with cooking, the device dynamically adjusts how the cooking instructions are presented based on recognized emotional information. For example, if the user is experiencing high stress levels, it provides relaxing visuals and sounds.

[0338] Step 7:

[0339] Once the user completes the cooking process, the device collects feedback from the user. This feedback may include comments on the cooking experience and suggestions for improvement.

[0340] Step 8:

[0341] The device sends collected feedback and emotional data to the server. The server analyzes this data and updates its database and artificial intelligence models to make future recipe suggestions more accurate.

[0342] (Example 2)

[0343] 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".

[0344] In modern life, there is a demand for personalized cooking instructions for individual users. However, providing cooking instructions that adapt to user preferences, available ingredients, and even emotional states is not easy. Conventional recipe systems struggle to provide dynamic support based on user emotions or to optimize future suggestions based on feedback from users' own cooking experiences. Therefore, a new system is needed to provide a better personalized cooking experience.

[0345] 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.

[0346] In this invention, the server includes intelligent processing means for generating cooking procedures based on the user's preferences and available ingredients, means for analyzing the user's emotions using emotion recognition means, and data processing means for receiving feedback from the user and updating the data set. This makes it possible to provide the user with personalized cooking procedures, provide appropriate support according to the user's emotional state, and optimize future suggestions based on the feedback.

[0347] "Intelligent processing means" refers to a function that utilizes artificial intelligence to generate appropriate cooking procedures based on the user's preferences and available ingredients.

[0348] The "presentation means" is a function that immediately provides the generated cooking procedure to the user, allowing the user to easily obtain cooking information.

[0349] The "emotion recognition means" is a function that analyzes the user's emotional state through their facial expressions and voice, and dynamically adjusts the support provided during cooking.

[0350] "Display means" refers to a function that provides visual instructions to the user, with the aim of clearly presenting cooking procedures in the form of images or videos.

[0351] The "data processing means" is a function that collects user feedback and updates the overall system data set to improve the accuracy of future cooking suggestions.

[0352] "Video" refers to a media format that uses images or a combination of images and audio to visually explain cooking procedures.

[0353] "Feedback" refers to evaluations and opinions provided by users based on their cooking experience, and is information used to improve the system in the future.

[0354] This invention is a system that enables users to obtain personalized cooking instructions based on their preferences and available ingredients. The system consists of three elements: a server, a terminal, and a user, each performing a different function to carry out the invention.

[0355] The server utilizes an artificial intelligence model as an intelligent processing tool. Ideally, this model should be a generative AI model excelling in natural language processing and data analysis. Specifically, the server receives information about the user's preferences and available ingredients, and selects the optimal recipe from a recipe database. During this process, it adjusts the recipe according to the user's requests, generating personalized cooking instructions. For example, for a user who prefers spicy food, the server might add a spicy twist.

[0356] The device functions as both a presentation tool and an emotion recognition tool. As a presentation tool, it visually provides the user with cooking instructions sent from the server, displaying each step clearly using images and videos. As an emotion recognition tool, its built-in camera and microphone analyze the user's facial expressions and voice to determine their emotions. If the user is having trouble or feeling fatigued during cooking, this information is recognized by the device, and appropriate assistance is provided.

[0357] Users connect to the server via their device and cook according to the generated cooking instructions. During cooking, user feedback and emotional data are collected by the device and sent to the server. The server then uses this feedback to update its database and AI model, improving the accuracy of suggestions for future users.

[0358] As a concrete example, if a user requests a "meal perfect for a quick lunch" and enters the ingredients into their device, the server will create a prompt to generate a suitable recipe. An example of a prompt message to the generating AI model might be: "The user wants a light meal for a busy day, and has chicken, lemon, and garlic on hand. Please generate an appropriate recipe based on this information."

[0359] This system allows users to enjoy a personalized cooking experience and receive support tailored to their emotional state at the time.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The user enters their cooking preferences and available ingredients into the terminal. The terminal collects the entered information as data and sends it to the server. This information includes specific data about the user's preferences and the types and quantities of ingredients used.

[0363] Step 2:

[0364] The server receives data from the terminal and analyzes it using a generative AI model, which is an intelligent processing tool. Specifically, it interprets the input information using natural language processing and searches for appropriate recipes in the database. In this process, the model selects recipes based on the entered keywords and customizes them to be optimal for the user. As a result, personalized cooking instructions are obtained and sent to the terminal.

[0365] Step 3:

[0366] The terminal receives personalized cooking instructions sent from the server and functions as a presentation tool. Specifically, it visually presents the cooking instructions to the user step by step using images and videos. This process allows the user to easily check the steps.

[0367] Step 4:

[0368] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice, functioning as a means of emotion recognition. If the user appears confused or tired, the device analyzes this data and decides whether to provide any additional explanations or advice. Based on this information, the device dynamically adjusts its display to support the user.

[0369] Step 5:

[0370] The user prepares the dish according to the provided cooking instructions. After cooking is complete, the user enters feedback into the device. This feedback includes the clarity of the cooking instructions, satisfaction with the recipe, and areas for improvement.

[0371] Step 6:

[0372] The device sends user feedback and emotional data collected during cooking to a server. The server uses this data to update its database and generative AI model through data processing. This update improves the accuracy of future recipe suggestions and provides a better, more personalized cooking experience.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] Conventional cooking support systems lacked personalized support tailored to individual user preferences and emotions, resulting in a lower quality cooking experience. In particular, they lacked mechanisms to alleviate emotional stress and fatigue during the cooking process, highlighting the need to improve user satisfaction.

[0376] 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.

[0377] In this invention, the server includes processing means using artificial intelligence to generate cooking procedures based on the user's preferences and available items, emotion analysis means for recognizing emotions and adjusting support during cooking, and data processing means for collecting user feedback and forming data to update the storage device. This enables highly personalized cooking support that responds to the user's individual needs and emotions.

[0378] A "user" is an individual who uses the system to engage in cooking activities and receives support tailored to their individual preferences and feelings.

[0379] "Preferences" refer to the personal tendencies of a user's preferred tastes, cooking methods, and ingredient choices.

[0380] "Items" refer to all items that the user possesses, including ingredients and cooking utensils that can be used for cooking.

[0381] "Cooking procedure" refers to the specific steps and processes that a user takes to perform a cooking task, and these constitute the overall flow of the cooking process.

[0382] "Artificial intelligence" is a computer technology that has the ability to generate cooking procedures based on the user's preferences and available items, and to adjust them appropriately.

[0383] "Emotion analysis means" refers to technology that analyzes the user's facial expressions and voice to determine the user's emotions, enabling appropriate adjustments to cooking assistance.

[0384] "Feedback" refers to information and data provided by users after using a system, based on suggestions for system improvements or additional information.

[0385] "Data formation methods" refer to processes and technologies used to update databases based on collected information and feedback, thereby improving the accuracy and efficiency of the system.

[0386] A "storage device" is a digital data storage device that holds collected data and generated information, and allows it to be retrieved as needed.

[0387] The system of this invention combines artificial intelligence and emotion analysis technology to improve the user's cooking experience. The server receives information about the user's preferences and available items, and generates cooking instructions based on this information. This process utilizes a generative AI model in the cloud. Specifically, the server analyzes data from the user, selects the optimal procedure from a multinational cuisine recipe database, and then individually adjusts it to generate a personalized recipe.

[0388] The terminal visually presents cooking instructions received from the server to the user. This includes displaying videos and still images for each step of the cooking process, allowing the user to easily follow along with the instructions. The terminal also incorporates emotion analysis capabilities, using a camera and microphone to detect the user's facial expressions and voice, and analyzing their emotions in real time. If the user is experiencing stress, appropriate breaks or encouraging messages will be displayed.

[0389] The specific hardware used includes humanoid robots and tablet devices, and software such as OpenCV and Google Cloud Speech-to-Text is used for emotion analysis. As an example, when a user enters "I want a fun cooking experience" into their device, the server provides a recipe corresponding to that request, and the device displays "You're doing great!" when it detects the user's positive emotions.

