system

Abstract

Provide a system. A means for a user to input an arbitrary theme; means for analyzing the input topic and extracting relevant features; a generating means for generating a recipe based on the extracted features; means for providing the generated recipe to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] Conventional search systems have the problem of making it difficult for users to find a variety of dressings and new cooking ideas. This forces users to cook with limited options, which does not meet the needs for new flavors and experiences. Furthermore, users often hesitate to try new dishes because they do not know the specific cooking methods. [Means for solving the problem]

[0005] The present invention provides a system that uses a generative AI to automatically generate recipes based on a theme entered by the user, and provides specific cooking methods. Specifically, the system includes a means for entering a theme from the user, a means for analyzing the entered theme and extracting related features, a generating means for generating a recipe based on the extracted features, and a means for providing the generated recipe to the user. This reduces the barriers for users to try new dishes and provides a diverse cooking experience.

[0006] "User" refers to an individual or group who uses this system to input a theme and search for new dishes or mixing methods.

[0007] A "theme" is any keyword or phrase entered by the user, and refers to the subject for which mixing methods or dishes are suggested.

[0008] "Input means" refers to an interface or mechanism for a user to communicate a theme to the system, such as a text box or a voice input device.

[0009] "Means for analysis" refers to the function of analyzing the input topic mechanically or using artificial intelligence to extract relevant features and elements.

[0010] "Means of extraction" refers to the function of extracting relevant features, elements, or keywords from the analyzed theme and preparing them to be sent to the generative AI.

[0011] "Generator" refers to an artificial intelligence or algorithm that generates a formula or cooking recipe based on the extracted features.

[0012] "Means for providing" refers to an interface or mechanism for displaying, notifying, or communicating the generated recipe or cooking method to the user.

[0013] The "system" refers to the overall mechanism that combines the above means to generate and provide new recipes for dishes based on user input. [Brief explanation of the drawings]

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

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

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

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

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

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

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

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0035] The present invention is a system that automatically generates new dishes and mixing methods based on any theme entered by the user and provides specific cooking procedures. The system includes an interface for the user to input the theme, a module for analyzing the theme, a mixing method generation module using generation AI, and an interface that provides the generated mixing methods to the user.

[0036] System Overview

[0037] 1. The user enters a theme

[0038] The user inputs a theme of their choice through the interface displayed on the device. This theme specifies the direction and image of the dish and includes specific keywords and phrases. For example, themes such as "Pokémon-style dressing" or "Jujutsu Kaisen-style ice cream" can be input.

[0039] 2. Thematic Analysis

[0040] The server receives the theme submitted by the user and passes it to an analysis module. This analysis module extracts keywords and features from the input theme and identifies related elements. For example, in the case of "Pokémon-style dressing," features such as colorful, playful designs and unique flavor variations are extracted.

[0041] 3. Generate a recipe

[0042] The server inputs the features extracted from the analysis module into the generation AI and instructs it to generate a recipe. Based on this, the generation AI generates a specific ingredient list and cooking instructions. For example, for a "Pokémon-style dressing," it generates a "bright yellow curry-flavored dressing made with curry powder, mustard, and honey."

[0043] 4. Providing mixing methods

[0044] The server sends the generated recipe to the terminal and provides it to the user. The terminal displays the recipe in an easy-to-understand format, guiding the user when actually cooking the dish. For example, the terminal may provide instructions for "mixing curry powder, mustard, and honey to create a dressing."

[0045] Specific examples

[0046] Below is a specific example of a user requesting "Jujutsu Kaisen-style ice cream."

[0047] Entering the theme

[0048] The user types "Jujutsu Kaisen-style ice cream" into the device, and the device sends this to the server.

[0049] Theme Analysis

[0050] The server analyzes the "Jujutsu Kaisen-style ice cream" and extracts its characteristics: "dark color," "luxury," and "surprising flavor."

[0051] Generate recipes

[0052] The server inputs the characteristics into the AI ​​generator and instructs it to generate a recipe for the ice cream. The AI ​​then generates a specific recipe for the ice cream using black sesame paste, sugar, cream, dry chocolate, and gold powder.

[0053] Providing mixing methods

[0054] The server sends the generated recipe to the terminal, which then provides the user with instructions such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on some spicy chocolate sauce and sprinkle with gold powder."

[0055] This allows users to try out new themes and create their own unique dishes and mixing methods, broadening the scope of their cooking experience.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] The device provides the user with an input interface, such as a text box or voice input option displayed on the screen.

[0059] Step 2:

[0060] The user inputs a theme into the terminal. For example, the user inputs the theme "Pokémon-style dressing."

[0061] Step 3:

[0062] The terminal transmits the input theme to the server in the form of data.

[0063] Step 4:

[0064] The server passes the received themes to the analysis module for analysis, which extracts themes' keywords and identifies related features.

[0065] Step 5:

[0066] The analysis module sends the extracted features back to the server. For example, in the case of "Pokémon-style dressing," it extracts bright colors, playfulness, and character traits.

[0067] Step 6:

[0068] The server prepares input data for the generative AI based on the extracted features, and converts it into a format for sending to the AI.

[0069] Step 7:

[0070] The server sends the feature data to the generation AI and instructs it to generate a mixing method. The generation AI generates a mixing method or recipe based on the input features.

[0071] Step 8:

[0072] The generative AI generates a specific list of ingredients, mixing methods, and cooking steps, and sends that data back to the server. For example, it might generate a recipe for "curry dressing made with curry powder, mustard, and honey."

[0073] Step 9:

[0074] The server formats the generated recipe data and converts it into a format that can be provided to the user.

[0075] Step 10:

[0076] The server then sends the formatted data to the terminal, including the generated mixing method and recipe.

[0077] Step 11:

[0078] The device displays the generated recipe to the user, specifically, the list of ingredients and cooking instructions on the screen, helping the user to create the dish by following the instructions.

[0079] Step 12:

[0080] Users can actually cook dishes and dressings according to the recipes provided, allowing them to enjoy a new cooking experience.

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] In today's world, there are an increasing number of cases where users desire new dishes and recipes based on a variety of themes, but there is no automated system to realize this.There is a need for a system that can automatically generate dishes and recipes based on a theme by allowing the user to input an arbitrary theme, and provide specific cooking instructions.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes means for inputting a theme of a user's choice, means for transmitting the input theme to the server, means for passing the theme data received by the server to an analysis module and extracting features from the theme, means for the server to input a prompt sentence to the generative AI model based on the extracted features and generate a recipe, means for the server to transmit the generated recipe data to the user's terminal, and means for the terminal to provide the user with the received recipe in an easy-to-understand format. This allows the user to easily acquire new dishes and recipes based on a desired theme and obtain specific guidance for actually cooking.

[0086] A "user" is someone who uses the system to input a theme of their choice and wishes to create new dishes or recipes.

[0087] A "theme" is a keyword or phrase that a user inputs into the system to express the direction or image of a dish or preparation method.

[0088] The "server" is a computer system that receives a theme input by a user, analyzes the theme, generates a compounding method, and provides the generated compounding method to the user.

[0089] "Terminal" means a device through which a user inputs a theme and receives the generated formula, including a mobile phone, tablet, personal computer, etc.

[0090] "Analysis Module" means a software component within the server for analyzing themes and extracting relevant features.

[0091] "Features" are elements or characteristics related to a theme extracted by the analysis module.

[0092] "Generative AI model" refers to an artificial intelligence model that the server uses to input a prompt statement and automatically generate a formula based on the characteristics of the statement.

[0093] A "prompt sentence" is a sentence that indicates the content that should be generated based on the input entered by the server when instructing the generative AI model to generate a formulation method.

[0094] A "recipe" is a recipe generated by a generative AI model, including a specific list of ingredients and cooking steps.

[0095] The system of the present invention automatically generates new dishes and recipes based on any theme entered by a user and provides specific cooking procedures. The system includes an interface for the user to enter the theme, a module for analyzing the theme, a recipe generation module using a generative AI model, and an interface for providing the generated recipes to the user.

[0096] First, the user inputs a theme using the device interface. For example, a theme such as "Jujutsu Kaisen-style ice cream" is input into the device. Then, the device transmits the input theme to the server.

[0097] The server receives the theme sent by the user and passes it to an analysis module. This analysis module extracts keywords and features from the input theme and identifies related elements. For example, in the case of "Jujutsu Kaisen-style ice cream," features such as "dark color," "luxury," and "surprising flavor" are extracted.

[0098] The server then inputs the extracted features from the analysis module into a generative AI model to generate a recipe. Based on this prompt, the generative AI model generates a specific ingredient list and cooking instructions. For example, for "Jujutsu Kaisen-style ice cream," it generates a "specific ice cream recipe using black sesame paste, sugar, cream, dry chocolate, and gold powder."

[0099] The server receives the recipe and sends it to the device, which then presents it to the user in an easy-to-understand format. For example, the device might display specific recipe steps such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour in a spicy chocolate sauce and sprinkle with gold powder."

[0100] This allows users to try out new themes and create their own unique dishes and mixing methods, broadening the scope of their cooking experience.

[0101] As a concrete example, consider the case where a user requests "Jujutsu Kaisen-style ice cream." The user enters "Jujutsu Kaisen-style ice cream" into their device, which then sends this to the server. The server analyzes "Jujutsu Kaisen-style ice cream" and extracts the characteristics "dark color," "luxury," and "surprising flavor." The server inputs these characteristics into a generative AI model and instructs it to generate a mixing method. The generative AI model generates a specific ice cream recipe using "black sesame paste, sugar, cream, dry chocolate, and gold powder." The server sends the generated recipe to the device, which then provides the user with instructions such as "mix the black sesame paste, sugar, and cream to make the ice cream, drizzle with dry chocolate sauce, and sprinkle with gold powder."

[0102] Here is an example of a prompt that can be used with this system:

[0103] Theme: Jujutsu Kaisen-style ice cream

[0104] Characteristics: Dark color, luxury, surprising flavor

[0105] Ingredients: black sesame paste, sugar, cream, dry chocolate, gold powder

[0106] Steps: Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder.

[0107] This allows users to easily acquire new dishes and recipes based on a desired theme, and obtain specific instructions for actually cooking.

[0108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0109] Step 1:

[0110] The user inputs a desired theme through the terminal interface.

[0111] Input: User-entered text for the theme (e.g., "Jujutsu Kaisen-style ice cream").

[0112] Output: The data format (text) of the input content is retained on the terminal.

[0113] Specific actions: Enter the theme using the keyboard in the input field displayed on the device screen and press the send button.

[0114] Step 2:

[0115] The terminal sends the user's input to the server.

[0116] Input: Theme text data stored on the device.

[0117] Output: The text data of the theme is sent to the server.

[0118] Specific operation: The device sends the entered theme data to the server using an HTTP POST request.

[0119] Step 3:

[0120] The server passes the received theme data to the analysis module.

[0121] Input: The text data of the theme received by the server.

[0122] Output: Data passed to the analysis module for thematic analysis.

[0123] Specific operation: The application inside the server passes the theme data to the API of the analysis module.

[0124] Step 4:

[0125] The analysis module extracts keywords and features from the themes.

[0126] Input: Thematic text data passed to the analysis module.

[0127] Output: Extracted feature data (e.g., "dark color," "luxury," "surprising flavor").

[0128] Specific operation: The analysis module uses natural language processing algorithms to analyze the text data of a topic and extract relevant features.

[0129] Step 5:

[0130] The server receives the extracted feature data from the analysis module.

[0131] Input: Feature data from the analysis module.

[0132] Output: The feature data is stored on the server.

[0133] Specific operation: The analysis module returns the extracted feature data in JSON format to the server, which then receives it and stores it in a database.

[0134] Step 6:

[0135] The server creates a prompt sentence to input into the generative AI model based on the feature data.

[0136] Input: Feature data (e.g., "dark color," "luxury," "surprising flavor").

[0137] Output: The generated prompt (e.g., "Theme: Jujutsu Kaisen-style ice cream, Characteristics: dark color, luxurious, surprising flavor").

[0138] Specific operation: The server references the feature data, generates a prompt sentence based on it, and stores it as character string data.

[0139] Step 7:

[0140] The server inputs a prompt statement into the generative AI model, instructing it to generate a formula.

[0141] Input: Generated prompt (e.g., "Theme: Jujutsu Kaisen-style ice cream, Characteristics: dark color, luxury, surprising flavor").

[0142] Output: Recipe data (ingredients list and cooking instructions) returned by the generative AI model.

[0143] Specific operation: The server inputs the prompt sentence into the API of the generative AI model and requests the generation of a formula. The generative AI model generates a formula based on the input and returns the result to the server.

[0144] Step 8:

[0145] The server receives the recipe generated from the generative AI model.

[0146] Input: Recipe data returned by the generative AI model (e.g., a list of ingredients (e.g., "black sesame paste, sugar, cream, dry chocolate, gold dust") and specific cooking instructions).

[0147] Output: The recipe data is stored on the server.

[0148] Specific operation: Receives formulation method data from the generative AI model and stores it in the server.

[0149] Step 9:

[0150] The server transmits the generated recipe data to the user's terminal.

[0151] Input: Retained recipe data.

[0152] Output: The recipe data is sent to the terminal.

[0153] Specific operation: The server sends the formulation method data to the user's terminal as an HTTP response.

[0154] Step 10:

[0155] The compounding method received by the terminal is provided to the user in an easy-to-understand format.

[0156] Input: Formulation data sent from the server.

[0157] Output: The recipe shown to the user (e.g., "Mix black sesame paste, sugar, and cream to make ice cream, then drizzle with hot chocolate sauce and sprinkle with gold dust").

[0158] Specific operation: The terminal displays the received formulation data on the screen so that the user can check it.

[0159] (Application example 1)

[0160] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] In recent years, food delivery services have been increasing in number, and they are required to meet the diverse needs of consumers. However, existing delivery services offer limited menus, making it difficult to provide customized dishes and drinks that users desire. Therefore, there is a need for a system that can automatically generate new dishes and mixing methods based on any theme entered by the user and provide them to users.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0163] In this invention, the server includes means for inputting a theme from a user, means for analyzing the input theme and extracting related features, means for generating a recipe based on the extracted features, means for providing the generated recipe to the user, and means for linking with an external system that provides customized food based on the generated recipe, thereby enabling the user to order customized food and drinks based on a theme through a delivery service.

[0164] "Means for users to input any theme" refers to devices or software that provide an interface that allows users to freely input themes and keywords.

[0165] "Means for analyzing the input topic and extracting relevant features" refers to algorithms or modules for analyzing the topic input by the user and extracting relevant keywords and features from it.

[0166] The "means for generating a recipe based on the extracted features" refers to an artificial intelligence model or algorithm for generating a new cooking method or recipe based on the extracted features.

[0167] The "means for providing the generated mixing method to the user" refers to a screen or application for displaying the generated cooking method or recipe in an easy-to-understand manner to the user.

[0168] "Means for linking with external systems that provide customized food based on the generated recipe" refers to protocols and APIs for linking with external food delivery services and cooking services in order to actually provide customized food based on the generated recipe.

[0169] "Generating recipes using artificial intelligence" refers to using artificial intelligence technologies such as machine learning and deep learning to generate new cooking methods and recipes based on freely entered themes.

[0170] "Means for extracting keywords from a topic and identifying related features" refers to natural language processing technology and text analysis tools that extract important keywords from the input topic and identify related features based on these.