[0390] Examples of prompts for a generative AI model include: "Detect emotions from the user's facial expressions and voice, and suggest appropriate advice and breaks to ensure the cooking process proceeds smoothly."

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The user inputs information about their preferences and available items into the terminal. The terminal then sends this information to the server. The input includes the types of ingredients and dishes selected by the user, and the output is data sent to the server.

[0394] Step 2:

[0395] The server analyzes the information received from the terminal and generates appropriate cooking instructions using a generative AI model. Here, user preference data is used as input, and personalized cooking instructions are generated as output. The specific generative AI model determines a multinational cuisine recipe that suits the user's preferences.

[0396] Step 3:

[0397] The server generates the cooking instructions and sends them to the terminal. The input is the generated cooking instructions, and the output is the transfer of information to the terminal.

[0398] Step 4:

[0399] The terminal visually presents the cooking instructions received from the server to the user. The terminal displays the cooking instructions step by step using videos and still images. This allows the user to understand the cooking instructions more intuitively through visual information.

[0400] Step 5:

[0401] The device analyzes the user's emotions in real time using its camera and microphone. Input includes the user's facial expressions and voice data, which are then used by the emotion analysis system to perform data calculations. The output is emotionally relevant feedback.

[0402] Step 6:

[0403] The device displays advice and messages tailored to the user's emotions based on the analysis results. For example, if the user shows a tired expression, it will display a message encouraging them to take a break. This makes the cooking experience more responsive to the user's psychological state.

[0404] Step 7:

[0405] After the user completes cooking, they enter feedback into the terminal. This includes evaluations of the cooking experience and the quality of the finished product. The feedback is processed by a data generation system and sent to the server, so it can be used to improve future suggestions.

[0406] Step 8:

[0407] The server receives feedback from users and updates the database. The input is feedback data, and the output is an adjusted cooking procedure for the next cooking session. This process improves the overall accuracy of the system and allows it to accommodate diverse user preferences.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention is a system that makes it easy for users to enjoy international cuisine based on their preferences and available ingredients. The system mainly consists of three elements: a server, a terminal, and a user, each playing a specific role.

[0425] The server uses artificial intelligence to generate personalized cooking instructions based on data submitted by the user. Specifically, the server analyzes the user's preferences and ingredient information and identifies the optimal recipe from a database of international cuisines. It then generates the necessary cooking steps based on the identified recipe and outputs them in a format suitable for the user.

[0426] The terminal is a device that provides users with cooking instructions sent from the server. The terminal displays the cooking instructions in a visually easy-to-understand format and provides supplementary information, including videos and images, to support each step of the cooking process. This allows users to smoothly proceed through the actual cooking process.

[0427] Users receive recipes and cooking instructions from the server via their device and cook according to the instructions. After cooking is complete, users input their experience as feedback into their device, which is then sent to the server. The server analyzes this feedback and uses it to update its database and train its artificial intelligence model, thereby improving the accuracy of subsequent recipe suggestions.

[0428] For example, if a user enters "I want to make a spicy chicken dish" into the terminal, the server will generate a recipe for spicy chicken curry based on that information. The terminal will then display an ingredient list and cooking instructions according to the generated recipe, and will also provide videos showing the intermediate steps of the cooking process at appropriate times.

[0429] Thus, the present invention is realized in a form that provides a cooking experience tailored to the user's needs and further enables improvement of the entire system through continuous learning.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The user enters their preferences and the ingredients they use into the device. The device receives this information, organizes the data, and then prepares to send it to the server.

[0433] Step 2:

[0434] The device sends data about user preferences and materials to the server. The server receives this data and proceeds with analysis.

[0435] Step 3:

[0436] The server uses an artificial intelligence model to analyze the data it receives. The analysis results are then used to generate appropriate international cuisine recipes.

[0437] Step 4:

[0438] The server organizes the generated recipes and sends them to the user's terminal as instructions in an easy-to-understand format.

[0439] Step 5:

[0440] The terminal presents the user with cooking instructions provided by the server and displays videos or images to assist the cooking process as needed.

[0441] Step 6:

[0442] The user cooks while referring to the device. Once cooking is complete, the user enters feedback about the cooking experience into the device.

[0443] Step 7:

[0444] The device sends user feedback to the server. The server receives the feedback and uses it to update the database and train artificial intelligence models.

[0445] (Example 1)

[0446] 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."

[0447] Providing users with concise and effective cooking instructions tailored to their diverse preferences and available ingredients is challenging. Furthermore, efficiently utilizing user feedback to improve the overall accuracy of the system's suggestions is also difficult. Additionally, improving the user's cooking experience through visual support during the cooking process is another challenge.

[0448] 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.

[0449] In this invention, the server includes information processing means for analyzing user preferences and available ingredients and creating cooking procedures using a generative AI model; presentation means for visually presenting the generated cooking procedures to the user; and display means for providing videos or images as visual support when the user is cooking. This makes it possible to provide cooking procedures tailored to the user, improve the accuracy of suggestions by utilizing feedback, and enhance the user's cooking experience.

[0450] A "user" refers to an individual who uses the system, inputting information about their preferences and ingredients, and receiving cooking instructions.

[0451] "Preferences" refer to choices and tendencies based on the user's likes and desires, and serve as criteria when making individual food selections.

[0452] "Available ingredients" refers to the specific foods and seasonings that users can use in cooking, and is an important factor in selecting a dish.

[0453] A "generative AI model" refers to a set of algorithms and programs that utilize artificial intelligence to generate optimal cooking procedures from a database.

[0454] "Information processing means" refers to a function within a system that analyzes data from users and generates cooking procedures and aggregates and analyzes feedback.

[0455] "Presentation means" refers to methods or interfaces for communicating generated cooking instructions to the user, primarily by presenting information through the terminal screen.

[0456] "Display means" refers to a function that displays videos or images to provide visual support for cooking procedures, and is used to improve the user's cooking experience.

[0457] "Feedback" refers to the evaluations and opinions that users input about the results of their cooking, and it is information that helps improve the system.

[0458] A "database" is part of a system that stores and manages recipe information and related data for multinational cuisine, and is used to generate cooking procedures.

[0459] This invention provides a system that offers users the optimal cooking procedure based on their diverse preferences and the ingredients available at home. This system consists of three elements: a server, a terminal, and a user, each playing a specific role.

[0460] The server receives prompt messages from the user and suggests dishes based on their content. Specifically, the server utilizes a generative AI model to search and generate the optimal recipe from a broad database of international cuisines, based on the user's preferences and ingredient information. This process is expected to utilize programming languages ​​such as Python and Java, as well as AI libraries such as TensorFlow and PyTorch. The server then sends the generated cooking instructions to the terminal.

[0461] The terminal's role is to present cooking instructions sent from the server to the user. The terminal utilizes interfaces such as a display and speaker to provide the cooking instructions to the user in a visually easy-to-understand format using text, images, and videos. It also has a function to display videos that show each step of the recipe at the right time, allowing the user to proceed smoothly with the cooking process.

[0462] Users perform the actual cooking based on information received through their device. After cooking is complete, users enter feedback about their cooking experience into the device. This feedback is sent to the server and used to improve future suggestions.

[0463] For example, if a user enters a prompt message such as "I want to make a spicy chicken dish" into their device, the server can generate an optimal spicy chicken curry recipe based on this message and send it to the device along with cooking instructions and an ingredient list. In this way, it is possible to provide users with individually customized cooking suggestions.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The user inputs prompt messages and information about the ingredients they possess through the terminal. For example, they might input the prompt message, "I want to make a spicy chicken dish." Based on this input, the terminal generates a dataset and sends that data to the server.

[0467] Step 2:

[0468] The server analyzes the user's prompt message and ingredient information received from the terminal. It processes the received data to identify the user's preferences and constraints. Based on the results of this analysis, it extracts relevant recipe candidates from a multinational cuisine database.

[0469] Step 3:

[0470] The server uses the extracted recipe candidates to optimize them using a generation AI model. Specifically, the AI ​​model evaluates and selects the recipe candidate that best matches the user's preferences. Based on the selected recipe, it generates specific cooking instructions. Each step in these instructions includes necessary ingredient information and timing.

[0471] Step 4:

[0472] The server sends the generated cooking instructions and ingredient list back to the terminal. The terminal visually displays the received information to the user. To make cooking easier for the user, the instructions are organized clearly and supplementary media such as videos and images may be added.

[0473] Step 5:

[0474] The user cooks according to the cooking instructions provided on the device. Once cooking is complete, they input the results and experience as feedback into the device, generating feedback data.

[0475] Step 6:

[0476] The device sends user feedback data to the server. The server analyzes this feedback, updates the database information, and uses it as training data for the generated AI model. This process allows the system to continuously improve the accuracy of subsequent recipe suggestions.

[0477] (Application Example 1)

[0478] 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."

[0479] In modern society, there is a need for systems that easily suggest dishes tailored to individual preferences and health conditions, and that support the cooking process. However, existing solutions struggle to efficiently incorporate diverse user preferences and available ingredients into recipe suggestions, and they lack sufficient support for beginners to understand the cooking process. In particular, there is a lack of continuous improvement of systems based on user feedback, making it difficult to adapt to individual needs. Innovative methods are needed to solve these problems.

[0480] 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.

[0481] In this invention, the server includes information processing means for generating cooking procedures based on the user's preferences and available ingredients; display means for presenting the generated cooking procedures to the user; information display means for providing visual support to the user during cooking; information processing means for receiving feedback from the user and updating the information storage location; and means for suggesting the most suitable dish based on the user's preference data. This enables the efficient provision of recipes tailored to individual user needs, visual support for the cooking process, and system improvements based on feedback.

[0482] A "user" is an individual who utilizes the system, providing information about their preferences and available ingredients, and cooking based on the provided recipes.

[0483] "Preferences" refer to information that represents the tastes and cooking styles that users enjoy, and are important input data for the system to generate the optimal cooking procedure for each individual user.

[0484] "Available ingredients" refer to the ingredients the user currently has on hand, and the system uses these as a basis to suggest realistic recipes.

[0485] "Cooking procedure" refers to the specific steps and processes for completing a dish, which are generated by the system based on the user's preferences and ingredients.

[0486] "Artificial intelligence" is a technology that analyzes data provided by users and generates optimal cooking procedures, and is a component of a system that gives the system the ability to make personalized suggestions.

[0487] "Information processing means" refers to the means used on a server to perform data analysis and recipe generation based on user preferences and ingredients.

[0488] "Display means" refers to the means by which a system presents the generated cooking procedure to the user, and includes technologies and devices that provide information visually.

[0489] "Information display means" refers to display functions that provide visual support to users during cooking, and play a role in helping users understand through videos and images.

[0490] "Feedback" refers to information that users report to the system about their actual cooking experiences and impressions, contributing to the continuous improvement of the system.

[0491] An "information storage location" is a place where a system stores user feedback and preference data, providing storage functionality for subsequent data analysis and recommendations.

[0492] "Optimal dish suggestions" refers to suggesting dishes that are likely to satisfy the user the most, based on the user's preferences, available ingredients, and past feedback.

[0493] The system for carrying out the present invention comprises information processing means, display means, information display means, and feedback processing means. The server performs analysis using a generative AI model based on preference and usable material data received from the user. The hardware used here includes a server utilizing cloud computing, and the software uses machine learning frameworks such as TensorFlow and PyTorch for data analysis.

[0494] The server generates the optimal recipe that best matches the user's preferences and sends it to the device. At this time, specific cooking instructions and a list of ingredients are presented to the user through a display. The device has an application developed with React Native installed, allowing the user to intuitively understand and follow the cooking instructions.

[0495] Furthermore, videos and images are provided to offer visual support at each step of the cooking process as means of displaying information. This visual content is retrieved through services such as the Cooking API and delivered to users in a timely manner.

[0496] As a feedback processing mechanism, the terminal is equipped with a function to collect user feedback on their cooking experience and return it to the server. This feedback is stored in an information storage location and contributes to improving the generated AI model through continuous learning.

[0497] For example, if a user inputs "I want to try making a complex dish using my favorite spices," the server will suggest a recipe (e.g., Indian-style spice curry) that matches the user's preferences and ingredients, and the app will guide the user through the cooking process with a video. Another example of a prompt for the generating AI model would be: "Generate the optimal recipe based on the user's ingredients (chicken, tomatoes, spices) and preferences (spicy). Provide step-by-step instructions with image guides."

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The user enters their preferences and available ingredients using a terminal. The entered data is sent to the server in text format. It is crucial that the user's preferences and ingredient information are accurately captured in this step.

[0501] Step 2:

[0502] The server analyzes the received user data and generates personalized recipes using a generative AI model. Based on the input data (user preferences and ingredients), the AI ​​selects the optimal cooking procedure and outputs it as a recipe. Here, TensorFlow and PyTorch are used to analyze the AI ​​model.

[0503] Step 3:

[0504] The server sends the generated recipe information to the terminal. This output includes specific cooking instructions and ingredient lists. Data transmission to the terminal occurs over the network and is based on a communication protocol (e.g., HTTP).

[0505] Step 4:

[0506] The device visually presents the received recipe to the user. The outputted cooking instructions are displayed through the interface of an app built with React Native. They are displayed step-by-step for easy user understanding.

[0507] Step 5:

[0508] The device utilizes information display means to present users with videos and images based on cooking procedures. This involves data processing to retrieve relevant content using the Cooking API. The output consists of videos and images to provide visual support to the user at the appropriate time.

[0509] Step 6:

[0510] After cooking is complete, the user enters their feedback into the terminal. This user feedback is sent to the server as a new dataset. This feedback data is saved in text format.

[0511] Step 7:

[0512] The server updates the information storage location and improves the accuracy of the generated AI model based on the feedback data. This process analyzes the feedback and incorporates it into the AI ​​model's learning process. The output is an improvement in the model's accuracy in future recipe suggestions.

[0513] 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.

[0514] This invention combines a system that generates personalized cooking instructions tailored to the user's preferences and available ingredients, and provides visual assistance, with an emotion engine that recognizes the user's emotions. The system consists of three elements: a server, a terminal, and a user, each element responsible for a different function.

[0515] The server has the ability to generate personalized recipes for international cuisine using artificial intelligence based on data collected from users. First, the server receives information about the user's preferences and ingredients sent from the terminal and analyzes it. Using the results of this analysis, it selects an appropriate recipe from a database of dishes and further adjusts it to create cooking instructions optimized for the user.

[0516] The terminal plays the role of visually presenting cooking instructions sent from the server to the user. This involves not only displaying recipes but also presenting videos and images according to each step of the cooking process, making it easier for the user to visually understand the procedure. The terminal is also equipped with an emotion engine that can analyze the user's facial expressions and voice to recognize emotions. This emotion information is used to dynamically adjust the presentation of the cooking instructions.

[0517] Users connect to the server via their device and cook according to the provided recipes and cooking procedures. During cooking, their emotions are recognized through the device, and support is provided according to their psychological state at that time. Once cooking is complete, users input their experience and feedback into the device. This feedback and collected emotional data are sent to the server, and the system's overall database and artificial intelligence model are updated, improving the accuracy of future recipe suggestions.

[0518] For example, if a user requests a "light meal for a busy day" and enters the ingredients into the device, the server generates a suitable recipe. If the user shows signs of fatigue during cooking, the device displays advice prompting them to take breaks at each step, helping to smooth the cooking process. In this way, incorporating an emotion engine improves user satisfaction and makes the cooking experience more personal and comfortable.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The user enters their preferences and available ingredients into the device. The device receives this information and prepares to send the data to the server.