[0171] Based on this invention, a specific embodiment for constructing a system that allows users to order customized food and drinks based on any theme through a delivery service will be described.

[0172] First, a user inputs a theme of their choice using the smartphone application "Custom Food." This input theme is then sent to the server. The server then uses an analysis module to analyze the input theme and extract related features. This analysis module uses natural language processing techniques and text analysis tools (e.g., NLTK and SpaCy) to extract keywords from the theme and identify related features.

[0173] The analyzed feature information is input into a generative AI model (for example, the GPT-3 (registered trademark) model) to generate new cooking methods and recipes. The generated recipes are sent from the server to the user's smartphone application and displayed to the user. The user can check the suggested recipes and, if they like them, can order them directly from the delivery service.

[0174] The system works with external food delivery services, and customized dishes and drinks are prepared based on the generated recipes and delivered to the user. The system uses RESTful APIs and JSON data exchange for communication.

[0175] As a concrete example, suppose the user enters the theme "Orange Drinks for Halloween Party." For this theme, the following prompt sentences are used:

[0176] User-submitted theme: Orange drink for Halloween party

[0177] Extracted keywords: Halloween, orange, party, drink

[0178] Generative AI prompt: Generate a new orange drink recipe based on these keywords.

[0179] Based on this prompt, the generative AI model generates a recipe, suggesting "mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish." This recipe is provided to the user, and if the user places an order, it is actually delivered via the delivery service.

[0180] In this way, a system is realized that allows users to receive customized food and drinks based on any theme through a delivery service.

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Step 1:

[0183] The user starts the smartphone application "Custom Food" and inputs a theme of their choice. Here, the theme is "Orange Drink for Halloween Party." The input theme is sent to the server as JSON format data.

[0184] Input: "Orange drink for Halloween party"

[0185] Output: JSON data sent to the server

[0186] Step 2:

[0187] The server passes the received theme data to the analysis module, which analyzes it and extracts theme keywords. Here, natural language processing techniques (e.g., NLTK or SpaCy) are used to identify the following keywords:

[0188] Input: "Orange drink for Halloween party"

[0189] Data processing: Text analysis

[0190] Output: Keywords "Halloween, orange, party, drink"

[0191] Step 3:

[0192] The server inputs the extracted keywords into a generative AI model (e.g., GPT-3) to create a prompt for formulating the recipe. The generative AI model then generates a new recipe based on the prompt.

[0193] Enter: Keywords "Halloween, orange, party, drink"

[0194] Data Computation: Prompt input to generative AI models

[0195] Output: New recipe: "Mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish."

[0196] Step 4:

[0197] The server sends the generated recipe as JSON format data to a smartphone application, where the user receives and confirms the new recipe.

[0198] Input: New recipe: "Mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish."

[0199] Output: Send JSON data to smartphone application

[0200] Step 5:

[0201] The user checks the provided recipes, and if they like them, they place an order with the delivery service directly through the application. This order information is sent to an external delivery system, and processing begins.

[0202] Input: New recipe order information

[0203] Output: Send order data to external delivery system

[0204] Step 6:

[0205] The external delivery system prepares customized food and drinks based on the received order information and delivers them to the user.

[0206] Input: Order data

[0207] Output: Delivery to user

[0208] As a result, through this system, users can receive customized food and drinks based on any theme through a delivery service.

[0209] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0210] The present invention is a system that automatically generates new dishes and recipes based on any theme and emotion input by a user, and provides specific cooking instructions. The system includes an interface for the user to input the theme and emotion, a module for analyzing the theme and emotion, a recipe generation module using generative AI, and an interface that provides the generated recipes to the user.

[0211] System Overview

[0212] 1. User inputs theme and emotion

[0213] The user inputs a theme and the emotion they want to reflect through the interface displayed on the device. The theme specifies the direction and image of the dish and includes specific keywords and phrases. The emotion represents the user's feelings. For example, "Pokémon-style dressing" and the emotion "fun" can be input.

[0214] 2. Emotion Analysis

[0215] The device uses an emotion engine to analyze emotions based on user input, facial expressions, voice, etc. This emotion data is sent to a server and integrated with thematic analysis.

[0216] 3. Thematic Analysis

[0217] The server receives the themes and emotions submitted by the user and passes them to an analysis module. This analysis module extracts keywords and features from the input themes and identifies characteristics that incorporate related emotional elements. For example, combining "Pokémon-style dressing" with the emotion "fun" can identify a colorful, playful, and energetic flavor.

[0218] 4. Generate a recipe

[0219] The server inputs the features extracted from the analysis module into the generation AI and instructs it to generate a recipe. Based on this, the generation AI generates a specific list of ingredients and cooking instructions. For example, based on "Pokémon-style dressing" and the emotion "fun," it generates "a bright yellow curry-flavored dressing made with curry powder, mustard, and honey."

[0220] 5. Providing mixing methods

[0221] The server sends the generated recipe to the terminal and provides it to the user. The terminal displays the recipe in an easy-to-understand format, guiding the user when actually cooking the dish. For example, the terminal may provide instructions for "mixing curry powder, mustard, and honey to create a dressing."

[0222] Specific examples

[0223] Below is a specific example where a user requests "Jujutsu Kaisen-style ice cream" and "surprise."

[0224] Entering themes and emotions

[0225] The user inputs the emotion "Jujutsu Kaisen-style ice cream" and "surprise" into the device, which then sends it to the server. The emotion engine analyzes facial expressions and voice to complete the emotion data.

[0226] Theme and sentiment analysis

[0227] The server analyzes the emotion of "Jujutsu Kaisen-style ice cream" and "surprise," and identifies the characteristics as "dark color," "luxury," and "surprising flavor."

[0228] Generate recipes

[0229] The server inputs the characteristics into the AI ​​generator and instructs it to generate a recipe for the ice cream. The AI ​​then generates a specific recipe for the ice cream using black sesame paste, sugar, cream, dry chocolate, and gold powder.

[0230] Providing mixing methods

[0231] The server sends the generated recipe to the terminal, which then provides the user with instructions such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on some spicy chocolate sauce and sprinkle with gold powder."

[0232] This allows users to try out new themes and emotions based dishes and preparations, broadening the scope of their cooking experience.

[0233] The processing flow will be explained below.

[0234] Step 1:

[0235] The user inputs the theme and emotion using the input interface displayed on the terminal, for example, "Pokémon-style dressing" and "fun."

[0236] Step 2:

[0237] The terminal transmits the input theme and emotion to the server, where the theme and emotion data is converted into an appropriate format and transmitted.

[0238] Step 3:

[0239] The server passes the received themes and emotions to an analysis module, which extracts thematic keywords and related emotional features.

[0240] Step 4:

[0241] The device acquires emotional data from the user's facial expressions and voice, analyzes it using an emotion engine, and then combines the analyzed emotional data with themes and sends it to the server.

[0242] Step 5:

[0243] The server receives the extracted features and emotion data from the analysis module and prepares it as input data for the generative AI. The input data format is then adjusted.

[0244] Step 6:

[0245] The server sends the prepared characteristic and emotion data to the generation AI, which then generates a specific ingredient list and cooking instructions.

[0246] Step 7:

[0247] The generative AI generates a specific list of ingredients, mixing methods, and cooking steps, and sends that data back to the server. For example, it might generate a recipe for "curry dressing made with curry powder, mustard, and honey."

[0248] Step 8:

[0249] The server formats the generated recipe data and converts it into a format that can be provided to the user.

[0250] Step 9:

[0251] The server sends the generated mixing method data to the terminal. The generated mixing method or recipe is sent in an appropriate format.

[0252] Step 10:

[0253] The device displays the generated recipe to the user, including a list of ingredients and cooking instructions.

[0254] Step 11:

[0255] Users can actually cook dishes and dressings according to the recipes provided, allowing them to enjoy a new cooking experience.

[0256] Example 2

[0257] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0258] Conventional recipe generation systems for cooking and mixing require users to specify specific recipes and ingredients, making it difficult to provide creative cooking and mixing methods that reflect the user's emotions or themes.There is a demand for a method to easily generate new cooking and mixing methods based on themes that reflect the user's emotions, thereby making the user's cooking experience richer and more enjoyable.

[0259] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0260] In this invention, the server includes a means for inputting a theme and emotion from the user, a means for analyzing the input theme and emotion and extracting related features, a generation AI means for generating a recipe based on the extracted features, and a means for providing the generated recipe to the user. This makes it possible to automatically generate new and creative dishes and recipes based on the theme and emotion input by the user.

[0261] A "user" is an entity that uses the system to input a theme and emotion and create a new dish or recipe.

[0262] "Theme" refers to keywords or phrases related to the direction or image of the dish entered by the user.

[0263] "Emotion" is input data for expressing the user's feelings or emotions.

[0264] "Means" refers to the mechanisms or methods used to achieve a particular function.

[0265] An "emotion analysis engine" is a software module that analyzes user input, facial expressions, voice, etc. to identify emotions.

[0266] The "theme analysis module" is a software module that extracts keywords and features from the input theme and integrates related emotional elements to identify characteristics.

[0267] "Generative AI" is a model that uses artificial intelligence technology to generate new dishes and recipes based on identified themes and emotions.

[0268] "Formulation" refers to the specific ingredient list and cooking steps for creating a dish or drink.

[0269] A "server" is a computer system that receives input from a user, performs analysis, and provides the generated formula.

[0270] A "terminal" is a device (such as a smartphone, tablet, or PC) that allows a user to input a theme and emotion, and receive and display the generated recipe.

[0271] The present invention is a system that automatically generates new dishes and recipes based on any theme and emotion input by a user, and provides specific cooking instructions. The system includes an interface for the user to input the theme and emotion, a module for analyzing the theme and emotion, a recipe generation module using generative AI, and an interface that provides the generated recipes to the user.

[0272] The system is programmed using the following hardware and software:

[0273] Hardware: User devices (smartphones, tablets, PCs), servers

[0274] Software: Sentiment analysis engine, theme analysis module, generative AI, user interface

[0275] The system operates as follows:

[0276] 1. User inputs theme and emotion:

[0277] Users input their desired theme and emotion through the device interface, such as "Pokémon-style dressing" or "fun" into the input form displayed on the app screen of their smartphone or tablet, and then press the send button.

[0278] 2. Emotion Analysis:

[0279] The device passes the user's input data, voice, facial expressions, etc. to the emotion analysis engine for analysis. For example, the device's camera captures the user's facial expression and identifies the emotion based on the image data. Text data entered by the user is also analyzed, and the analysis results are sent to the server.

[0280] 3. Thematic Analysis:

[0281] The server receives the theme and emotion data sent by the user and sends it to the theme analysis module. The theme analysis module extracts keywords and features from the input theme and integrates them with the emotion analysis results. Specifically, from the data "Pokémon-style dressing" and "fun," it identifies characteristics such as "colorful," "playful," and "encouraging."

[0282] 4. Generate formulation:

[0283] The server inputs the extracted features from the analysis module into the generative AI, which then generates a specific recipe, including a list of ingredients and cooking instructions. For example, it creates a recipe for a bright yellow curry-flavored dressing made with curry powder, mustard, and honey.

[0284] 5. Providing formulation methods:

[0285] The server sends the generated recipe to the device and provides it to the user. The device displays the recipe on the screen, allowing the user to follow the instructions to cook the dish. For example, the smartphone app screen might show instructions for mixing curry powder, mustard, and honey to create a dressing.

[0286] Specific examples

[0287] If a user requests "Jujutsu Kaisen-style ice cream" and "surprise":

[0288] 1. User inputs theme and emotion:

[0289] The user enters "Jujutsu Kaisen-style ice cream" and "surprise" into the device and sends it.

[0290] 2. Emotion Analysis:

[0291] The device uses an emotion analysis engine to analyze the input content and supplementary data and transmits them to the server.

[0292] 3. Thematic and emotional analysis:

[0293] The server receives this and passes it to an analysis module, which identifies characteristics such as "dark color," "luxury," and "surprising flavor."

[0294] 4. Generate formulation:

[0295] The server suggests ingredients to the AI ​​to use, such as black sesame paste, sugar, cream, dry chocolate, and gold powder, and generates a specific recipe.

[0296] 5. Providing formulation methods:

[0297] The server sends the generated recipe to the device, which then displays instructions to the user, such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder."

[0298] This allows users to easily generate their own dishes and recipes based on new themes and emotions by entering prompts such as "Theme: Jujutsu Kaisen-style ice cream; Emotion: Surprise" or "Theme: Harry Potter-style dessert; Emotion: Magic."

[0299] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0300] Step 1:

[0301] User inputs theme and sentiment

[0302] The user inputs a theme and emotion using the device interface. For example, they input the words "Pokémon-style dressing" and "fun" into the smartphone app screen and press the send button.

[0303] The input theme and emotion data (e.g., "Pokémon-style dressing" and "fun") are received by the device, and the device proceeds to the next step.

[0304] Step 2:

[0305] Perform sentiment analysis

[0306] Based on the theme and emotion data received by the device, emotions are analyzed using an emotion analysis engine.

[0307] Specifically, the device's camera captures the user's facial expression, and the emotion analysis engine analyzes the image data. The input text data is also processed by the emotion analysis engine. The analysis results (e.g., cheerfulness, degree of enjoyment) are displayed on the device and sent to the server.

[0308] Step 3:

[0309] Conduct a theme analysis

[0310] The server receives the theme and emotion data sent from the terminal and passes it to the theme analysis module.

[0311] The theme analysis module extracts key keywords and characteristics from the input theme and combines them with the sentiment analysis results. For example, from the data of "Pokémon-style dressing" and "fun," it identifies the characteristics of colorful and energetic. This characteristic data is then input into the generative AI.

[0312] Step 4:

[0313] Generate a recipe

[0314] The server inputs the characteristic data sent from the theme analysis module into the generation AI and instructs it to generate a specific synthesis method.

[0315] Based on the characteristic data, the generative AI generates a specific list of ingredients and cooking instructions. For example, it might generate a recipe for a bright yellow curry-flavored dressing made with curry powder, mustard, and honey. This generated recipe data is then returned to the server.

[0316] Step 5:

[0317] Providing users with compounding methods

[0318] The server transmits the generated formulation method data to the user's terminal.

[0319] The device then displays the received recipe on a user interface. For example, a recipe for mixing curry powder, mustard, and honey to create a dressing is displayed on the smartphone screen. This allows the user to follow the displayed recipe to prepare the dish.

[0320] Specific examples

[0321] If the user enters "Jujutsu Kaisen style ice cream" and "surprise", the processing steps are:

[0322] Step 1:

[0323] User inputs theme and sentiment

[0324] The user enters "Jujutsu Kaisen-style ice cream" and "surprise" into the device's interface and presses the send button.

[0325] Step 2:

[0326] Perform sentiment analysis

[0327] The device passes the input theme and emotion to the emotion analysis engine for analysis. After facial expression capture and text data analysis, the emotion analysis results are sent to the server (e.g., surprise, elation).

[0328] Step 3:

[0329] Conduct a theme analysis

[0330] The server passes the received theme and emotion data to the theme analysis module, which extracts keywords and characteristics. Specific analysis results, such as "dark color," "luxury," and "surprising flavor," are identified and sent to the generation AI.

[0331] Step 4:

[0332] Generate a recipe

[0333] The server inputs the analysis results into the AI ​​generator and instructs it to generate a specific recipe. The AI ​​generator generates a recipe using black sesame paste, sugar, cream, dry chocolate, and gold powder and sends it back to the server.

[0334] Step 5:

[0335] Providing users with compounding methods

[0336] The server sends the generated recipe to the terminal, which then displays instructions to the user, such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder."

[0337] This allows users to create and cook their own unique dishes and recipes based on new themes and emotions.

[0338] (Application example 2)

[0339] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0340] Conventional systems have struggled to automatically generate personalized recipes based on a user's preferences and emotions and instantly deliver meals based on those recipes. Therefore, there is a need for a system that allows users to easily enjoy meals that match their specific emotions or themes. There is also a need for technology that can link the generation of recipes using AI with the actual delivery of ingredients.

[0341] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0342] In this invention, the server includes means for inputting a theme and emotion from a user, means for analyzing the input theme and emotion and extracting related features, means for generating a recipe based on the extracted features, means for providing the generated recipe to the user, and means for supplying and delivering items based on the generated recipe. This enables automatic generation of personalized dishes according to the user's emotion or theme, and actual delivery of the dishes based on the dishes.

[0343] A "theme" is a keyword or phrase that indicates the direction or image of the dish specified by the user.

[0344] "Emotions" are data that indicate the feelings and emotional states of the user, and are analyzed together with themes.

[0345] "Parsing" is the process of extracting relevant features from the input themes and sentiments.

[0346] "Features" are thematic and emotional characteristics or elements extracted through analysis.

[0347] "Generator" is a module that uses artificial intelligence and algorithms to generate a recipe based on the analyzed characteristics.

[0348] "Recipe" refers to a specific ingredient list and cooking instructions that are automatically generated by the generating means.

[0349] The "means for providing" is an interface for displaying or communicating the generated recipe to the user.

[0350] "Product supply" is the process of providing actual ingredients and dishes to the user based on the created recipe.

[0351] "Delivery" refers to the act of transporting the supplied item to a location designated by the user.

[0352] This invention is a system that automatically generates new recipes based on any theme and emotion entered by a user and provides specific cooking procedures. To this end, the system includes the following components and processes:

[0353] Components:

[0354] 1. User devices: Includes smartphones, tablets, etc.

[0355] 2. Emotion Analysis Module: Includes software for analyzing emotions, such as Face API.

[0356] 3. Backend server: A server that uses the Django framework to perform analysis and generation.

[0357] 4. Generative AI model: Use generative AI such as GPT-4 (registered trademark).

[0358] 5. Delivery system: Use the API of partner delivery services (e.g. Uber Eats API).

[0359] 6. Database: Includes databases such as PostgreSQL.

[0360] Explanation of program operation:

[0361] In this system, users first input their desired theme and emotion on the interface using a smartphone or tablet. The theme and emotion data is collected from the device. The emotion analysis module uses Face API to scan the user's emotions in real time and determine the primary emotion.

[0362] These inputs are then sent to a backend server, which uses the Django framework to parse thematic and sentiment keywords and extract relevant features, which are then used as prompts for a generative AI model (GPT-4).

[0363] GPT-4 generates a specific recipe based on the prompts, including a list of ingredients and specific cooking steps. The generated recipe is sent from the server to the user's device for review and selection.

[0364] The food is prepared at a partner restaurant based on the recipe selected by the user, and then delivered to the user using the Uber Eats API. This entire process is designed to provide users with a personalized cooking experience.

[0365] Example explanation:

[0366] For example, if a user inputs the theme and emotion "Christmas dinner" and "happiness," this data will be analyzed by the emotion analysis module and sent to the backend server. The server will use the analysis module to extract the features of "Christmas dinner" and "happiness," and send the following prompt to GPT-4: "Generate a new food recipe based on theme: Christmas dinner, emotion: happiness."

[0367] The recipe generated by GPT-4 specifically includes the ingredients "roast chicken, cranberry sauce, and mashed potatoes," and cooking instructions are generated based on those ingredients. This recipe is provided to the user, and after the user confirms it, the food is prepared at a partner restaurant and delivered via the Uber Eats API.

[0368] This system allows users to enjoy cooking that is optimized to their own theme and emotion.

[0369] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0370] Step 1:

[0371] The user inputs a theme and emotion of their choice into the device. The user inputs a theme (e.g., "Christmas dinner") and emotion (e.g., "happy") through the interface on their smartphone or tablet. This becomes the initial input for generating user input data.

[0372] Step 2:

[0373] The device uses an emotion analysis module to scan the user's emotions and determine the primary emotion. Using Face API or similar, it analyzes emotions from the user's facial expressions and obtains complementary emotion data. The user's facial expression data is given as input, and the analyzed emotion data is obtained as output.

[0374] Step 3:

[0375] The terminal sends theme and emotion data to the backend server. The input is the theme and emotion data entered by the user, which is sent to the backend server via a RESTful API using Django. The output is the data received by the server.

[0376] Step 4:

[0377] The server analyzes the received themes and emotions and extracts related features. Using the Django framework, the server analyzes themes and emotions for keywords and extracts related features (e.g., for "Christmas dinner," it extracts "celebratory," "warm," and "special"). The input is themes and emotions, and the output is feature data.

[0378] Step 5:

[0379] The server inputs the extracted features as a prompt to the generative AI model (GPT-4) to generate a recipe. The server generates a prompt, "Generate a new cooking recipe based on theme: Christmas dinner, emotion: happiness," and sends it to GPT-4. The input is the prompt, and the output is the generated recipe data.

[0380] Step 6:

[0381] The server provides the generated recipe to the user. The server sends the generated recipe data (e.g., "roast chicken, cranberry sauce, and mashed potatoes") to the terminal. The input is the generated recipe data, and the output is the display data on the user terminal.

[0382] Step 7:

[0383] The user checks and selects the provided recipe. The user checks the provided recipe through the terminal interface and selects a specific dish. The input is the provided recipe data, and the output is the selected recipe data.

[0384] Step 8:

[0385] The server instructs the partner restaurant to prepare the dish based on the selected recipe. Based on the selected recipe data, the server notifies the partner restaurant of the necessary ingredients and cooking procedures. The input is the selected recipe data, and the output is cooking instruction data for the partner restaurant.

[0386] Step 9:

[0387] The partner restaurant prepares the food based on the recipe. The partner restaurant prepares the food based on the recipe data provided by the server. The input is cooking instruction data, and the output is the actual food.

[0388] Step 10:

[0389] The cooked food is delivered to the user using a delivery service. The server uses the Uber Eats API to deliver the food to the user's specified location. The input is delivery instruction data, and the output is the arrival of the food at the user's location.

[0390] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0391] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0392] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0393] [Second embodiment]

[0394] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0395] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0396] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0397] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0398] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0399] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0400] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0401] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0402] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0403] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0404] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0405] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0406] The present invention is a system that automatically generates new dishes and mixing methods based on any theme entered by the user and provides specific cooking procedures. The system includes an interface for the user to input the theme, a module for analyzing the theme, a mixing method generation module using generation AI, and an interface that provides the generated mixing methods to the user.

[0407] System Overview

[0408] 1. The user enters a theme

[0409] The user inputs a theme of their choice through the interface displayed on the device. This theme specifies the direction and image of the dish and includes specific keywords and phrases. For example, themes such as "Pokémon-style dressing" or "Jujutsu Kaisen-style ice cream" can be input.

[0410] 2. Thematic Analysis

[0411] The server receives the theme submitted by the user and passes it to an analysis module. This analysis module extracts keywords and features from the input theme and identifies related elements. For example, in the case of "Pokémon-style dressing," features such as colorful, playful designs and unique flavor variations are extracted.

[0412] 3. Generate a recipe

[0413] The server inputs the features extracted from the analysis module into the generation AI and instructs it to generate a recipe. Based on this, the generation AI generates a specific ingredient list and cooking instructions. For example, for a "Pokémon-style dressing," it generates a "bright yellow curry-flavored dressing made with curry powder, mustard, and honey."

[0414] 4. Providing mixing methods

[0415] The server sends the generated recipe to the terminal and provides it to the user. The terminal displays the recipe in an easy-to-understand format, guiding the user when actually cooking the dish. For example, the terminal may provide instructions for "mixing curry powder, mustard, and honey to create a dressing."

[0416] Specific examples

[0417] Below is a specific example of a user requesting "Jujutsu Kaisen-style ice cream."

[0418] Entering the theme

[0419] The user types "Jujutsu Kaisen-style ice cream" into the device, and the device sends this to the server.

[0420] Theme Analysis

[0421] The server analyzes the "Jujutsu Kaisen-style ice cream" and extracts its characteristics: "dark color," "luxury," and "surprising flavor."

[0422] Generate recipes

[0423] The server inputs the characteristics into the AI ​​generator and instructs it to generate a recipe for the ice cream. The AI ​​then generates a specific recipe for the ice cream using black sesame paste, sugar, cream, dry chocolate, and gold powder.

[0424] Providing mixing methods

[0425] The server sends the generated recipe to the terminal, which then provides the user with instructions such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on some spicy chocolate sauce and sprinkle with gold powder."

[0426] This allows users to try out new themes and create their own unique dishes and mixing methods, broadening the scope of their cooking experience.

[0427] The processing flow will be explained below.

[0428] Step 1:

[0429] The device provides the user with an input interface, such as a text box or voice input option displayed on the screen.

[0430] Step 2:

[0431] The user inputs a theme into the terminal. For example, the user inputs the theme "Pokémon-style dressing."

[0432] Step 3:

[0433] The terminal transmits the input theme to the server in the form of data.

[0434] Step 4:

[0435] The server passes the received themes to the analysis module for analysis, which extracts themes' keywords and identifies related features.

[0436] Step 5:

[0437] The analysis module sends the extracted features back to the server. For example, in the case of "Pokémon-style dressing," it extracts bright colors, playfulness, and character traits.

[0438] Step 6:

[0439] The server prepares input data for the generative AI based on the extracted features, and converts it into a format for sending to the AI.

[0440] Step 7:

[0441] The server sends the feature data to the generation AI and instructs it to generate a mixing method. The generation AI generates a mixing method or recipe based on the input features.

[0442] Step 8:

[0443] The generative AI generates a specific list of ingredients, mixing methods, and cooking steps, and sends that data back to the server. For example, it might generate a recipe for "curry dressing made with curry powder, mustard, and honey."

[0444] Step 9:

[0445] The server formats the generated recipe data and converts it into a format that can be provided to the user.

[0446] Step 10:

[0447] The server then sends the formatted data to the terminal, including the generated mixing method and recipe.

[0448] Step 11:

[0449] The device displays the generated recipe to the user, specifically, the list of ingredients and cooking instructions on the screen, helping the user to create the dish by following the instructions.

[0450] Step 12:

[0451] Users can actually cook dishes and dressings according to the recipes provided, allowing them to enjoy a new cooking experience.

[0452] Example 1

[0453] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0454] In today's world, there are an increasing number of cases where users desire new dishes and recipes based on a variety of themes, but there is no automated system to realize this.There is a need for a system that can automatically generate dishes and recipes based on a theme by allowing the user to input an arbitrary theme, and provide specific cooking instructions.

[0455] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0456] In this invention, the server includes means for inputting a theme of a user's choice, means for transmitting the input theme to the server, means for passing the theme data received by the server to an analysis module and extracting features from the theme, means for the server to input a prompt sentence to the generative AI model based on the extracted features and generate a recipe, means for the server to transmit the generated recipe data to the user's terminal, and means for the terminal to provide the user with the received recipe in an easy-to-understand format. This allows the user to easily acquire new dishes and recipes based on a desired theme and obtain specific guidance for actually cooking.

[0457] A "user" is someone who uses the system to input a theme of their choice and wishes to create new dishes or recipes.

[0458] A "theme" is a keyword or phrase that a user inputs into the system to express the direction or image of a dish or preparation method.

[0459] The "server" is a computer system that receives a theme input by a user, analyzes the theme, generates a compounding method, and provides the generated compounding method to the user.

[0460] "Terminal" means a device through which a user inputs a theme and receives the generated formula, including a mobile phone, tablet, personal computer, etc.

[0461] "Analysis Module" means a software component within the server for analyzing themes and extracting relevant features.

[0462] "Features" are elements or characteristics related to a theme extracted by the analysis module.

[0463] "Generative AI model" refers to an artificial intelligence model that the server uses to input a prompt statement and automatically generate a formula based on the characteristics of the statement.

[0464] A "prompt sentence" is a sentence that indicates the content that should be generated based on the input entered by the server when instructing the generative AI model to generate a formulation method.

[0465] A "recipe" is a recipe generated by a generative AI model, including a specific list of ingredients and cooking steps.

[0466] The system of the present invention automatically generates new dishes and recipes based on any theme entered by a user and provides specific cooking procedures. The system includes an interface for the user to enter the theme, a module for analyzing the theme, a recipe generation module using a generative AI model, and an interface for providing the generated recipes to the user.

[0467] First, the user inputs a theme using the device interface. For example, a theme such as "Jujutsu Kaisen-style ice cream" is input into the device. Then, the device transmits the input theme to the server.

[0468] The server receives the theme sent by the user and passes it to an analysis module. This analysis module extracts keywords and features from the input theme and identifies related elements. For example, in the case of "Jujutsu Kaisen-style ice cream," features such as "dark color," "luxury," and "surprising flavor" are extracted.

[0469] The server then inputs the extracted features from the analysis module into a generative AI model to generate a recipe. Based on this prompt, the generative AI model generates a specific ingredient list and cooking instructions. For example, for "Jujutsu Kaisen-style ice cream," it generates a "specific ice cream recipe using black sesame paste, sugar, cream, dry chocolate, and gold powder."

[0470] The server receives the recipe and sends it to the device, which then presents it to the user in an easy-to-understand format. For example, the device might display specific recipe steps such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour in a spicy chocolate sauce and sprinkle with gold powder."

[0471] This allows users to try out new themes and create their own unique dishes and mixing methods, broadening the scope of their cooking experience.

[0472] As a concrete example, consider the case where a user requests "Jujutsu Kaisen-style ice cream." The user enters "Jujutsu Kaisen-style ice cream" into their device, which then sends this to the server. The server analyzes "Jujutsu Kaisen-style ice cream" and extracts the characteristics "dark color," "luxury," and "surprising flavor." The server inputs these characteristics into a generative AI model and instructs it to generate a mixing method. The generative AI model generates a specific ice cream recipe using "black sesame paste, sugar, cream, dry chocolate, and gold powder." The server sends the generated recipe to the device, which then provides the user with instructions such as "mix the black sesame paste, sugar, and cream to make the ice cream, drizzle with dry chocolate sauce, and sprinkle with gold powder."

[0473] Here is an example of a prompt that can be used with this system:

[0474] Theme: Jujutsu Kaisen-style ice cream

[0475] Characteristics: Dark color, luxury, surprising flavor

[0476] Ingredients: black sesame paste, sugar, cream, dry chocolate, gold powder

[0477] Steps: Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder.

[0478] This allows users to easily acquire new dishes and recipes based on a desired theme, and obtain specific instructions for actually cooking.

[0479] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0480] Step 1:

[0481] The user inputs a desired theme through the terminal interface.

[0482] Input: User-entered text for the theme (e.g., "Jujutsu Kaisen-style ice cream").

[0483] Output: The data format (text) of the input content is retained on the terminal.