[0522] Step 2:

[0523] The server receives information about preferences and ingredients sent from the terminal. Based on this, the server uses artificial intelligence to generate a cooking recipe.

[0524] Step 3:

[0525] The server organizes the recipes it generates and sends the optimal cooking procedure to the user's terminal. This procedure is prepared as a detailed instruction manual.

[0526] Step 4:

[0527] The terminal displays the cooking instructions received from the server and provides visual support to make them easy for the user to understand. Videos or images illustrating each step of the cooking process are presented as needed.

[0528] Step 5:

[0529] The device uses its built-in emotion engine to analyze the user's emotions from their facial expressions and voice. The analysis results are used to understand the user's psychological state.

[0530] Step 6:

[0531] As the user proceeds with cooking, the device dynamically adjusts how the cooking instructions are presented based on recognized emotional information. For example, if the user is experiencing high stress levels, it provides relaxing visuals and sounds.

[0532] Step 7:

[0533] Once the user completes the cooking process, the device collects feedback from the user. This feedback may include comments on the cooking experience and suggestions for improvement.

[0534] Step 8:

[0535] The device sends collected feedback and emotional data to the server. The server analyzes this data and updates its database and artificial intelligence models to make future recipe suggestions more accurate.

[0536] (Example 2)

[0537] 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."

[0538] In modern life, there is a demand for personalized cooking instructions for individual users. However, providing cooking instructions that adapt to user preferences, available ingredients, and even emotional states is not easy. Conventional recipe systems struggle to provide dynamic support based on user emotions or to optimize future suggestions based on feedback from users' own cooking experiences. Therefore, a new system is needed to provide a better personalized cooking experience.

[0539] 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.

[0540] In this invention, the server includes intelligent processing means for generating cooking procedures based on the user's preferences and available ingredients, means for analyzing the user's emotions using emotion recognition means, and data processing means for receiving feedback from the user and updating the data set. This makes it possible to provide the user with personalized cooking procedures, provide appropriate support according to the user's emotional state, and optimize future suggestions based on the feedback.

[0541] "Intelligent processing means" refers to a function that utilizes artificial intelligence to generate appropriate cooking procedures based on the user's preferences and available ingredients.

[0542] The "presentation means" is a function that immediately provides the generated cooking procedure to the user, allowing the user to easily obtain cooking information.

[0543] The "emotion recognition means" is a function that analyzes the user's emotional state through their facial expressions and voice, and dynamically adjusts the support provided during cooking.

[0544] "Display means" refers to a function that provides visual instructions to the user, with the aim of clearly presenting cooking procedures in the form of images or videos.

[0545] The "data processing means" is a function that collects user feedback and updates the overall system data set to improve the accuracy of future cooking suggestions.

[0546] "Video" refers to a media format that uses images or a combination of images and audio to visually explain cooking procedures.

[0547] "Feedback" refers to evaluations and opinions provided by users based on their cooking experience, and is information used to improve the system in the future.

[0548] This invention is a system that enables users to obtain personalized cooking instructions based on their preferences and available ingredients. The system consists of three elements: a server, a terminal, and a user, each performing a different function to carry out the invention.

[0549] The server utilizes an artificial intelligence model as an intelligent processing tool. Ideally, this model should be a generative AI model excelling in natural language processing and data analysis. Specifically, the server receives information about the user's preferences and available ingredients, and selects the optimal recipe from a recipe database. During this process, it adjusts the recipe according to the user's requests, generating personalized cooking instructions. For example, for a user who prefers spicy food, the server might add a spicy twist.

[0550] The device functions as both a presentation tool and an emotion recognition tool. As a presentation tool, it visually provides the user with cooking instructions sent from the server, displaying each step clearly using images and videos. As an emotion recognition tool, its built-in camera and microphone analyze the user's facial expressions and voice to determine their emotions. If the user is having trouble or feeling fatigued during cooking, this information is recognized by the device, and appropriate assistance is provided.

[0551] Users connect to the server via their device and cook according to the generated cooking instructions. During cooking, user feedback and emotional data are collected by the device and sent to the server. The server then uses this feedback to update its database and AI model, improving the accuracy of suggestions for future users.

[0552] As a concrete example, if a user requests a "meal perfect for a quick lunch" and enters the ingredients into their device, the server will create a prompt to generate a suitable recipe. An example of a prompt message to the generating AI model might be: "The user wants a light meal for a busy day, and has chicken, lemon, and garlic on hand. Please generate an appropriate recipe based on this information."

[0553] This system allows users to enjoy a personalized cooking experience and receive support tailored to their emotional state at the time.

[0554] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0555] Step 1:

[0556] The user enters their cooking preferences and available ingredients into the terminal. The terminal collects the entered information as data and sends it to the server. This information includes specific data about the user's preferences and the types and quantities of ingredients used.

[0557] Step 2:

[0558] The server receives data from the terminal and analyzes it using a generative AI model, which is an intelligent processing tool. Specifically, it interprets the input information using natural language processing and searches for appropriate recipes in the database. In this process, the model selects recipes based on the entered keywords and customizes them to be optimal for the user. As a result, personalized cooking instructions are obtained and sent to the terminal.

[0559] Step 3:

[0560] The terminal receives personalized cooking instructions sent from the server and functions as a presentation tool. Specifically, it visually presents the cooking instructions to the user step by step using images and videos. This process allows the user to easily check the steps.

[0561] Step 4:

[0562] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice, functioning as a means of emotion recognition. If the user appears confused or tired, the device analyzes this data and decides whether to provide any additional explanations or advice. Based on this information, the device dynamically adjusts its display to support the user.

[0563] Step 5:

[0564] The user prepares the dish according to the provided cooking instructions. After cooking is complete, the user enters feedback into the device. This feedback includes the clarity of the cooking instructions, satisfaction with the recipe, and areas for improvement.

[0565] Step 6:

[0566] The device sends user feedback and emotional data collected during cooking to a server. The server uses this data to update its database and generative AI model through data processing. This update improves the accuracy of future recipe suggestions and provides a better, more personalized cooking experience.

[0567] (Application Example 2)

[0568] 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."

[0569] Conventional cooking support systems lacked personalized support tailored to individual user preferences and emotions, resulting in a lower quality cooking experience. In particular, they lacked mechanisms to alleviate emotional stress and fatigue during the cooking process, highlighting the need to improve user satisfaction.

[0570] 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.

[0571] In this invention, the server includes processing means using artificial intelligence to generate cooking procedures based on the user's preferences and available items, emotion analysis means for recognizing emotions and adjusting support during cooking, and data processing means for collecting user feedback and forming data to update the storage device. This enables highly personalized cooking support that responds to the user's individual needs and emotions.

[0572] A "user" is an individual who uses the system to engage in cooking activities and receives support tailored to their individual preferences and feelings.

[0573] "Preferences" refer to the personal tendencies of a user's preferred tastes, cooking methods, and ingredient choices.

[0574] "Items" refer to all items that the user possesses, including ingredients and cooking utensils that can be used for cooking.

[0575] "Cooking procedure" refers to the specific steps and processes that a user takes to perform a cooking task, and these constitute the overall flow of the cooking process.

[0576] "Artificial intelligence" is a computer technology that has the ability to generate cooking procedures based on the user's preferences and available items, and to adjust them appropriately.

[0577] "Emotion analysis means" refers to technology that analyzes the user's facial expressions and voice to determine the user's emotions, enabling appropriate adjustments to cooking assistance.

[0578] "Feedback" refers to information and data provided by users after using a system, based on suggestions for system improvements or additional information.

[0579] "Data formation methods" refer to processes and technologies used to update databases based on collected information and feedback, thereby improving the accuracy and efficiency of the system.

[0580] A "storage device" is a digital data storage device that holds collected data and generated information, and allows it to be retrieved as needed.