[0484] Specific actions: Enter the theme using the keyboard in the input field displayed on the device screen and press the send button.

[0485] Step 2:

[0486] The terminal sends the user's input to the server.

[0487] Input: Theme text data stored on the device.

[0488] Output: The text data of the theme is sent to the server.

[0489] Specific operation: The device sends the entered theme data to the server using an HTTP POST request.

[0490] Step 3:

[0491] The server passes the received theme data to the analysis module.

[0492] Input: The text data of the theme received by the server.

[0493] Output: Data passed to the analysis module for thematic analysis.

[0494] Specific operation: The application inside the server passes the theme data to the API of the analysis module.

[0495] Step 4:

[0496] The analysis module extracts keywords and features from the themes.

[0497] Input: Thematic text data passed to the analysis module.

[0498] Output: Extracted feature data (e.g., "dark color," "luxury," "surprising flavor").

[0499] Specific operation: The analysis module uses natural language processing algorithms to analyze the text data of a topic and extract relevant features.

[0500] Step 5:

[0501] The server receives the extracted feature data from the analysis module.

[0502] Input: Feature data from the analysis module.

[0503] Output: The feature data is stored on the server.

[0504] Specific operation: The analysis module returns the extracted feature data in JSON format to the server, which then receives it and stores it in a database.

[0505] Step 6:

[0506] The server creates a prompt sentence to input into the generative AI model based on the feature data.

[0507] Input: Feature data (e.g., "dark color," "luxury," "surprising flavor").

[0508] Output: The generated prompt (e.g., "Theme: Jujutsu Kaisen-style ice cream, Characteristics: dark color, luxurious, surprising flavor").

[0509] Specific operation: The server references the feature data, generates a prompt sentence based on it, and stores it as character string data.

[0510] Step 7:

[0511] The server inputs a prompt statement into the generative AI model, instructing it to generate a formula.

[0512] Input: Generated prompt (e.g., "Theme: Jujutsu Kaisen-style ice cream, Characteristics: dark color, luxury, surprising flavor").

[0513] Output: Recipe data (ingredients list and cooking instructions) returned by the generative AI model.

[0514] Specific operation: The server inputs the prompt sentence into the API of the generative AI model and requests the generation of a formula. The generative AI model generates a formula based on the input and returns the result to the server.

[0515] Step 8:

[0516] The server receives the recipe generated from the generative AI model.

[0517] Input: Recipe data returned by the generative AI model (e.g., a list of ingredients (e.g., "black sesame paste, sugar, cream, dry chocolate, gold dust") and specific cooking instructions).

[0518] Output: The recipe data is stored on the server.

[0519] Specific operation: Receives formulation method data from the generative AI model and stores it in the server.

[0520] Step 9:

[0521] The server transmits the generated recipe data to the user's terminal.

[0522] Input: Retained recipe data.

[0523] Output: The recipe data is sent to the terminal.

[0524] Specific operation: The server sends the formulation method data to the user's terminal as an HTTP response.

[0525] Step 10:

[0526] The compounding method received by the terminal is provided to the user in an easy-to-understand format.

[0527] Input: Formulation data sent from the server.

[0528] Output: The recipe shown to the user (e.g., "Mix black sesame paste, sugar, and cream to make ice cream, then drizzle with hot chocolate sauce and sprinkle with gold dust").

[0529] Specific operation: The terminal displays the received formulation data on the screen so that the user can check it.

[0530] (Application example 1)

[0531] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0532] In recent years, food delivery services have been increasing in number, and they are required to meet the diverse needs of consumers. However, existing delivery services offer limited menus, making it difficult to provide customized dishes and drinks that users desire. Therefore, there is a need for a system that can automatically generate new dishes and mixing methods based on any theme entered by the user and provide them to users.

[0533] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0534] In this invention, the server includes means for inputting a theme from a user, means for analyzing the input theme and extracting related features, means for generating a recipe based on the extracted features, means for providing the generated recipe to the user, and means for linking with an external system that provides customized food based on the generated recipe, thereby enabling the user to order customized food and drinks based on a theme through a delivery service.

[0535] "Means for users to input any theme" refers to devices or software that provide an interface that allows users to freely input themes and keywords.

[0536] "Means for analyzing the input topic and extracting relevant features" refers to algorithms or modules for analyzing the topic input by the user and extracting relevant keywords and features from it.

[0537] The "means for generating a recipe based on the extracted features" refers to an artificial intelligence model or algorithm for generating a new cooking method or recipe based on the extracted features.

[0538] The "means for providing the generated mixing method to the user" refers to a screen or application for displaying the generated cooking method or recipe in an easy-to-understand manner to the user.

[0539] "Means for linking with external systems that provide customized food based on the generated recipe" refers to protocols and APIs for linking with external food delivery services and cooking services in order to actually provide customized food based on the generated recipe.

[0540] "Generating recipes using artificial intelligence" refers to using artificial intelligence technologies such as machine learning and deep learning to generate new cooking methods and recipes based on freely entered themes.

[0541] "Means for extracting keywords from a topic and identifying related features" refers to natural language processing technology and text analysis tools that extract important keywords from the input topic and identify related features based on these.

[0542] Based on this invention, a specific embodiment for constructing a system that allows users to order customized food and drinks based on any theme through a delivery service will be described.

[0543] First, a user inputs a theme of their choice using the smartphone application "Custom Food." This input theme is then sent to the server. The server then uses an analysis module to analyze the input theme and extract related features. This analysis module uses natural language processing techniques and text analysis tools (e.g., NLTK and SpaCy) to extract keywords from the theme and identify related features.

[0544] The analyzed feature information is input into a generative AI model (for example, the GPT-3 model) to generate new cooking methods and recipes. The generated recipes are sent from the server to the user's smartphone application and displayed to the user. The user can check the suggested recipes and, if they like them, can order them directly from the delivery service.

[0545] The system works with external food delivery services, and customized dishes and drinks are prepared based on the generated recipes and delivered to the user. The system uses RESTful APIs and JSON data exchange for communication.

[0546] As a concrete example, suppose the user enters the theme "Orange Drinks for Halloween Party." For this theme, the following prompt sentences are used:

[0547] User-submitted theme: Orange drink for Halloween party

[0548] Extracted keywords: Halloween, orange, party, drink

[0549] Generative AI prompt: Generate a new orange drink recipe based on these keywords.

[0550] Based on this prompt, the generative AI model generates a recipe, suggesting "mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish." This recipe is provided to the user, and if the user places an order, it is actually delivered via the delivery service.

[0551] In this way, a system is realized that allows users to receive customized food and drinks based on any theme through a delivery service.

[0552] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0553] Step 1:

[0554] The user starts the smartphone application "Custom Food" and inputs a theme of their choice. Here, the theme is "Orange Drink for Halloween Party." The input theme is sent to the server as JSON format data.

[0555] Input: "Orange drink for Halloween party"

[0556] Output: JSON data sent to the server

[0557] Step 2:

[0558] The server passes the received theme data to the analysis module, which analyzes it and extracts theme keywords. Here, natural language processing techniques (e.g., NLTK or SpaCy) are used to identify the following keywords:

[0559] Input: "Orange drink for Halloween party"

[0560] Data processing: Text analysis

[0561] Output: Keywords "Halloween, orange, party, drink"

[0562] Step 3:

[0563] The server inputs the extracted keywords into a generative AI model (e.g., GPT-3) to create a prompt for formulating the recipe. The generative AI model then generates a new recipe based on the prompt.

[0564] Enter: Keywords "Halloween, orange, party, drink"

[0565] Data Computation: Prompt input to generative AI models

[0566] Output: New recipe: "Mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish."

[0567] Step 4:

[0568] The server sends the generated recipe as JSON format data to a smartphone application, where the user receives and confirms the new recipe.

[0569] Input: New recipe: "Mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish."

[0570] Output: Send JSON data to smartphone application

[0571] Step 5:

[0572] The user checks the provided recipes, and if they like them, they place an order with the delivery service directly through the application. This order information is sent to an external delivery system, and processing begins.

[0573] Input: New recipe order information

[0574] Output: Send order data to external delivery system

[0575] Step 6:

[0576] The external delivery system prepares customized food and drinks based on the received order information and delivers them to the user.

[0577] Input: Order data

[0578] Output: Delivery to user

[0579] As a result, through this system, users can receive customized food and drinks based on any theme through a delivery service.

[0580] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0581] The present invention is a system that automatically generates new dishes and recipes based on any theme and emotion input by a user, and provides specific cooking instructions. The system includes an interface for the user to input the theme and emotion, a module for analyzing the theme and emotion, a recipe generation module using generative AI, and an interface that provides the generated recipes to the user.

[0582] System Overview

[0583] 1. User inputs theme and emotion

[0584] The user inputs a theme and the emotion they want to reflect through the interface displayed on the device. The theme specifies the direction and image of the dish and includes specific keywords and phrases. The emotion represents the user's feelings. For example, "Pokémon-style dressing" and the emotion "fun" can be input.

[0585] 2. Emotion Analysis

[0586] The device uses an emotion engine to analyze emotions based on user input, facial expressions, voice, etc. This emotion data is sent to a server and integrated with thematic analysis.

[0587] 3. Thematic Analysis

[0588] The server receives the themes and emotions submitted by the user and passes them to an analysis module. This analysis module extracts keywords and features from the input themes and identifies characteristics that incorporate related emotional elements. For example, combining "Pokémon-style dressing" with the emotion "fun" can identify a colorful, playful, and energetic flavor.

[0589] 4. Generate a recipe

[0590] The server inputs the features extracted from the analysis module into the generation AI and instructs it to generate a recipe. Based on this, the generation AI generates a specific list of ingredients and cooking instructions. For example, based on "Pokémon-style dressing" and the emotion "fun," it generates "a bright yellow curry-flavored dressing made with curry powder, mustard, and honey."

[0591] 5. Providing mixing methods

[0592] The server sends the generated recipe to the terminal and provides it to the user. The terminal displays the recipe in an easy-to-understand format, guiding the user when actually cooking the dish. For example, the terminal may provide instructions for "mixing curry powder, mustard, and honey to create a dressing."

[0593] Specific examples

[0594] Below is a specific example where a user requests "Jujutsu Kaisen-style ice cream" and "surprise."

[0595] Entering themes and emotions

[0596] The user inputs the emotion "Jujutsu Kaisen-style ice cream" and "surprise" into the device, which then sends it to the server. The emotion engine analyzes facial expressions and voice to complete the emotion data.

[0597] Theme and sentiment analysis

[0598] The server analyzes the emotion of "Jujutsu Kaisen-style ice cream" and "surprise," and identifies the characteristics as "dark color," "luxury," and "surprising flavor."

[0599] Generate recipes

[0600] The server inputs the characteristics into the AI ​​generator and instructs it to generate a recipe for the ice cream. The AI ​​then generates a specific recipe for the ice cream using black sesame paste, sugar, cream, dry chocolate, and gold powder.

[0601] Providing mixing methods

[0602] The server sends the generated recipe to the terminal, which then provides the user with instructions such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on some spicy chocolate sauce and sprinkle with gold powder."

[0603] This allows users to try out new themes and emotions based dishes and preparations, broadening the scope of their cooking experience.

[0604] The processing flow will be explained below.

[0605] Step 1:

[0606] The user inputs the theme and emotion using the input interface displayed on the terminal, for example, "Pokémon-style dressing" and "fun."

[0607] Step 2:

[0608] The terminal transmits the input theme and emotion to the server, where the theme and emotion data is converted into an appropriate format and transmitted.

[0609] Step 3:

[0610] The server passes the received themes and emotions to an analysis module, which extracts thematic keywords and related emotional features.

[0611] Step 4:

[0612] The device acquires emotional data from the user's facial expressions and voice, analyzes it using an emotion engine, and then combines the analyzed emotional data with themes and sends it to the server.

[0613] Step 5:

[0614] The server receives the extracted features and emotion data from the analysis module and prepares it as input data for the generative AI. The input data format is then adjusted.

[0615] Step 6:

[0616] The server sends the prepared characteristic and emotion data to the generation AI, which then generates a specific ingredient list and cooking instructions.

[0617] Step 7:

[0618] The generative AI generates a specific list of ingredients, mixing methods, and cooking steps, and sends that data back to the server. For example, it might generate a recipe for "curry dressing made with curry powder, mustard, and honey."

[0619] Step 8:

[0620] The server formats the generated recipe data and converts it into a format that can be provided to the user.

[0621] Step 9:

[0622] The server sends the generated mixing method data to the terminal. The generated mixing method or recipe is sent in an appropriate format.

[0623] Step 10:

[0624] The device displays the generated recipe to the user, including a list of ingredients and cooking instructions.

[0625] Step 11:

[0626] Users can actually cook dishes and dressings according to the recipes provided, allowing them to enjoy a new cooking experience.

[0627] Example 2

[0628] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0629] Conventional recipe generation systems for cooking and mixing require users to specify specific recipes and ingredients, making it difficult to provide creative cooking and mixing methods that reflect the user's emotions or themes.There is a demand for a method to easily generate new cooking and mixing methods based on themes that reflect the user's emotions, thereby making the user's cooking experience richer and more enjoyable.

[0630] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0631] In this invention, the server includes a means for inputting a theme and emotion from the user, a means for analyzing the input theme and emotion and extracting related features, a generation AI means for generating a recipe based on the extracted features, and a means for providing the generated recipe to the user. This makes it possible to automatically generate new and creative dishes and recipes based on the theme and emotion input by the user.

[0632] A "user" is an entity that uses the system to input a theme and emotion and create a new dish or recipe.

[0633] "Theme" refers to keywords or phrases related to the direction or image of the dish entered by the user.

[0634] "Emotion" is input data for expressing the user's feelings or emotions.

[0635] "Means" refers to the mechanisms or methods used to achieve a particular function.

[0636] An "emotion analysis engine" is a software module that analyzes user input, facial expressions, voice, etc. to identify emotions.

[0637] The "theme analysis module" is a software module that extracts keywords and features from the input theme and integrates related emotional elements to identify characteristics.

[0638] "Generative AI" is a model that uses artificial intelligence technology to generate new dishes and recipes based on identified themes and emotions.

[0639] "Formulation" refers to the specific ingredient list and cooking steps for creating a dish or drink.

[0640] A "server" is a computer system that receives input from a user, performs analysis, and provides the generated formula.

[0641] A "terminal" is a device (such as a smartphone, tablet, or PC) that allows a user to input a theme and emotion, and receive and display the generated recipe.

[0642] The present invention is a system that automatically generates new dishes and recipes based on any theme and emotion input by a user, and provides specific cooking instructions. The system includes an interface for the user to input the theme and emotion, a module for analyzing the theme and emotion, a recipe generation module using generative AI, and an interface that provides the generated recipes to the user.

[0643] The system is programmed using the following hardware and software:

[0644] Hardware: User devices (smartphones, tablets, PCs), servers

[0645] Software: Sentiment analysis engine, theme analysis module, generative AI, user interface

[0646] The system operates as follows:

[0647] 1. User inputs theme and emotion:

[0648] Users input their desired theme and emotion through the device interface, such as "Pokémon-style dressing" or "fun" into the input form displayed on the app screen of their smartphone or tablet, and then press the send button.

[0649] 2. Emotion Analysis:

[0650] The device passes the user's input data, voice, facial expressions, etc. to the emotion analysis engine for analysis. For example, the device's camera captures the user's facial expression and identifies the emotion based on the image data. Text data entered by the user is also analyzed, and the analysis results are sent to the server.

[0651] 3. Thematic Analysis:

[0652] The server receives the theme and emotion data sent by the user and sends it to the theme analysis module. The theme analysis module extracts keywords and features from the input theme and integrates them with the emotion analysis results. Specifically, from the data "Pokémon-style dressing" and "fun," it identifies characteristics such as "colorful," "playful," and "encouraging."

[0653] 4. Generate formulation:

[0654] The server inputs the extracted features from the analysis module into the generative AI, which then generates a specific recipe, including a list of ingredients and cooking instructions. For example, it creates a recipe for a bright yellow curry-flavored dressing made with curry powder, mustard, and honey.

[0655] 5. Providing formulation methods:

[0656] The server sends the generated recipe to the device and provides it to the user. The device displays the recipe on the screen, allowing the user to follow the instructions to cook the dish. For example, the smartphone app screen might show instructions for mixing curry powder, mustard, and honey to create a dressing.

[0657] Specific examples

[0658] If a user requests "Jujutsu Kaisen-style ice cream" and "surprise":

[0659] 1. User inputs theme and emotion:

[0660] The user enters "Jujutsu Kaisen-style ice cream" and "surprise" into the device and sends it.

[0661] 2. Emotion Analysis:

[0662] The device uses an emotion analysis engine to analyze the input content and supplementary data and transmits them to the server.

[0663] 3. Thematic and emotional analysis:

[0664] The server receives this and passes it to an analysis module, which identifies characteristics such as "dark color," "luxury," and "surprising flavor."

[0665] 4. Generate formulation:

[0666] The server suggests ingredients to the AI ​​to use, such as black sesame paste, sugar, cream, dry chocolate, and gold powder, and generates a specific recipe.

[0667] 5. Providing formulation methods:

[0668] The server sends the generated recipe to the device, which then displays instructions to the user, such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder."

[0669] This allows users to easily generate their own dishes and recipes based on new themes and emotions by entering prompts such as "Theme: Jujutsu Kaisen-style ice cream; Emotion: Surprise" or "Theme: Harry Potter-style dessert; Emotion: Magic."

[0670] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0671] Step 1:

[0672] User inputs theme and sentiment

[0673] The user inputs a theme and emotion using the device interface. For example, they input the words "Pokémon-style dressing" and "fun" into the smartphone app screen and press the send button.

[0674] The input theme and emotion data (e.g., "Pokémon-style dressing" and "fun") are received by the device, and the device proceeds to the next step.

[0675] Step 2:

[0676] Perform sentiment analysis

[0677] Based on the theme and emotion data received by the device, emotions are analyzed using an emotion analysis engine.

[0678] Specifically, the device's camera captures the user's facial expression, and the emotion analysis engine analyzes the image data. The input text data is also processed by the emotion analysis engine. The analysis results (e.g., cheerfulness, degree of enjoyment) are displayed on the device and sent to the server.

[0679] Step 3:

[0680] Conduct a theme analysis

[0681] The server receives the theme and emotion data sent from the terminal and passes it to the theme analysis module.

[0682] The theme analysis module extracts key keywords and characteristics from the input theme and combines them with the sentiment analysis results. For example, from the data of "Pokémon-style dressing" and "fun," it identifies the characteristics of colorful and energetic. This characteristic data is then input into the generative AI.

[0683] Step 4:

[0684] Generate a recipe

[0685] The server inputs the characteristic data sent from the theme analysis module into the generation AI and instructs it to generate a specific synthesis method.

[0686] Based on the characteristic data, the generative AI generates a specific list of ingredients and cooking instructions. For example, it might generate a recipe for a bright yellow curry-flavored dressing made with curry powder, mustard, and honey. This generated recipe data is then returned to the server.

[0687] Step 5:

[0688] Providing users with compounding methods

[0689] The server transmits the generated formulation method data to the user's terminal.

[0690] The device then displays the received recipe on a user interface. For example, a recipe for mixing curry powder, mustard, and honey to create a dressing is displayed on the smartphone screen. This allows the user to follow the displayed recipe to prepare the dish.

[0691] Specific examples

[0692] If the user enters "Jujutsu Kaisen style ice cream" and "surprise", the processing steps are:

[0693] Step 1:

[0694] User inputs theme and sentiment

[0695] The user enters "Jujutsu Kaisen-style ice cream" and "surprise" into the device's interface and presses the send button.

[0696] Step 2:

[0697] Perform sentiment analysis

[0698] The device passes the input theme and emotion to the emotion analysis engine for analysis. After facial expression capture and text data analysis, the emotion analysis results are sent to the server (e.g., surprise, elation).

[0699] Step 3:

[0700] Conduct a theme analysis

[0701] The server passes the received theme and emotion data to the theme analysis module, which extracts keywords and characteristics. Specific analysis results, such as "dark color," "luxury," and "surprising flavor," are identified and sent to the generation AI.

[0702] Step 4:

[0703] Generate a recipe

[0704] The server inputs the analysis results into the AI ​​generator and instructs it to generate a specific recipe. The AI ​​generator generates a recipe using black sesame paste, sugar, cream, dry chocolate, and gold powder and sends it back to the server.

[0705] Step 5:

[0706] Providing users with compounding methods

[0707] The server sends the generated recipe to the terminal, which then displays instructions to the user, such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder."

[0708] This allows users to create and cook their own unique dishes and recipes based on new themes and emotions.

[0709] (Application example 2)

[0710] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0711] Conventional systems have struggled to automatically generate personalized recipes based on a user's preferences and emotions and instantly deliver meals based on those recipes. Therefore, there is a need for a system that allows users to easily enjoy meals that match their specific emotions or themes. There is also a need for technology that can link the generation of recipes using AI with the actual delivery of ingredients.

[0712] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0713] In this invention, the server includes means for inputting a theme and emotion from a user, means for analyzing the input theme and emotion and extracting related features, means for generating a recipe based on the extracted features, means for providing the generated recipe to the user, and means for supplying and delivering items based on the generated recipe. This enables automatic generation of personalized dishes according to the user's emotion or theme, and actual delivery of the dishes based on the dishes.

[0714] A "theme" is a keyword or phrase that indicates the direction or image of the dish specified by the user.

[0715] "Emotions" are data that indicate the feelings and emotional states of the user, and are analyzed together with themes.

[0716] "Parsing" is the process of extracting relevant features from the input themes and sentiments.

[0717] "Features" are thematic and emotional characteristics or elements extracted through analysis.

[0718] "Generator" is a module that uses artificial intelligence and algorithms to generate a recipe based on the analyzed characteristics.

[0719] "Recipe" refers to a specific ingredient list and cooking instructions that are automatically generated by the generating means.

[0720] The "means for providing" is an interface for displaying or communicating the generated recipe to the user.

[0721] "Product supply" is the process of providing actual ingredients and dishes to the user based on the created recipe.

[0722] "Delivery" refers to the act of transporting the supplied item to a location designated by the user.

[0723] This invention is a system that automatically generates new recipes based on any theme and emotion entered by a user and provides specific cooking procedures. To this end, the system includes the following components and processes:

[0724] Components:

[0725] 1. User devices: Includes smartphones, tablets, etc.

[0726] 2. Emotion Analysis Module: Includes software for analyzing emotions, such as Face API.

[0727] 3. Backend server: A server that uses the Django framework to perform analysis and generation.

[0728] 4. Generative AI models: Use generative AI such as GPT-4.

[0729] 5. Delivery system: Use the API of partner delivery services (e.g. Uber Eats API).

[0730] 6. Database: Includes databases such as PostgreSQL.

[0731] Explanation of program operation:

[0732] In this system, users first input their desired theme and emotion on the interface using a smartphone or tablet. The theme and emotion data is collected from the device. The emotion analysis module uses Face API to scan the user's emotions in real time and determine the primary emotion.

[0733] These inputs are then sent to a backend server, which uses the Django framework to parse thematic and sentiment keywords and extract relevant features, which are then used as prompts for a generative AI model (GPT-4).

[0734] GPT-4 generates a specific recipe based on the prompts, including a list of ingredients and specific cooking steps. The generated recipe is sent from the server to the user's device for review and selection.

[0735] The food is prepared at a partner restaurant based on the recipe selected by the user, and then delivered to the user using the Uber Eats API. This entire process is designed to provide users with a personalized cooking experience.

[0736] Example explanation:

[0737] For example, if a user inputs the theme and emotion "Christmas dinner" and "happiness," this data will be analyzed by the emotion analysis module and sent to the backend server. The server will use the analysis module to extract the features of "Christmas dinner" and "happiness," and send the following prompt to GPT-4: "Generate a new food recipe based on theme: Christmas dinner, emotion: happiness."

[0738] The recipe generated by GPT-4 specifically includes the ingredients "roast chicken, cranberry sauce, and mashed potatoes," and cooking instructions are generated based on those ingredients. This recipe is provided to the user, and after the user confirms it, the food is prepared at a partner restaurant and delivered via the Uber Eats API.

[0739] This system allows users to enjoy cooking that is optimized to their own theme and emotion.

[0740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0741] Step 1:

[0742] The user inputs a theme and emotion of their choice into the device. The user inputs a theme (e.g., "Christmas dinner") and emotion (e.g., "happy") through the interface on their smartphone or tablet. This becomes the initial input for generating user input data.

[0743] Step 2:

[0744] The device uses an emotion analysis module to scan the user's emotions and determine the primary emotion. Using Face API or similar, it analyzes emotions from the user's facial expressions and obtains complementary emotion data. The user's facial expression data is given as input, and the analyzed emotion data is obtained as output.

[0745] Step 3:

[0746] The terminal sends theme and emotion data to the backend server. The input is the theme and emotion data entered by the user, which is sent to the backend server via a RESTful API using Django. The output is the data received by the server.

[0747] Step 4:

[0748] The server analyzes the received themes and emotions and extracts related features. Using the Django framework, the server analyzes themes and emotions for keywords and extracts related features (e.g., for "Christmas dinner," it extracts "celebratory," "warm," and "special"). The input is themes and emotions, and the output is feature data.

[0749] Step 5:

[0750] The server inputs the extracted features as a prompt to the generative AI model (GPT-4) to generate a recipe. The server generates a prompt, "Generate a new cooking recipe based on theme: Christmas dinner, emotion: happiness," and sends it to GPT-4. The input is the prompt, and the output is the generated recipe data.

[0751] Step 6:

[0752] The server provides the generated recipe to the user. The server sends the generated recipe data (e.g., "roast chicken, cranberry sauce, and mashed potatoes") to the terminal. The input is the generated recipe data, and the output is the display data on the user terminal.

[0753] Step 7:

[0754] The user checks and selects the provided recipe. The user checks the provided recipe through the terminal interface and selects a specific dish. The input is the provided recipe data, and the output is the selected recipe data.

[0755] Step 8:

[0756] The server instructs the partner restaurant to prepare the dish based on the selected recipe. Based on the selected recipe data, the server notifies the partner restaurant of the necessary ingredients and cooking procedures. The input is the selected recipe data, and the output is cooking instruction data for the partner restaurant.

[0757] Step 9:

[0758] The partner restaurant prepares the food based on the recipe. The partner restaurant prepares the food based on the recipe data provided by the server. The input is cooking instruction data, and the output is the actual food.

[0759] Step 10:

[0760] The cooked food is delivered to the user using a delivery service. The server uses the Uber Eats API to deliver the food to the user's specified location. The input is delivery instruction data, and the output is the arrival of the food at the user's location.

[0761] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0762] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0763] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0764] [Third embodiment]

[0765] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0766] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0767] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0768] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0769] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0770] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0771] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0772] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0773] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0774] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0775] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0776] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0777] The present invention is a system that automatically generates new dishes and mixing methods based on any theme entered by the user and provides specific cooking procedures. The system includes an interface for the user to input the theme, a module for analyzing the theme, a mixing method generation module using generation AI, and an interface that provides the generated mixing methods to the user.

[0778] System Overview

[0779] 1. The user enters a theme

[0780] The user inputs a theme of their choice through the interface displayed on the device. This theme specifies the direction and image of the dish and includes specific keywords and phrases. For example, themes such as "Pokémon-style dressing" or "Jujutsu Kaisen-style ice cream" can be input.

[0781] 2. Thematic Analysis

[0782] The server receives the theme submitted by the user and passes it to an analysis module. This analysis module extracts keywords and features from the input theme and identifies related elements. For example, in the case of "Pokémon-style dressing," features such as colorful, playful designs and unique flavor variations are extracted.

[0783] 3. Generate a recipe

[0784] The server inputs the features extracted from the analysis module into the generation AI and instructs it to generate a recipe. Based on this, the generation AI generates a specific ingredient list and cooking instructions. For example, for a "Pokémon-style dressing," it generates a "bright yellow curry-flavored dressing made with curry powder, mustard, and honey."

[0785] 4. Providing mixing methods

[0786] The server sends the generated recipe to the terminal and provides it to the user. The terminal displays the recipe in an easy-to-understand format, guiding the user when actually cooking the dish. For example, the terminal may provide instructions for "mixing curry powder, mustard, and honey to create a dressing."

[0787] Specific examples

[0788] Below is a specific example of a user requesting "Jujutsu Kaisen-style ice cream."

[0789] Entering the theme

[0790] The user types "Jujutsu Kaisen-style ice cream" into the device, and the device sends this to the server.

[0791] Theme Analysis

[0792] The server analyzes the "Jujutsu Kaisen-style ice cream" and extracts its characteristics: "dark color," "luxury," and "surprising flavor."

[0793] Generate recipes

[0794] The server inputs the characteristics into the AI ​​generator and instructs it to generate a recipe for the ice cream. The AI ​​then generates a specific recipe for the ice cream using black sesame paste, sugar, cream, dry chocolate, and gold powder.

[0795] Providing mixing methods

[0796] The server sends the generated recipe to the terminal, which then provides the user with instructions such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on some spicy chocolate sauce and sprinkle with gold powder."

[0797] This allows users to try out new themes and create their own unique dishes and mixing methods, broadening the scope of their cooking experience.

[0798] The processing flow will be explained below.

[0799] Step 1:

[0800] The device provides the user with an input interface, such as a text box or voice input option displayed on the screen.

[0801] Step 2:

[0802] The user inputs a theme into the terminal. For example, the user inputs the theme "Pokémon-style dressing."

[0803] Step 3:

[0804] The terminal transmits the input theme to the server in the form of data.

[0805] Step 4:

[0806] The server passes the received themes to the analysis module for analysis, which extracts themes' keywords and identifies related features.

[0807] Step 5:

[0808] The analysis module sends the extracted features back to the server. For example, in the case of "Pokémon-style dressing," it extracts bright colors, playfulness, and character traits.

[0809] Step 6:

[0810] The server prepares input data for the generative AI based on the extracted features, and converts it into a format for sending to the AI.

[0811] Step 7:

[0812] The server sends the feature data to the generation AI and instructs it to generate a mixing method. The generation AI generates a mixing method or recipe based on the input features.

[0813] Step 8:

[0814] The generative AI generates a specific list of ingredients, mixing methods, and cooking steps, and sends that data back to the server. For example, it might generate a recipe for "curry dressing made with curry powder, mustard, and honey."

[0815] Step 9:

[0816] The server formats the generated recipe data and converts it into a format that can be provided to the user.