[0581] The system of this invention combines artificial intelligence and emotion analysis technology to improve the user's cooking experience. The server receives information about the user's preferences and available items, and generates cooking instructions based on this information. This process utilizes a generative AI model in the cloud. Specifically, the server analyzes data from the user, selects the optimal procedure from a multinational cuisine recipe database, and then individually adjusts it to generate a personalized recipe.

[0582] The terminal visually presents cooking instructions received from the server to the user. This includes displaying videos and still images for each step of the cooking process, allowing the user to easily follow along with the instructions. The terminal also incorporates emotion analysis capabilities, using a camera and microphone to detect the user's facial expressions and voice, and analyzing their emotions in real time. If the user is experiencing stress, appropriate breaks or encouraging messages will be displayed.

[0583] The specific hardware used includes humanoid robots and tablet devices, and software such as OpenCV and Google Cloud Speech-to-Text is used for emotion analysis. As an example, when a user enters "I want a fun cooking experience" into their device, the server provides a recipe corresponding to that request, and the device displays "You're doing great!" when it detects the user's positive emotions.

[0584] Examples of prompts for a generative AI model include: "Detect emotions from the user's facial expressions and voice, and suggest appropriate advice and breaks to ensure the cooking process proceeds smoothly."

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0586] Step 1:

[0587] The user inputs information about their preferences and available items into the terminal. The terminal then sends this information to the server. The input includes the types of ingredients and dishes selected by the user, and the output is data sent to the server.

[0588] Step 2:

[0589] The server analyzes the information received from the terminal and generates appropriate cooking instructions using a generative AI model. Here, user preference data is used as input, and personalized cooking instructions are generated as output. The specific generative AI model determines a multinational cuisine recipe that suits the user's preferences.

[0590] Step 3:

[0591] The server generates the cooking instructions and sends them to the terminal. The input is the generated cooking instructions, and the output is the transfer of information to the terminal.

[0592] Step 4:

[0593] The terminal visually presents the cooking instructions received from the server to the user. The terminal displays the cooking instructions step by step using videos and still images. This allows the user to understand the cooking instructions more intuitively through visual information.

[0594] Step 5:

[0595] The device analyzes the user's emotions in real time using its camera and microphone. Input includes the user's facial expressions and voice data, which are then used by the emotion analysis system to perform data calculations. The output is emotionally relevant feedback.

[0596] Step 6:

[0597] The device displays advice and messages tailored to the user's emotions based on the analysis results. For example, if the user shows a tired expression, it will display a message encouraging them to take a break. This makes the cooking experience more responsive to the user's psychological state.

[0598] Step 7:

[0599] After the user completes cooking, they enter feedback into the terminal. This includes evaluations of the cooking experience and the quality of the finished product. The feedback is processed by a data generation system and sent to the server, so it can be used to improve future suggestions.

[0600] Step 8:

[0601] The server receives feedback from users and updates the database. The input is feedback data, and the output is an adjusted cooking procedure for the next cooking session. This process improves the overall accuracy of the system and allows it to accommodate diverse user preferences.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] [Fourth Embodiment]

[0606] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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".

[0619] This invention is a system that makes it easy for users to enjoy international cuisine based on their preferences and available ingredients. The system mainly consists of three elements: a server, a terminal, and a user, each playing a specific role.

[0620] The server uses artificial intelligence to generate personalized cooking instructions based on data submitted by the user. Specifically, the server analyzes the user's preferences and ingredient information and identifies the optimal recipe from a database of international cuisines. It then generates the necessary cooking steps based on the identified recipe and outputs them in a format suitable for the user.

[0621] The terminal is a device that provides users with cooking instructions sent from the server. The terminal displays the cooking instructions in a visually easy-to-understand format and provides supplementary information, including videos and images, to support each step of the cooking process. This allows users to smoothly proceed through the actual cooking process.

[0622] Users receive recipes and cooking instructions from the server via their device and cook according to the instructions. After cooking is complete, users input their experience as feedback into their device, which is then sent to the server. The server analyzes this feedback and uses it to update its database and train its artificial intelligence model, thereby improving the accuracy of subsequent recipe suggestions.

[0623] For example, if a user enters "I want to make a spicy chicken dish" into the terminal, the server will generate a recipe for spicy chicken curry based on that information. The terminal will then display an ingredient list and cooking instructions according to the generated recipe, and will also provide videos showing the intermediate steps of the cooking process at appropriate times.

[0624] Thus, the present invention is realized in a form that provides a cooking experience tailored to the user's needs and further enables improvement of the entire system through continuous learning.

[0625] The following describes the processing flow.

[0626] Step 1:

[0627] The user enters their preferences and the ingredients they use into the device. The device receives this information, organizes the data, and then prepares to send it to the server.

[0628] Step 2:

[0629] The device sends data about user preferences and materials to the server. The server receives this data and proceeds with analysis.

[0630] Step 3:

[0631] The server uses an artificial intelligence model to analyze the data it receives. The analysis results are then used to generate appropriate international cuisine recipes.

[0632] Step 4:

[0633] The server organizes the generated recipes and sends them to the user's terminal as instructions in an easy-to-understand format.

[0634] Step 5:

[0635] The terminal presents the user with cooking instructions provided by the server and displays videos or images to assist the cooking process as needed.

[0636] Step 6:

[0637] The user cooks while referring to the device. Once cooking is complete, the user enters feedback about the cooking experience into the device.

[0638] Step 7:

[0639] The device sends user feedback to the server. The server receives the feedback and uses it to update the database and train artificial intelligence models.

[0640] (Example 1)

[0641] 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".

[0642] Providing users with concise and effective cooking instructions tailored to their diverse preferences and available ingredients is challenging. Furthermore, efficiently utilizing user feedback to improve the overall accuracy of the system's suggestions is also difficult. Additionally, improving the user's cooking experience through visual support during the cooking process is another challenge.

[0643] 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.

[0644] In this invention, the server includes information processing means for analyzing user preferences and available ingredients and creating cooking procedures using a generative AI model; presentation means for visually presenting the generated cooking procedures to the user; and display means for providing videos or images as visual support when the user is cooking. This makes it possible to provide cooking procedures tailored to the user, improve the accuracy of suggestions by utilizing feedback, and enhance the user's cooking experience.

[0645] A "user" refers to an individual who uses the system, inputting information about their preferences and ingredients, and receiving cooking instructions.

[0646] "Preferences" refer to choices and tendencies based on the user's likes and desires, and serve as criteria when making individual food selections.

[0647] "Available ingredients" refers to the specific foods and seasonings that users can use in cooking, and is an important factor in selecting a dish.

[0648] A "generative AI model" refers to a set of algorithms and programs that utilize artificial intelligence to generate optimal cooking procedures from a database.

[0649] "Information processing means" refers to a function within a system that analyzes data from users and generates cooking procedures and aggregates and analyzes feedback.

[0650] "Presentation means" refers to methods or interfaces for communicating generated cooking instructions to the user, primarily by presenting information through the terminal screen.

[0651] "Display means" refers to a function that displays videos or images to provide visual support for cooking procedures, and is used to improve the user's cooking experience.

[0652] "Feedback" refers to the evaluations and opinions that users input about the results of their cooking, and it is information that helps improve the system.

[0653] A "database" is part of a system that stores and manages recipe information and related data for multinational cuisine, and is used to generate cooking procedures.

[0654] This invention provides a system that offers users the optimal cooking procedure based on their diverse preferences and the ingredients available at home. This system consists of three elements: a server, a terminal, and a user, each playing a specific role.

[0655] The server receives prompt messages from the user and suggests dishes based on their content. Specifically, the server utilizes a generative AI model to search and generate the optimal recipe from a broad database of international cuisines, based on the user's preferences and ingredient information. This process is expected to utilize programming languages ​​such as Python and Java, as well as AI libraries such as TensorFlow and PyTorch. The server then sends the generated cooking instructions to the terminal.