[0817] Step 10:

[0818] The server then sends the formatted data to the terminal, including the generated mixing method and recipe.

[0819] Step 11:

[0820] The device displays the generated recipe to the user, specifically, the list of ingredients and cooking instructions on the screen, helping the user to create the dish by following the instructions.

[0821] Step 12:

[0822] Users can actually cook dishes and dressings according to the recipes provided, allowing them to enjoy a new cooking experience.

[0823] Example 1

[0824] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0825] In today's world, there are an increasing number of cases where users desire new dishes and recipes based on a variety of themes, but there is no automated system to realize this.There is a need for a system that can automatically generate dishes and recipes based on a theme by allowing the user to input an arbitrary theme, and provide specific cooking instructions.

[0826] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0827] In this invention, the server includes means for inputting a theme of a user's choice, means for transmitting the input theme to the server, means for passing the theme data received by the server to an analysis module and extracting features from the theme, means for the server to input a prompt sentence to the generative AI model based on the extracted features and generate a recipe, means for the server to transmit the generated recipe data to the user's terminal, and means for the terminal to provide the user with the received recipe in an easy-to-understand format. This allows the user to easily acquire new dishes and recipes based on a desired theme and obtain specific guidance for actually cooking.

[0828] A "user" is someone who uses the system to input a theme of their choice and wishes to create new dishes or recipes.

[0829] A "theme" is a keyword or phrase that a user inputs into the system to express the direction or image of a dish or preparation method.

[0830] The "server" is a computer system that receives a theme input by a user, analyzes the theme, generates a compounding method, and provides the generated compounding method to the user.

[0831] "Terminal" means a device through which a user inputs a theme and receives the generated formula, including a mobile phone, tablet, personal computer, etc.

[0832] "Analysis Module" means a software component within the server for analyzing themes and extracting relevant features.

[0833] "Features" are elements or characteristics related to a theme extracted by the analysis module.

[0834] "Generative AI model" refers to an artificial intelligence model that the server uses to input a prompt statement and automatically generate a formula based on the characteristics of the statement.

[0835] A "prompt sentence" is a sentence that indicates the content that should be generated based on the input entered by the server when instructing the generative AI model to generate a formulation method.

[0836] A "recipe" is a recipe generated by a generative AI model, including a specific list of ingredients and cooking steps.

[0837] The system of the present invention automatically generates new dishes and recipes based on any theme entered by a user and provides specific cooking procedures. The system includes an interface for the user to enter the theme, a module for analyzing the theme, a recipe generation module using a generative AI model, and an interface for providing the generated recipes to the user.

[0838] First, the user inputs a theme using the device interface. For example, a theme such as "Jujutsu Kaisen-style ice cream" is input into the device. Then, the device transmits the input theme to the server.

[0839] The server receives the theme sent by the user and passes it to an analysis module. This analysis module extracts keywords and features from the input theme and identifies related elements. For example, in the case of "Jujutsu Kaisen-style ice cream," features such as "dark color," "luxury," and "surprising flavor" are extracted.

[0840] The server then inputs the extracted features from the analysis module into a generative AI model to generate a recipe. Based on this prompt, the generative AI model generates a specific ingredient list and cooking instructions. For example, for "Jujutsu Kaisen-style ice cream," it generates a "specific ice cream recipe using black sesame paste, sugar, cream, dry chocolate, and gold powder."

[0841] The server receives the recipe and sends it to the device, which then presents it to the user in an easy-to-understand format. For example, the device might display specific recipe steps such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour in a spicy chocolate sauce and sprinkle with gold powder."

[0842] This allows users to try out new themes and create their own unique dishes and mixing methods, broadening the scope of their cooking experience.

[0843] As a concrete example, consider the case where a user requests "Jujutsu Kaisen-style ice cream." The user enters "Jujutsu Kaisen-style ice cream" into their device, which then sends this to the server. The server analyzes "Jujutsu Kaisen-style ice cream" and extracts the characteristics "dark color," "luxury," and "surprising flavor." The server inputs these characteristics into a generative AI model and instructs it to generate a mixing method. The generative AI model generates a specific ice cream recipe using "black sesame paste, sugar, cream, dry chocolate, and gold powder." The server sends the generated recipe to the device, which then provides the user with instructions such as "mix the black sesame paste, sugar, and cream to make the ice cream, drizzle with dry chocolate sauce, and sprinkle with gold powder."

[0844] Here is an example of a prompt that can be used with this system:

[0845] Theme: Jujutsu Kaisen-style ice cream

[0846] Characteristics: Dark color, luxury, surprising flavor

[0847] Ingredients: black sesame paste, sugar, cream, dry chocolate, gold powder

[0848] Steps: Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder.

[0849] This allows users to easily acquire new dishes and recipes based on a desired theme, and obtain specific instructions for actually cooking.

[0850] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0851] Step 1:

[0852] The user inputs a desired theme through the terminal interface.

[0853] Input: User-entered text for the theme (e.g., "Jujutsu Kaisen-style ice cream").

[0854] Output: The data format (text) of the input content is retained on the terminal.

[0855] Specific actions: Enter the theme using the keyboard in the input field displayed on the device screen and press the send button.

[0856] Step 2:

[0857] The terminal sends the user's input to the server.

[0858] Input: Theme text data stored on the device.

[0859] Output: The text data of the theme is sent to the server.

[0860] Specific operation: The device sends the entered theme data to the server using an HTTP POST request.

[0861] Step 3:

[0862] The server passes the received theme data to the analysis module.

[0863] Input: The text data of the theme received by the server.

[0864] Output: Data passed to the analysis module for thematic analysis.

[0865] Specific operation: The application inside the server passes the theme data to the API of the analysis module.

[0866] Step 4:

[0867] The analysis module extracts keywords and features from the themes.

[0868] Input: Thematic text data passed to the analysis module.

[0869] Output: Extracted feature data (e.g., "dark color," "luxury," "surprising flavor").

[0870] Specific operation: The analysis module uses natural language processing algorithms to analyze the text data of a topic and extract relevant features.

[0871] Step 5:

[0872] The server receives the extracted feature data from the analysis module.

[0873] Input: Feature data from the analysis module.

[0874] Output: The feature data is stored on the server.

[0875] Specific operation: The analysis module returns the extracted feature data in JSON format to the server, which then receives it and stores it in a database.

[0876] Step 6:

[0877] The server creates a prompt sentence to input into the generative AI model based on the feature data.

[0878] Input: Feature data (e.g., "dark color," "luxury," "surprising flavor").

[0879] Output: The generated prompt (e.g., "Theme: Jujutsu Kaisen-style ice cream, Characteristics: dark color, luxurious, surprising flavor").

[0880] Specific operation: The server references the feature data, generates a prompt sentence based on it, and stores it as character string data.

[0881] Step 7:

[0882] The server inputs a prompt statement into the generative AI model, instructing it to generate a formula.

[0883] Input: Generated prompt (e.g., "Theme: Jujutsu Kaisen-style ice cream, Characteristics: dark color, luxury, surprising flavor").

[0884] Output: Recipe data (ingredients list and cooking instructions) returned by the generative AI model.

[0885] Specific operation: The server inputs the prompt sentence into the API of the generative AI model and requests the generation of a formula. The generative AI model generates a formula based on the input and returns the result to the server.

[0886] Step 8:

[0887] The server receives the recipe generated from the generative AI model.

[0888] Input: Recipe data returned by the generative AI model (e.g., a list of ingredients (e.g., "black sesame paste, sugar, cream, dry chocolate, gold dust") and specific cooking instructions).

[0889] Output: The recipe data is stored on the server.

[0890] Specific operation: Receives formulation method data from the generative AI model and stores it in the server.

[0891] Step 9:

[0892] The server transmits the generated recipe data to the user's terminal.

[0893] Input: Retained recipe data.

[0894] Output: The recipe data is sent to the terminal.

[0895] Specific operation: The server sends the formulation method data to the user's terminal as an HTTP response.

[0896] Step 10:

[0897] The compounding method received by the terminal is provided to the user in an easy-to-understand format.

[0898] Input: Formulation data sent from the server.

[0899] Output: The recipe shown to the user (e.g., "Mix black sesame paste, sugar, and cream to make ice cream, then drizzle with hot chocolate sauce and sprinkle with gold dust").

[0900] Specific operation: The terminal displays the received formulation data on the screen so that the user can check it.

[0901] (Application example 1)

[0902] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0903] In recent years, food delivery services have been increasing in number, and they are required to meet the diverse needs of consumers. However, existing delivery services offer limited menus, making it difficult to provide customized dishes and drinks that users desire. Therefore, there is a need for a system that can automatically generate new dishes and mixing methods based on any theme entered by the user and provide them to users.

[0904] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0905] In this invention, the server includes means for inputting a theme from a user, means for analyzing the input theme and extracting related features, means for generating a recipe based on the extracted features, means for providing the generated recipe to the user, and means for linking with an external system that provides customized food based on the generated recipe, thereby enabling the user to order customized food and drinks based on a theme through a delivery service.

[0906] "Means for users to input any theme" refers to devices or software that provide an interface that allows users to freely input themes and keywords.

[0907] "Means for analyzing the input topic and extracting relevant features" refers to algorithms or modules for analyzing the topic input by the user and extracting relevant keywords and features from it.

[0908] The "means for generating a recipe based on the extracted features" refers to an artificial intelligence model or algorithm for generating a new cooking method or recipe based on the extracted features.

[0909] The "means for providing the generated mixing method to the user" refers to a screen or application for displaying the generated cooking method or recipe in an easy-to-understand manner to the user.

[0910] "Means for linking with external systems that provide customized food based on the generated recipe" refers to protocols and APIs for linking with external food delivery services and cooking services in order to actually provide customized food based on the generated recipe.

[0911] "Generating recipes using artificial intelligence" refers to using artificial intelligence technologies such as machine learning and deep learning to generate new cooking methods and recipes based on freely entered themes.

[0912] "Means for extracting keywords from a topic and identifying related features" refers to natural language processing technology and text analysis tools that extract important keywords from the input topic and identify related features based on these.

[0913] Based on this invention, a specific embodiment for constructing a system that allows users to order customized food and drinks based on any theme through a delivery service will be described.

[0914] First, a user inputs a theme of their choice using the smartphone application "Custom Food." This input theme is then sent to the server. The server then uses an analysis module to analyze the input theme and extract related features. This analysis module uses natural language processing techniques and text analysis tools (e.g., NLTK and SpaCy) to extract keywords from the theme and identify related features.

[0915] The analyzed feature information is input into a generative AI model (for example, the GPT-3 model) to generate new cooking methods and recipes. The generated recipes are sent from the server to the user's smartphone application and displayed to the user. The user can check the suggested recipes and, if they like them, can order them directly from the delivery service.

[0916] The system works with external food delivery services, and customized dishes and drinks are prepared based on the generated recipes and delivered to the user. The system uses RESTful APIs and JSON data exchange for communication.

[0917] As a concrete example, suppose the user enters the theme "Orange Drinks for Halloween Party." For this theme, the following prompt sentences are used:

[0918] User-submitted theme: Orange drink for Halloween party

[0919] Extracted keywords: Halloween, orange, party, drink

[0920] Generative AI prompt: Generate a new orange drink recipe based on these keywords.

[0921] Based on this prompt, the generative AI model generates a recipe, suggesting "mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish." This recipe is provided to the user, and if the user places an order, it is actually delivered via the delivery service.

[0922] In this way, a system is realized that allows users to receive customized food and drinks based on any theme through a delivery service.

[0923] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0924] Step 1:

[0925] The user starts the smartphone application "Custom Food" and inputs a theme of their choice. Here, the theme is "Orange Drink for Halloween Party." The input theme is sent to the server as JSON format data.

[0926] Input: "Orange drink for Halloween party"

[0927] Output: JSON data sent to the server

[0928] Step 2:

[0929] The server passes the received theme data to the analysis module, which analyzes it and extracts theme keywords. Here, natural language processing techniques (e.g., NLTK or SpaCy) are used to identify the following keywords:

[0930] Input: "Orange drink for Halloween party"

[0931] Data processing: Text analysis

[0932] Output: Keywords "Halloween, orange, party, drink"

[0933] Step 3:

[0934] The server inputs the extracted keywords into a generative AI model (e.g., GPT-3) to create a prompt for formulating the recipe. The generative AI model then generates a new recipe based on the prompt.

[0935] Enter: Keywords "Halloween, orange, party, drink"

[0936] Data Computation: Prompt input to generative AI models

[0937] Output: New recipe: "Mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish."

[0938] Step 4:

[0939] The server sends the generated recipe as JSON format data to a smartphone application, where the user receives and confirms the new recipe.

[0940] Input: New recipe: "Mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish."

[0941] Output: Send JSON data to smartphone application

[0942] Step 5:

[0943] The user checks the provided recipes, and if they like them, they place an order with the delivery service directly through the application. This order information is sent to an external delivery system, and processing begins.

[0944] Input: New recipe order information

[0945] Output: Send order data to external delivery system

[0946] Step 6:

[0947] The external delivery system prepares customized food and drinks based on the received order information and delivers them to the user.

[0948] Input: Order data

[0949] Output: Delivery to user

[0950] As a result, through this system, users can receive customized food and drinks based on any theme through a delivery service.

[0951] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0952] The present invention is a system that automatically generates new dishes and recipes based on any theme and emotion input by a user, and provides specific cooking instructions. The system includes an interface for the user to input the theme and emotion, a module for analyzing the theme and emotion, a recipe generation module using generative AI, and an interface that provides the generated recipes to the user.

[0953] System Overview

[0954] 1. User inputs theme and emotion

[0955] The user inputs a theme and the emotion they want to reflect through the interface displayed on the device. The theme specifies the direction and image of the dish and includes specific keywords and phrases. The emotion represents the user's feelings. For example, "Pokémon-style dressing" and the emotion "fun" can be input.

[0956] 2. Emotion Analysis

[0957] The device uses an emotion engine to analyze emotions based on user input, facial expressions, voice, etc. This emotion data is sent to a server and integrated with thematic analysis.

[0958] 3. Thematic Analysis

[0959] The server receives the themes and emotions submitted by the user and passes them to an analysis module. This analysis module extracts keywords and features from the input themes and identifies characteristics that incorporate related emotional elements. For example, combining "Pokémon-style dressing" with the emotion "fun" can identify a colorful, playful, and energetic flavor.

[0960] 4. Generate a recipe

[0961] The server inputs the features extracted from the analysis module into the generation AI and instructs it to generate a recipe. Based on this, the generation AI generates a specific list of ingredients and cooking instructions. For example, based on "Pokémon-style dressing" and the emotion "fun," it generates "a bright yellow curry-flavored dressing made with curry powder, mustard, and honey."

[0962] 5. Providing mixing methods

[0963] The server sends the generated recipe to the terminal and provides it to the user. The terminal displays the recipe in an easy-to-understand format, guiding the user when actually cooking the dish. For example, the terminal may provide instructions for "mixing curry powder, mustard, and honey to create a dressing."

[0964] Specific examples

[0965] Below is a specific example where a user requests "Jujutsu Kaisen-style ice cream" and "surprise."

[0966] Entering themes and emotions

[0967] The user inputs the emotion "Jujutsu Kaisen-style ice cream" and "surprise" into the device, which then sends it to the server. The emotion engine analyzes facial expressions and voice to complete the emotion data.