[0656] The terminal's role is to present cooking instructions sent from the server to the user. The terminal utilizes interfaces such as a display and speaker to provide the cooking instructions to the user in a visually easy-to-understand format using text, images, and videos. It also has a function to display videos that show each step of the recipe at the right time, allowing the user to proceed smoothly with the cooking process.

[0657] Users perform the actual cooking based on information received through their device. After cooking is complete, users enter feedback about their cooking experience into the device. This feedback is sent to the server and used to improve future suggestions.

[0658] For example, if a user enters a prompt message such as "I want to make a spicy chicken dish" into their device, the server can generate an optimal spicy chicken curry recipe based on this message and send it to the device along with cooking instructions and an ingredient list. In this way, it is possible to provide users with individually customized cooking suggestions.

[0659] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0660] Step 1:

[0661] The user inputs prompt messages and information about the ingredients they possess through the terminal. For example, they might input the prompt message, "I want to make a spicy chicken dish." Based on this input, the terminal generates a dataset and sends that data to the server.

[0662] Step 2:

[0663] The server analyzes the user's prompt message and ingredient information received from the terminal. It processes the received data to identify the user's preferences and constraints. Based on the results of this analysis, it extracts relevant recipe candidates from a multinational cuisine database.

[0664] Step 3:

[0665] The server uses the extracted recipe candidates to optimize them using a generation AI model. Specifically, the AI ​​model evaluates and selects the recipe candidate that best matches the user's preferences. Based on the selected recipe, it generates specific cooking instructions. Each step in these instructions includes necessary ingredient information and timing.

[0666] Step 4:

[0667] The server sends the generated cooking instructions and ingredient list back to the terminal. The terminal visually displays the received information to the user. To make cooking easier for the user, the instructions are organized clearly and supplementary media such as videos and images may be added.

[0668] Step 5:

[0669] The user cooks according to the cooking instructions provided on the device. Once cooking is complete, they input the results and experience as feedback into the device, generating feedback data.

[0670] Step 6:

[0671] The device sends user feedback data to the server. The server analyzes this feedback, updates the database information, and uses it as training data for the generated AI model. This process allows the system to continuously improve the accuracy of subsequent recipe suggestions.

[0672] (Application Example 1)

[0673] 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".

[0674] In modern society, there is a need for systems that easily suggest dishes tailored to individual preferences and health conditions, and that support the cooking process. However, existing solutions struggle to efficiently incorporate diverse user preferences and available ingredients into recipe suggestions, and they lack sufficient support for beginners to understand the cooking process. In particular, there is a lack of continuous improvement of systems based on user feedback, making it difficult to adapt to individual needs. Innovative methods are needed to solve these problems.

[0675] 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.

[0676] In this invention, the server includes information processing means for generating cooking procedures based on the user's preferences and available ingredients; display means for presenting the generated cooking procedures to the user; information display means for providing visual support to the user during cooking; information processing means for receiving feedback from the user and updating the information storage location; and means for suggesting the most suitable dish based on the user's preference data. This enables the efficient provision of recipes tailored to individual user needs, visual support for the cooking process, and system improvements based on feedback.

[0677] A "user" is an individual who utilizes the system, providing information about their preferences and available ingredients, and cooking based on the provided recipes.

[0678] "Preferences" refer to information that represents the tastes and cooking styles that users enjoy, and are important input data for the system to generate the optimal cooking procedure for each individual user.

[0679] "Available ingredients" refer to the ingredients the user currently has on hand, and the system uses these as a basis to suggest realistic recipes.

[0680] "Cooking procedure" refers to the specific steps and processes for completing a dish, which are generated by the system based on the user's preferences and ingredients.

[0681] "Artificial intelligence" is a technology that analyzes data provided by users and generates optimal cooking procedures, and is a component of a system that gives the system the ability to make personalized suggestions.

[0682] "Information processing means" refers to the means used on a server to perform data analysis and recipe generation based on user preferences and ingredients.

[0683] "Display means" refers to the means by which a system presents the generated cooking procedure to the user, and includes technologies and devices that provide information visually.

[0684] "Information display means" refers to display functions that provide visual support to users during cooking, and play a role in helping users understand through videos and images.

[0685] "Feedback" refers to information that users report to the system about their actual cooking experiences and impressions, contributing to the continuous improvement of the system.

[0686] An "information storage location" is a place where a system stores user feedback and preference data, providing storage functionality for subsequent data analysis and recommendations.

[0687] "Optimal dish suggestions" refers to suggesting dishes that are likely to satisfy the user the most, based on the user's preferences, available ingredients, and past feedback.

[0688] The system for carrying out the present invention comprises information processing means, display means, information display means, and feedback processing means. The server performs analysis using a generative AI model based on preference and usable material data received from the user. The hardware used here includes a server utilizing cloud computing, and the software uses machine learning frameworks such as TensorFlow and PyTorch for data analysis.

[0689] The server generates the optimal recipe that best matches the user's preferences and sends it to the device. At this time, specific cooking instructions and a list of ingredients are presented to the user through a display. The device has an application developed with React Native installed, allowing the user to intuitively understand and follow the cooking instructions.

[0690] Furthermore, videos and images are provided to offer visual support at each step of the cooking process as means of displaying information. This visual content is retrieved through services such as the Cooking API and delivered to users in a timely manner.

[0691] As a feedback processing mechanism, the terminal is equipped with a function to collect user feedback on their cooking experience and return it to the server. This feedback is stored in an information storage location and contributes to improving the generated AI model through continuous learning.

[0692] For example, if a user inputs "I want to try making a complex dish using my favorite spices," the server will suggest a recipe (e.g., Indian-style spice curry) that matches the user's preferences and ingredients, and the app will guide the user through the cooking process with a video. Another example of a prompt for the generating AI model would be: "Generate the optimal recipe based on the user's ingredients (chicken, tomatoes, spices) and preferences (spicy). Provide step-by-step instructions with image guides."

[0693] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0694] Step 1:

[0695] The user enters their preferences and available ingredients using a terminal. The entered data is sent to the server in text format. It is crucial that the user's preferences and ingredient information are accurately captured in this step.

[0696] Step 2:

[0697] The server analyzes the received user data and generates personalized recipes using a generative AI model. Based on the input data (user preferences and ingredients), the AI ​​selects the optimal cooking procedure and outputs it as a recipe. Here, TensorFlow and PyTorch are used to analyze the AI ​​model.

[0698] Step 3:

[0699] The server sends the generated recipe information to the terminal. This output includes specific cooking instructions and ingredient lists. Data transmission to the terminal occurs over the network and is based on a communication protocol (e.g., HTTP).

[0700] Step 4:

[0701] The device visually presents the received recipe to the user. The outputted cooking instructions are displayed through the interface of an app built with React Native. They are displayed step-by-step for easy user understanding.

[0702] Step 5:

[0703] The device utilizes information display means to present users with videos and images based on cooking procedures. This involves data processing to retrieve relevant content using the Cooking API. The output consists of videos and images to provide visual support to the user at the appropriate time.

[0704] Step 6:

[0705] After cooking is complete, the user enters their feedback into the terminal. This user feedback is sent to the server as a new dataset. This feedback data is saved in text format.

[0706] Step 7:

[0707] The server updates the information storage location and improves the accuracy of the generated AI model based on the feedback data. This process analyzes the feedback and incorporates it into the AI ​​model's learning process. The output is an improvement in the model's accuracy in future recipe suggestions.

[0708] 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.

[0709] This invention combines a system that generates personalized cooking instructions tailored to the user's preferences and available ingredients, and provides visual assistance, with an emotion engine that recognizes the user's emotions. The system consists of three elements: a server, a terminal, and a user, each element responsible for a different function.

[0710] The server has the ability to generate personalized recipes for international cuisine using artificial intelligence based on data collected from users. First, the server receives information about the user's preferences and ingredients sent from the terminal and analyzes it. Using the results of this analysis, it selects an appropriate recipe from a database of dishes and further adjusts it to create cooking instructions optimized for the user.