[0968] Theme and sentiment analysis

[0969] The server analyzes the emotion of "Jujutsu Kaisen-style ice cream" and "surprise," and identifies the characteristics as "dark color," "luxury," and "surprising flavor."

[0970] Generate recipes

[0971] The server inputs the characteristics into the AI ​​generator and instructs it to generate a recipe for the ice cream. The AI ​​then generates a specific recipe for the ice cream using black sesame paste, sugar, cream, dry chocolate, and gold powder.

[0972] Providing mixing methods

[0973] The server sends the generated recipe to the terminal, which then provides the user with instructions such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on some spicy chocolate sauce and sprinkle with gold powder."

[0974] This allows users to try out new themes and emotions based dishes and preparations, broadening the scope of their cooking experience.

[0975] The processing flow will be explained below.

[0976] Step 1:

[0977] The user inputs the theme and emotion using the input interface displayed on the terminal, for example, "Pokémon-style dressing" and "fun."

[0978] Step 2:

[0979] The terminal transmits the input theme and emotion to the server, where the theme and emotion data is converted into an appropriate format and transmitted.

[0980] Step 3:

[0981] The server passes the received themes and emotions to an analysis module, which extracts thematic keywords and related emotional features.

[0982] Step 4:

[0983] The device acquires emotional data from the user's facial expressions and voice, analyzes it using an emotion engine, and then combines the analyzed emotional data with themes and sends it to the server.

[0984] Step 5:

[0985] The server receives the extracted features and emotion data from the analysis module and prepares it as input data for the generative AI. The input data format is then adjusted.

[0986] Step 6:

[0987] The server sends the prepared characteristic and emotion data to the generation AI, which then generates a specific ingredient list and cooking instructions.

[0988] Step 7:

[0989] The generative AI generates a specific list of ingredients, mixing methods, and cooking steps, and sends that data back to the server. For example, it might generate a recipe for "curry dressing made with curry powder, mustard, and honey."

[0990] Step 8:

[0991] The server formats the generated recipe data and converts it into a format that can be provided to the user.

[0992] Step 9:

[0993] The server sends the generated mixing method data to the terminal. The generated mixing method or recipe is sent in an appropriate format.

[0994] Step 10:

[0995] The device displays the generated recipe to the user, including a list of ingredients and cooking instructions.

[0996] Step 11:

[0997] Users can actually cook dishes and dressings according to the recipes provided, allowing them to enjoy a new cooking experience.

[0998] Example 2

[0999] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1000] Conventional recipe generation systems for cooking and mixing require users to specify specific recipes and ingredients, making it difficult to provide creative cooking and mixing methods that reflect the user's emotions or themes.There is a demand for a method to easily generate new cooking and mixing methods based on themes that reflect the user's emotions, thereby making the user's cooking experience richer and more enjoyable.

[1001] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1002] In this invention, the server includes a means for inputting a theme and emotion from the user, a means for analyzing the input theme and emotion and extracting related features, a generation AI means for generating a recipe based on the extracted features, and a means for providing the generated recipe to the user. This makes it possible to automatically generate new and creative dishes and recipes based on the theme and emotion input by the user.

[1003] A "user" is an entity that uses the system to input a theme and emotion and create a new dish or recipe.

[1004] "Theme" refers to keywords or phrases related to the direction or image of the dish entered by the user.

[1005] "Emotion" is input data for expressing the user's feelings or emotions.

[1006] "Means" refers to the mechanisms or methods used to achieve a particular function.

[1007] An "emotion analysis engine" is a software module that analyzes user input, facial expressions, voice, etc. to identify emotions.

[1008] The "theme analysis module" is a software module that extracts keywords and features from the input theme and integrates related emotional elements to identify characteristics.

[1009] "Generative AI" is a model that uses artificial intelligence technology to generate new dishes and recipes based on identified themes and emotions.

[1010] "Formulation" refers to the specific ingredient list and cooking steps for creating a dish or drink.

[1011] A "server" is a computer system that receives input from a user, performs analysis, and provides the generated formula.

[1012] A "terminal" is a device (such as a smartphone, tablet, or PC) that allows a user to input a theme and emotion, and receive and display the generated recipe.

[1013] The present invention is a system that automatically generates new dishes and recipes based on any theme and emotion input by a user, and provides specific cooking instructions. The system includes an interface for the user to input the theme and emotion, a module for analyzing the theme and emotion, a recipe generation module using generative AI, and an interface that provides the generated recipes to the user.

[1014] The system is programmed using the following hardware and software:

[1015] Hardware: User devices (smartphones, tablets, PCs), servers

[1016] Software: Sentiment analysis engine, theme analysis module, generative AI, user interface

[1017] The system operates as follows:

[1018] 1. User inputs theme and emotion:

[1019] Users input their desired theme and emotion through the device interface, such as "Pokémon-style dressing" or "fun" into the input form displayed on the app screen of their smartphone or tablet, and then press the send button.

[1020] 2. Emotion Analysis:

[1021] The device passes the user's input data, voice, facial expressions, etc. to the emotion analysis engine for analysis. For example, the device's camera captures the user's facial expression and identifies the emotion based on the image data. Text data entered by the user is also analyzed, and the analysis results are sent to the server.

[1022] 3. Thematic Analysis:

[1023] The server receives the theme and emotion data sent by the user and sends it to the theme analysis module. The theme analysis module extracts keywords and features from the input theme and integrates them with the emotion analysis results. Specifically, from the data "Pokémon-style dressing" and "fun," it identifies characteristics such as "colorful," "playful," and "encouraging."

[1024] 4. Generate formulation:

[1025] The server inputs the extracted features from the analysis module into the generative AI, which then generates a specific recipe, including a list of ingredients and cooking instructions. For example, it creates a recipe for a bright yellow curry-flavored dressing made with curry powder, mustard, and honey.

[1026] 5. Providing formulation methods:

[1027] The server sends the generated recipe to the device and provides it to the user. The device displays the recipe on the screen, allowing the user to follow the instructions to cook the dish. For example, the smartphone app screen might show instructions for mixing curry powder, mustard, and honey to create a dressing.

[1028] Specific examples

[1029] If a user requests "Jujutsu Kaisen-style ice cream" and "surprise":

[1030] 1. User inputs theme and emotion:

[1031] The user enters "Jujutsu Kaisen-style ice cream" and "surprise" into the device and sends it.

[1032] 2. Emotion Analysis:

[1033] The device uses an emotion analysis engine to analyze the input content and supplementary data and transmits them to the server.

[1034] 3. Thematic and emotional analysis:

[1035] The server receives this and passes it to an analysis module, which identifies characteristics such as "dark color," "luxury," and "surprising flavor."

[1036] 4. Generate formulation:

[1037] The server suggests ingredients to the AI ​​to use, such as black sesame paste, sugar, cream, dry chocolate, and gold powder, and generates a specific recipe.

[1038] 5. Providing formulation methods:

[1039] The server sends the generated recipe to the device, which then displays instructions to the user, such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder."

[1040] This allows users to easily generate their own dishes and recipes based on new themes and emotions by entering prompts such as "Theme: Jujutsu Kaisen-style ice cream; Emotion: Surprise" or "Theme: Harry Potter-style dessert; Emotion: Magic."

[1041] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1042] Step 1:

[1043] User inputs theme and sentiment

[1044] The user inputs a theme and emotion using the device interface. For example, they input the words "Pokémon-style dressing" and "fun" into the smartphone app screen and press the send button.

[1045] The input theme and emotion data (e.g., "Pokémon-style dressing" and "fun") are received by the device, and the device proceeds to the next step.

[1046] Step 2:

[1047] Perform sentiment analysis

[1048] Based on the theme and emotion data received by the device, emotions are analyzed using an emotion analysis engine.

[1049] Specifically, the device's camera captures the user's facial expression, and the emotion analysis engine analyzes the image data. The input text data is also processed by the emotion analysis engine. The analysis results (e.g., cheerfulness, degree of enjoyment) are displayed on the device and sent to the server.

[1050] Step 3:

[1051] Conduct a theme analysis

[1052] The server receives the theme and emotion data sent from the terminal and passes it to the theme analysis module.

[1053] The theme analysis module extracts key keywords and characteristics from the input theme and combines them with the sentiment analysis results. For example, from the data of "Pokémon-style dressing" and "fun," it identifies the characteristics of colorful and energetic. This characteristic data is then input into the generative AI.

[1054] Step 4:

[1055] Generate a recipe

[1056] The server inputs the characteristic data sent from the theme analysis module into the generation AI and instructs it to generate a specific synthesis method.

[1057] Based on the characteristic data, the generative AI generates a specific list of ingredients and cooking instructions. For example, it might generate a recipe for a bright yellow curry-flavored dressing made with curry powder, mustard, and honey. This generated recipe data is then returned to the server.

[1058] Step 5:

[1059] Providing users with compounding methods

[1060] The server transmits the generated formulation method data to the user's terminal.

[1061] The device then displays the received recipe on a user interface. For example, a recipe for mixing curry powder, mustard, and honey to create a dressing is displayed on the smartphone screen. This allows the user to follow the displayed recipe to prepare the dish.

[1062] Specific examples

[1063] If the user enters "Jujutsu Kaisen style ice cream" and "surprise", the processing steps are:

[1064] Step 1:

[1065] User inputs theme and sentiment

[1066] The user enters "Jujutsu Kaisen-style ice cream" and "surprise" into the device's interface and presses the send button.

[1067] Step 2:

[1068] Perform sentiment analysis

[1069] The device passes the input theme and emotion to the emotion analysis engine for analysis. After facial expression capture and text data analysis, the emotion analysis results are sent to the server (e.g., surprise, elation).

[1070] Step 3:

[1071] Conduct a theme analysis

[1072] The server passes the received theme and emotion data to the theme analysis module, which extracts keywords and characteristics. Specific analysis results, such as "dark color," "luxury," and "surprising flavor," are identified and sent to the generation AI.

[1073] Step 4:

[1074] Generate a recipe

[1075] The server inputs the analysis results into the AI ​​generator and instructs it to generate a specific recipe. The AI ​​generator generates a recipe using black sesame paste, sugar, cream, dry chocolate, and gold powder and sends it back to the server.

[1076] Step 5:

[1077] Providing users with compounding methods

[1078] The server sends the generated recipe to the terminal, which then displays instructions to the user, such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder."

[1079] This allows users to create and cook their own unique dishes and recipes based on new themes and emotions.

[1080] (Application example 2)

[1081] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1082] Conventional systems have struggled to automatically generate personalized recipes based on a user's preferences and emotions and instantly deliver meals based on those recipes. Therefore, there is a need for a system that allows users to easily enjoy meals that match their specific emotions or themes. There is also a need for technology that can link the generation of recipes using AI with the actual delivery of ingredients.

[1083] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1084] In this invention, the server includes means for inputting a theme and emotion from a user, means for analyzing the input theme and emotion and extracting related features, means for generating a recipe based on the extracted features, means for providing the generated recipe to the user, and means for supplying and delivering items based on the generated recipe. This enables automatic generation of personalized dishes according to the user's emotion or theme, and actual delivery of the dishes based on the dishes.

[1085] A "theme" is a keyword or phrase that indicates the direction or image of the dish specified by the user.

[1086] "Emotions" are data that indicate the feelings and emotional states of the user, and are analyzed together with themes.

[1087] "Parsing" is the process of extracting relevant features from the input themes and sentiments.

[1088] "Features" are thematic and emotional characteristics or elements extracted through analysis.

[1089] "Generator" is a module that uses artificial intelligence and algorithms to generate a recipe based on the analyzed characteristics.

[1090] "Recipe" refers to a specific ingredient list and cooking instructions that are automatically generated by the generating means.

[1091] The "means for providing" is an interface for displaying or communicating the generated recipe to the user.

[1092] "Product supply" is the process of providing actual ingredients and dishes to the user based on the created recipe.

[1093] "Delivery" refers to the act of transporting the supplied item to a location designated by the user.

[1094] This invention is a system that automatically generates new recipes based on any theme and emotion entered by a user and provides specific cooking procedures. To this end, the system includes the following components and processes:

[1095] Components:

[1096] 1. User devices: Includes smartphones, tablets, etc.

[1097] 2. Emotion Analysis Module: Includes software for analyzing emotions, such as Face API.

[1098] 3. Backend server: A server that uses the Django framework to perform analysis and generation.

[1099] 4. Generative AI models: Use generative AI such as GPT-4.

[1100] 5. Delivery system: Use the API of partner delivery services (e.g. Uber Eats API).

[1101] 6. Database: Includes databases such as PostgreSQL.

[1102] Explanation of program operation:

[1103] In this system, users first input their desired theme and emotion on the interface using a smartphone or tablet. The theme and emotion data is collected from the device. The emotion analysis module uses Face API to scan the user's emotions in real time and determine the primary emotion.

[1104] These inputs are then sent to a backend server, which uses the Django framework to parse thematic and sentiment keywords and extract relevant features, which are then used as prompts for a generative AI model (GPT-4).

[1105] GPT-4 generates a specific recipe based on the prompts, including a list of ingredients and specific cooking steps. The generated recipe is sent from the server to the user's device for review and selection.

[1106] The food is prepared at a partner restaurant based on the recipe selected by the user, and then delivered to the user using the Uber Eats API. This entire process is designed to provide users with a personalized cooking experience.

[1107] Example explanation:

[1108] For example, if a user inputs the theme and emotion "Christmas dinner" and "happiness," this data will be analyzed by the emotion analysis module and sent to the backend server. The server will use the analysis module to extract the features of "Christmas dinner" and "happiness," and send the following prompt to GPT-4: "Generate a new food recipe based on theme: Christmas dinner, emotion: happiness."

[1109] The recipe generated by GPT-4 specifically includes the ingredients "roast chicken, cranberry sauce, and mashed potatoes," and cooking instructions are generated based on those ingredients. This recipe is provided to the user, and after the user confirms it, the food is prepared at a partner restaurant and delivered via the Uber Eats API.

[1110] This system allows users to enjoy cooking that is optimized to their own theme and emotion.

[1111] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1112] Step 1:

[1113] The user inputs a theme and emotion of their choice into the device. The user inputs a theme (e.g., "Christmas dinner") and emotion (e.g., "happy") through the interface on their smartphone or tablet. This becomes the initial input for generating user input data.

[1114] Step 2:

[1115] The device uses an emotion analysis module to scan the user's emotions and determine the primary emotion. Using Face API or similar, it analyzes emotions from the user's facial expressions and obtains complementary emotion data. The user's facial expression data is given as input, and the analyzed emotion data is obtained as output.

[1116] Step 3:

[1117] The terminal sends theme and emotion data to the backend server. The input is the theme and emotion data entered by the user, which is sent to the backend server via a RESTful API using Django. The output is the data received by the server.

[1118] Step 4:

[1119] The server analyzes the received themes and emotions and extracts related features. Using the Django framework, the server analyzes themes and emotions for keywords and extracts related features (e.g., for "Christmas dinner," it extracts "celebratory," "warm," and "special"). The input is themes and emotions, and the output is feature data.

[1120] Step 5:

[1121] The server inputs the extracted features as a prompt to the generative AI model (GPT-4) to generate a recipe. The server generates a prompt, "Generate a new cooking recipe based on theme: Christmas dinner, emotion: happiness," and sends it to GPT-4. The input is the prompt, and the output is the generated recipe data.