[0711] The terminal plays the role of visually presenting cooking instructions sent from the server to the user. This involves not only displaying recipes but also presenting videos and images according to each step of the cooking process, making it easier for the user to visually understand the procedure. The terminal is also equipped with an emotion engine that can analyze the user's facial expressions and voice to recognize emotions. This emotion information is used to dynamically adjust the presentation of the cooking instructions.

[0712] Users connect to the server via their device and cook according to the provided recipes and cooking procedures. During cooking, their emotions are recognized through the device, and support is provided according to their psychological state at that time. Once cooking is complete, users input their experience and feedback into the device. This feedback and collected emotional data are sent to the server, and the system's overall database and artificial intelligence model are updated, improving the accuracy of future recipe suggestions.

[0713] For example, if a user requests a "light meal for a busy day" and enters the ingredients into the device, the server generates a suitable recipe. If the user shows signs of fatigue during cooking, the device displays advice prompting them to take breaks at each step, helping to smooth the cooking process. In this way, incorporating an emotion engine improves user satisfaction and makes the cooking experience more personal and comfortable.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The user enters their preferences and available ingredients into the device. The device receives this information and prepares to send the data to the server.

[0717] Step 2:

[0718] The server receives information about preferences and ingredients sent from the terminal. Based on this, the server uses artificial intelligence to generate a cooking recipe.

[0719] Step 3:

[0720] The server organizes the recipes it generates and sends the optimal cooking procedure to the user's terminal. This procedure is prepared as a detailed instruction manual.

[0721] Step 4:

[0722] The terminal displays the cooking instructions received from the server and provides visual support to make them easy for the user to understand. Videos or images illustrating each step of the cooking process are presented as needed.

[0723] Step 5:

[0724] The device uses its built-in emotion engine to analyze the user's emotions from their facial expressions and voice. The analysis results are used to understand the user's psychological state.

[0725] Step 6:

[0726] As the user proceeds with cooking, the device dynamically adjusts how the cooking instructions are presented based on recognized emotional information. For example, if the user is experiencing high stress levels, it provides relaxing visuals and sounds.

[0727] Step 7:

[0728] Once the user completes the cooking process, the device collects feedback from the user. This feedback may include comments on the cooking experience and suggestions for improvement.

[0729] Step 8:

[0730] The device sends collected feedback and emotional data to the server. The server analyzes this data and updates its database and artificial intelligence models to make future recipe suggestions more accurate.

[0731] (Example 2)

[0732] 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".

[0733] In modern life, there is a demand for personalized cooking instructions for individual users. However, providing cooking instructions that adapt to user preferences, available ingredients, and even emotional states is not easy. Conventional recipe systems struggle to provide dynamic support based on user emotions or to optimize future suggestions based on feedback from users' own cooking experiences. Therefore, a new system is needed to provide a better personalized cooking experience.

[0734] 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.

[0735] In this invention, the server includes intelligent processing means for generating cooking procedures based on the user's preferences and available ingredients, means for analyzing the user's emotions using emotion recognition means, and data processing means for receiving feedback from the user and updating the data set. This makes it possible to provide the user with personalized cooking procedures, provide appropriate support according to the user's emotional state, and optimize future suggestions based on the feedback.

[0736] "Intelligent processing means" refers to a function that utilizes artificial intelligence to generate appropriate cooking procedures based on the user's preferences and available ingredients.

[0737] The "presentation means" is a function that immediately provides the generated cooking procedure to the user, allowing the user to easily obtain cooking information.

[0738] The "emotion recognition means" is a function that analyzes the user's emotional state through their facial expressions and voice, and dynamically adjusts the support provided during cooking.

[0739] "Display means" refers to a function that provides visual instructions to the user, with the aim of clearly presenting cooking procedures in the form of images or videos.

[0740] The "data processing means" is a function that collects user feedback and updates the overall system data set to improve the accuracy of future cooking suggestions.

[0741] "Video" refers to a media format that uses images or a combination of images and audio to visually explain cooking procedures.

[0742] "Feedback" refers to evaluations and opinions provided by users based on their cooking experience, and is information used to improve the system in the future.

[0743] This invention is a system that enables users to obtain personalized cooking instructions based on their preferences and available ingredients. The system consists of three elements: a server, a terminal, and a user, each performing a different function to carry out the invention.

[0744] The server utilizes an artificial intelligence model as an intelligent processing tool. Ideally, this model should be a generative AI model excelling in natural language processing and data analysis. Specifically, the server receives information about the user's preferences and available ingredients, and selects the optimal recipe from a recipe database. During this process, it adjusts the recipe according to the user's requests, generating personalized cooking instructions. For example, for a user who prefers spicy food, the server might add a spicy twist.

[0745] The device functions as both a presentation tool and an emotion recognition tool. As a presentation tool, it visually provides the user with cooking instructions sent from the server, displaying each step clearly using images and videos. As an emotion recognition tool, its built-in camera and microphone analyze the user's facial expressions and voice to determine their emotions. If the user is having trouble or feeling fatigued during cooking, this information is recognized by the device, and appropriate assistance is provided.

[0746] Users connect to the server via their device and cook according to the generated cooking instructions. During cooking, user feedback and emotional data are collected by the device and sent to the server. The server then uses this feedback to update its database and AI model, improving the accuracy of suggestions for future users.

[0747] As a concrete example, if a user requests a "meal perfect for a quick lunch" and enters the ingredients into their device, the server will create a prompt to generate a suitable recipe. An example of a prompt message to the generating AI model might be: "The user wants a light meal for a busy day, and has chicken, lemon, and garlic on hand. Please generate an appropriate recipe based on this information."

[0748] This system allows users to enjoy a personalized cooking experience and receive support tailored to their emotional state at the time.

[0749] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0750] Step 1:

[0751] The user enters their cooking preferences and available ingredients into the terminal. The terminal collects the entered information as data and sends it to the server. This information includes specific data about the user's preferences and the types and quantities of ingredients used.

[0752] Step 2:

[0753] The server receives data from the terminal and analyzes it using a generative AI model, which is an intelligent processing tool. Specifically, it interprets the input information using natural language processing and searches for appropriate recipes in the database. In this process, the model selects recipes based on the entered keywords and customizes them to be optimal for the user. As a result, personalized cooking instructions are obtained and sent to the terminal.

[0754] Step 3:

[0755] The terminal receives personalized cooking instructions sent from the server and functions as a presentation tool. Specifically, it visually presents the cooking instructions to the user step by step using images and videos. This process allows the user to easily check the steps.

[0756] Step 4:

[0757] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice, functioning as a means of emotion recognition. If the user appears confused or tired, the device analyzes this data and decides whether to provide any additional explanations or advice. Based on this information, the device dynamically adjusts its display to support the user.

[0758] Step 5:

[0759] The user prepares the dish according to the provided cooking instructions. After cooking is complete, the user enters feedback into the device. This feedback includes the clarity of the cooking instructions, satisfaction with the recipe, and areas for improvement.

[0760] Step 6:

[0761] The device sends user feedback and emotional data collected during cooking to a server. The server uses this data to update its database and generative AI model through data processing. This update improves the accuracy of future recipe suggestions and provides a better, more personalized cooking experience.

[0762] (Application Example 2)

[0763] 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".

[0764] Conventional cooking support systems lacked personalized support tailored to individual user preferences and emotions, resulting in a lower quality cooking experience. In particular, they lacked mechanisms to alleviate emotional stress and fatigue during the cooking process, highlighting the need to improve user satisfaction.

[0765] 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.

[0766] In this invention, the server includes processing means using artificial intelligence to generate cooking procedures based on the user's preferences and available items, emotion analysis means for recognizing emotions and adjusting support during cooking, and data processing means for collecting user feedback and forming data to update the storage device. This enables highly personalized cooking support that responds to the user's individual needs and emotions.

[0767] A "user" is an individual who uses the system to engage in cooking activities and receives support tailored to their individual preferences and feelings.

[0768] "Preferences" refer to the personal tendencies of a user's preferred tastes, cooking methods, and ingredient choices.

[0769] "Items" refer to all items that the user possesses, including ingredients and cooking utensils that can be used for cooking.

[0770] "Cooking procedure" refers to the specific steps and processes that a user takes to perform a cooking task, and these constitute the overall flow of the cooking process.

[0771] "Artificial intelligence" is a computer technology that has the ability to generate cooking procedures based on the user's preferences and available items, and to adjust them appropriately.

[0772] "Emotion analysis means" refers to technology that analyzes the user's facial expressions and voice to determine the user's emotions, enabling appropriate adjustments to cooking assistance.

[0773] "Feedback" refers to information and data provided by users after using a system, based on suggestions for system improvements or additional information.

[0774] "Data formation methods" refer to processes and technologies used to update databases based on collected information and feedback, thereby improving the accuracy and efficiency of the system.

[0775] A "storage device" is a digital data storage device that holds collected data and generated information, and allows it to be retrieved as needed.

[0776] The system of this invention combines artificial intelligence and emotion analysis technology to improve the user's cooking experience. The server receives information about the user's preferences and available items, and generates cooking instructions based on this information. This process utilizes a generative AI model in the cloud. Specifically, the server analyzes data from the user, selects the optimal procedure from a multinational cuisine recipe database, and then individually adjusts it to generate a personalized recipe.

[0777] The terminal visually presents cooking instructions received from the server to the user. This includes displaying videos and still images for each step of the cooking process, allowing the user to easily follow along with the instructions. The terminal also incorporates emotion analysis capabilities, using a camera and microphone to detect the user's facial expressions and voice, and analyzing their emotions in real time. If the user is experiencing stress, appropriate breaks or encouraging messages will be displayed.

[0778] The specific hardware used includes humanoid robots and tablet devices, and software such as OpenCV and Google Cloud Speech-to-Text is used for emotion analysis. As an example, when a user enters "I want a fun cooking experience" into their device, the server provides a recipe corresponding to that request, and the device displays "You're doing great!" when it detects the user's positive emotions.

[0779] Examples of prompts for a generative AI model include: "Detect emotions from the user's facial expressions and voice, and suggest appropriate advice and breaks to ensure the cooking process proceeds smoothly."

[0780] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0781] Step 1:

[0782] The user inputs information about their preferences and available items into the terminal. The terminal then sends this information to the server. The input includes the types of ingredients and dishes selected by the user, and the output is data sent to the server.

[0783] Step 2:

[0784] The server analyzes the information received from the terminal and generates appropriate cooking instructions using a generative AI model. Here, user preference data is used as input, and personalized cooking instructions are generated as output. The specific generative AI model determines a multinational cuisine recipe that suits the user's preferences.

[0785] Step 3:

[0786] The server generates the cooking instructions and sends them to the terminal. The input is the generated cooking instructions, and the output is the transfer of information to the terminal.

[0787] Step 4:

[0788] The terminal visually presents the cooking instructions received from the server to the user. The terminal displays the cooking instructions step by step using videos and still images. This allows the user to understand the cooking instructions more intuitively through visual information.

[0789] Step 5:

[0790] The device analyzes the user's emotions in real time using its camera and microphone. Input includes the user's facial expressions and voice data, which are then used by the emotion analysis system to perform data calculations. The output is emotionally relevant feedback.

[0791] Step 6:

[0792] The device displays advice and messages tailored to the user's emotions based on the analysis results. For example, if the user shows a tired expression, it will display a message encouraging them to take a break. This makes the cooking experience more responsive to the user's psychological state.

[0793] Step 7:

[0794] After the user completes cooking, they enter feedback into the terminal. This includes evaluations of the cooking experience and the quality of the finished product. The feedback is processed by a data generation system and sent to the server, so it can be used to improve future suggestions.

[0795] Step 8:

[0796] The server receives feedback from users and updates the database. The input is feedback data, and the output is an adjusted cooking procedure for the next cooking session. This process improves the overall accuracy of the system and allows it to accommodate diverse user preferences.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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."

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] The following is further disclosed regarding the embodiments described above.

[0819] (Claim 1)

[0820] A processing method using artificial intelligence that generates cooking procedures based on user preferences and available ingredients,

[0821] A presentation means for presenting the generated cooking procedure to the user,

[0822] A display means that provides visual support to the user during cooking,

[0823] A data processing means that receives user feedback and updates the database,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, further comprising means for generating a video or images that provide a visual explanation based on the generated cooking procedure.

[0827] (Claim 3)

[0828] The system according to claim 1, further comprising means for analyzing user feedback and learning to optimize future recipe suggestions.

[0829] "Example 1"

[0830] (Claim 1)

[0831] An information processing means that analyzes user preferences and available ingredients, and creates cooking procedures using a generative AI model,

[0832] A presentation means for visually presenting the generated cooking procedure to the user,

[0833] A display means that provides videos or images as visual support when a user is cooking,

[0834] Information processing means for receiving user feedback and updating an information database,

[0835] A method for analyzing feedback and using it to train a generative AI model to optimize recipe suggestions,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, further comprising means for generating a video or images that provide a visual explanation based on the generated cooking procedure.

[0839] (Claim 3)

[0840] The system according to claim 1, further comprising a learning means for adaptively optimizing an information database based on user feedback.

[0841] "Application Example 1"

[0842] (Claim 1)

[0843] An information processing method using artificial intelligence that generates cooking procedures based on user preferences and available ingredients,

[0844] A display means for presenting the generated cooking procedure to the user,

[0845] Information display means that provides visual support to the user during cooking,

[0846] An information processing means that receives user feedback and updates the information storage location,

[0847] A method for suggesting the most suitable dishes based on user preference data,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, further comprising means for generating a video or images that provide a visual explanation based on the generated cooking procedure.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising means for analyzing user feedback and learning to optimize future cooking procedure suggestions.

[0853] "Example 2 of combining an emotion engine"

[0854] (Claim 1)

[0855] An intelligent processing means that generates cooking procedures based on the user's preferences and available ingredients,

[0856] A presentation means for providing the generated cooking procedure to the user,

[0857] An emotion recognition means that analyzes the user's facial expressions and voice to recognize emotions and adjust the display of cooking instructions,

[0858] A display means that provides visual assistance to the user during cooking,

[0859] A data processing means that receives user feedback and updates the data set,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, further comprising means for generating a video that provides a visual explanation based on the generated cooking procedure.

[0863] (Claim 3)

[0864] The system according to claim 1, further comprising means for analyzing user feedback and sentiment data to perform learning in order to optimize future cooking procedure suggestions.

[0865] "Application example 2 when combining with an emotional engine"

[0866] (Claim 1)

[0867] A processing method using artificial intelligence that generates cooking procedures based on the user's preferences and available items,

[0868] A presentation means for presenting the generated cooking procedure to the user,

[0869] A display means that provides visual support to the user during cooking tasks,

[0870] An emotion analysis tool that recognizes the user's emotions and adjusts support during cooking,

[0871] A data processing means that collects user feedback and updates the storage device using data formation means,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, further comprising a generating means for generating a video or still image that provides a visual explanation based on the generated cooking procedure.

[0875] (Claim 3)

[0876] The system according to claim 1, further comprising optimization means for performing machine learning to optimize future cooking suggestions by analyzing user feedback and emotional data. [Explanation of Symbols]

[0877] 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 processing method using artificial intelligence that generates cooking procedures based on user preferences and available ingredients, A presentation means for presenting the generated cooking procedure to the user, A display means that provides visual support to the user during cooking, A data processing means that receives user feedback and updates the database, A system that includes this.

2. The system according to claim 1, further comprising means for generating a video or images that provide a visual explanation based on the generated cooking procedure.

3. The system according to claim 1, further comprising means for analyzing user feedback and learning to optimize future recipe suggestions.

Images