[1122] Step 6:

[1123] The server provides the generated recipe to the user. The server sends the generated recipe data (e.g., "roast chicken, cranberry sauce, and mashed potatoes") to the terminal. The input is the generated recipe data, and the output is the display data on the user terminal.

[1124] Step 7:

[1125] The user checks and selects the provided recipe. The user checks the provided recipe through the terminal interface and selects a specific dish. The input is the provided recipe data, and the output is the selected recipe data.

[1126] Step 8:

[1127] The server instructs the partner restaurant to prepare the dish based on the selected recipe. Based on the selected recipe data, the server notifies the partner restaurant of the necessary ingredients and cooking procedures. The input is the selected recipe data, and the output is cooking instruction data for the partner restaurant.

[1128] Step 9:

[1129] The partner restaurant prepares the food based on the recipe. The partner restaurant prepares the food based on the recipe data provided by the server. The input is cooking instruction data, and the output is the actual food.

[1130] Step 10:

[1131] The cooked food is delivered to the user using a delivery service. The server uses the Uber Eats API to deliver the food to the user's specified location. The input is delivery instruction data, and the output is the arrival of the food at the user's location.

[1132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1133] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1134] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1135] [Fourth embodiment]

[1136] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1140] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1143] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1145] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1147] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1148] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1149] The present invention is a system that automatically generates new dishes and mixing methods based on any theme entered by the user and provides specific cooking procedures. The system includes an interface for the user to input the theme, a module for analyzing the theme, a mixing method generation module using generation AI, and an interface that provides the generated mixing methods to the user.

[1150] System Overview

[1151] 1. The user enters a theme

[1152] The user inputs a theme of their choice through the interface displayed on the device. This theme specifies the direction and image of the dish and includes specific keywords and phrases. For example, themes such as "Pokémon-style dressing" or "Jujutsu Kaisen-style ice cream" can be input.

[1153] 2. Thematic Analysis

[1154] The server receives the theme submitted by the user and passes it to an analysis module. This analysis module extracts keywords and features from the input theme and identifies related elements. For example, in the case of "Pokémon-style dressing," features such as colorful, playful designs and unique flavor variations are extracted.

[1155] 3. Generate a recipe

[1156] The server inputs the features extracted from the analysis module into the generation AI and instructs it to generate a recipe. Based on this, the generation AI generates a specific ingredient list and cooking instructions. For example, for a "Pokémon-style dressing," it generates a "bright yellow curry-flavored dressing made with curry powder, mustard, and honey."

[1157] 4. Providing mixing methods

[1158] The server sends the generated recipe to the terminal and provides it to the user. The terminal displays the recipe in an easy-to-understand format, guiding the user when actually cooking the dish. For example, the terminal may provide instructions for "mixing curry powder, mustard, and honey to create a dressing."

[1159] Specific examples

[1160] Below is a specific example of a user requesting "Jujutsu Kaisen-style ice cream."

[1161] Entering the theme

[1162] The user types "Jujutsu Kaisen-style ice cream" into the device, and the device sends this to the server.

[1163] Theme Analysis

[1164] The server analyzes the "Jujutsu Kaisen-style ice cream" and extracts its characteristics: "dark color," "luxury," and "surprising flavor."

[1165] Generate recipes

[1166] The server inputs the characteristics into the AI ​​generator and instructs it to generate a recipe for the ice cream. The AI ​​then generates a specific recipe for the ice cream using black sesame paste, sugar, cream, dry chocolate, and gold powder.

[1167] Providing mixing methods

[1168] The server sends the generated recipe to the terminal, which then provides the user with instructions such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on some spicy chocolate sauce and sprinkle with gold powder."

[1169] This allows users to try out new themes and create their own unique dishes and mixing methods, broadening the scope of their cooking experience.

[1170] The processing flow will be explained below.

[1171] Step 1:

[1172] The device provides the user with an input interface, such as a text box or voice input option displayed on the screen.

[1173] Step 2:

[1174] The user inputs a theme into the terminal. For example, the user inputs the theme "Pokémon-style dressing."

[1175] Step 3:

[1176] The terminal transmits the input theme to the server in the form of data.

[1177] Step 4:

[1178] The server passes the received themes to the analysis module for analysis, which extracts themes' keywords and identifies related features.

[1179] Step 5:

[1180] The analysis module sends the extracted features back to the server. For example, in the case of "Pokémon-style dressing," it extracts bright colors, playfulness, and character traits.

[1181] Step 6:

[1182] The server prepares input data for the generative AI based on the extracted features, and converts it into a format for sending to the AI.

[1183] Step 7:

[1184] The server sends the feature data to the generation AI and instructs it to generate a mixing method. The generation AI generates a mixing method or recipe based on the input features.

[1185] Step 8:

[1186] The generative AI generates a specific list of ingredients, mixing methods, and cooking steps, and sends that data back to the server. For example, it might generate a recipe for "curry dressing made with curry powder, mustard, and honey."

[1187] Step 9:

[1188] The server formats the generated recipe data and converts it into a format that can be provided to the user.

[1189] Step 10:

[1190] The server then sends the formatted data to the terminal, including the generated mixing method and recipe.

[1191] Step 11:

[1192] The device displays the generated recipe to the user, specifically, the list of ingredients and cooking instructions on the screen, helping the user to create the dish by following the instructions.

[1193] Step 12:

[1194] Users can actually cook dishes and dressings according to the recipes provided, allowing them to enjoy a new cooking experience.

[1195] Example 1

[1196] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1197] In today's world, there are an increasing number of cases where users desire new dishes and recipes based on a variety of themes, but there is no automated system to realize this.There is a need for a system that can automatically generate dishes and recipes based on a theme by allowing the user to input an arbitrary theme, and provide specific cooking instructions.

[1198] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1199] In this invention, the server includes means for inputting a theme of a user's choice, means for transmitting the input theme to the server, means for passing the theme data received by the server to an analysis module and extracting features from the theme, means for the server to input a prompt sentence to the generative AI model based on the extracted features and generate a recipe, means for the server to transmit the generated recipe data to the user's terminal, and means for the terminal to provide the user with the received recipe in an easy-to-understand format. This allows the user to easily acquire new dishes and recipes based on a desired theme and obtain specific guidance for actually cooking.

[1200] A "user" is someone who uses the system to input a theme of their choice and wishes to create new dishes or recipes.

[1201] A "theme" is a keyword or phrase that a user inputs into the system to express the direction or image of a dish or preparation method.

[1202] The "server" is a computer system that receives a theme input by a user, analyzes the theme, generates a compounding method, and provides the generated compounding method to the user.

[1203] "Terminal" means a device through which a user inputs a theme and receives the generated formula, including a mobile phone, tablet, personal computer, etc.

[1204] "Analysis Module" means a software component within the server for analyzing themes and extracting relevant features.

[1205] "Features" are elements or characteristics related to a theme extracted by the analysis module.

[1206] "Generative AI model" refers to an artificial intelligence model that the server uses to input a prompt statement and automatically generate a formula based on the characteristics of the statement.

[1207] A "prompt sentence" is a sentence that indicates the content that should be generated based on the input entered by the server when instructing the generative AI model to generate a formulation method.

[1208] A "recipe" is a recipe generated by a generative AI model, including a specific list of ingredients and cooking steps.

[1209] The system of the present invention automatically generates new dishes and recipes based on any theme entered by a user and provides specific cooking procedures. The system includes an interface for the user to enter the theme, a module for analyzing the theme, a recipe generation module using a generative AI model, and an interface for providing the generated recipes to the user.

[1210] First, the user inputs a theme using the device interface. For example, a theme such as "Jujutsu Kaisen-style ice cream" is input into the device. Then, the device transmits the input theme to the server.

[1211] The server receives the theme sent by the user and passes it to an analysis module. This analysis module extracts keywords and features from the input theme and identifies related elements. For example, in the case of "Jujutsu Kaisen-style ice cream," features such as "dark color," "luxury," and "surprising flavor" are extracted.

[1212] The server then inputs the extracted features from the analysis module into a generative AI model to generate a recipe. Based on this prompt, the generative AI model generates a specific ingredient list and cooking instructions. For example, for "Jujutsu Kaisen-style ice cream," it generates a "specific ice cream recipe using black sesame paste, sugar, cream, dry chocolate, and gold powder."

[1213] The server receives the recipe and sends it to the device, which then presents it to the user in an easy-to-understand format. For example, the device might display specific recipe steps such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour in a spicy chocolate sauce and sprinkle with gold powder."

[1214] This allows users to try out new themes and create their own unique dishes and mixing methods, broadening the scope of their cooking experience.

[1215] As a concrete example, consider the case where a user requests "Jujutsu Kaisen-style ice cream." The user enters "Jujutsu Kaisen-style ice cream" into their device, which then sends this to the server. The server analyzes "Jujutsu Kaisen-style ice cream" and extracts the characteristics "dark color," "luxury," and "surprising flavor." The server inputs these characteristics into a generative AI model and instructs it to generate a mixing method. The generative AI model generates a specific ice cream recipe using "black sesame paste, sugar, cream, dry chocolate, and gold powder." The server sends the generated recipe to the device, which then provides the user with instructions such as "mix the black sesame paste, sugar, and cream to make the ice cream, drizzle with dry chocolate sauce, and sprinkle with gold powder."

[1216] Here is an example of a prompt that can be used with this system:

[1217] Theme: Jujutsu Kaisen-style ice cream

[1218] Characteristics: Dark color, luxury, surprising flavor

[1219] Ingredients: black sesame paste, sugar, cream, dry chocolate, gold powder

[1220] Steps: Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder.

[1221] This allows users to easily acquire new dishes and recipes based on a desired theme, and obtain specific instructions for actually cooking.

[1222] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1223] Step 1:

[1224] The user inputs a desired theme through the terminal interface.

[1225] Input: User-entered text for the theme (e.g., "Jujutsu Kaisen-style ice cream").

[1226] Output: The data format (text) of the input content is retained on the terminal.

[1227] Specific actions: Enter the theme using the keyboard in the input field displayed on the device screen and press the send button.

[1228] Step 2:

[1229] The terminal sends the user's input to the server.

[1230] Input: Theme text data stored on the device.

[1231] Output: The text data of the theme is sent to the server.

[1232] Specific operation: The device sends the entered theme data to the server using an HTTP POST request.

[1233] Step 3:

[1234] The server passes the received theme data to the analysis module.

[1235] Input: The text data of the theme received by the server.

[1236] Output: Data passed to the analysis module for thematic analysis.

[1237] Specific operation: The application inside the server passes the theme data to the API of the analysis module.

[1238] Step 4:

[1239] The analysis module extracts keywords and features from the themes.

[1240] Input: Thematic text data passed to the analysis module.

[1241] Output: Extracted feature data (e.g., "dark color," "luxury," "surprising flavor").

[1242] Specific operation: The analysis module uses natural language processing algorithms to analyze the text data of a topic and extract relevant features.

[1243] Step 5:

[1244] The server receives the extracted feature data from the analysis module.

[1245] Input: Feature data from the analysis module.

[1246] Output: The feature data is stored on the server.

[1247] Specific operation: The analysis module returns the extracted feature data in JSON format to the server, which then receives it and stores it in a database.

[1248] Step 6:

[1249] The server creates a prompt sentence to input into the generative AI model based on the feature data.

[1250] Input: Feature data (e.g., "dark color," "luxury," "surprising flavor").

[1251] Output: The generated prompt (e.g., "Theme: Jujutsu Kaisen-style ice cream, Characteristics: dark color, luxurious, surprising flavor").

[1252] Specific operation: The server references the feature data, generates a prompt sentence based on it, and stores it as character string data.

[1253] Step 7:

[1254] The server inputs a prompt statement into the generative AI model, instructing it to generate a formula.

[1255] Input: Generated prompt (e.g., "Theme: Jujutsu Kaisen-style ice cream, Characteristics: dark color, luxury, surprising flavor").

[1256] Output: Recipe data (ingredients list and cooking instructions) returned by the generative AI model.

[1257] Specific operation: The server inputs the prompt sentence into the API of the generative AI model and requests the generation of a formula. The generative AI model generates a formula based on the input and returns the result to the server.

[1258] Step 8:

[1259] The server receives the recipe generated from the generative AI model.

[1260] Input: Recipe data returned by the generative AI model (e.g., a list of ingredients (e.g., "black sesame paste, sugar, cream, dry chocolate, gold dust") and specific cooking instructions).

[1261] Output: The recipe data is stored on the server.

[1262] Specific operation: Receives formulation method data from the generative AI model and stores it in the server.

[1263] Step 9:

[1264] The server transmits the generated recipe data to the user's terminal.

[1265] Input: Retained recipe data.

[1266] Output: The recipe data is sent to the terminal.

[1267] Specific operation: The server sends the formulation method data to the user's terminal as an HTTP response.

[1268] Step 10:

[1269] The compounding method received by the terminal is provided to the user in an easy-to-understand format.

[1270] Input: Formulation data sent from the server.

[1271] Output: The recipe shown to the user (e.g., "Mix black sesame paste, sugar, and cream to make ice cream, then drizzle with hot chocolate sauce and sprinkle with gold dust").

[1272] Specific operation: The terminal displays the received formulation data on the screen so that the user can check it.

[1273] (Application example 1)

[1274] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1275] In recent years, food delivery services have been increasing in number, and they are required to meet the diverse needs of consumers. However, existing delivery services offer limited menus, making it difficult to provide customized dishes and drinks that users desire. Therefore, there is a need for a system that can automatically generate new dishes and mixing methods based on any theme entered by the user and provide them to users.

[1276] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1277] In this invention, the server includes means for inputting a theme from a user, means for analyzing the input theme and extracting related features, means for generating a recipe based on the extracted features, means for providing the generated recipe to the user, and means for linking with an external system that provides customized food based on the generated recipe, thereby enabling the user to order customized food and drinks based on a theme through a delivery service.

[1278] "Means for users to input any theme" refers to devices or software that provide an interface that allows users to freely input themes and keywords.

[1279] "Means for analyzing the input topic and extracting relevant features" refers to algorithms or modules for analyzing the topic input by the user and extracting relevant keywords and features from it.

[1280] The "means for generating a recipe based on the extracted features" refers to an artificial intelligence model or algorithm for generating a new cooking method or recipe based on the extracted features.

[1281] The "means for providing the generated mixing method to the user" refers to a screen or application for displaying the generated cooking method or recipe in an easy-to-understand manner to the user.

[1282] "Means for linking with external systems that provide customized food based on the generated recipe" refers to protocols and APIs for linking with external food delivery services and cooking services in order to actually provide customized food based on the generated recipe.

[1283] "Generating recipes using artificial intelligence" refers to using artificial intelligence technologies such as machine learning and deep learning to generate new cooking methods and recipes based on freely entered themes.

[1284] "Means for extracting keywords from a topic and identifying related features" refers to natural language processing technology and text analysis tools that extract important keywords from the input topic and identify related features based on these.

[1285] Based on this invention, a specific embodiment for constructing a system that allows users to order customized food and drinks based on any theme through a delivery service will be described.

[1286] First, a user inputs a theme of their choice using the smartphone application "Custom Food." This input theme is then sent to the server. The server then uses an analysis module to analyze the input theme and extract related features. This analysis module uses natural language processing techniques and text analysis tools (e.g., NLTK and SpaCy) to extract keywords from the theme and identify related features.

[1287] The analyzed feature information is input into a generative AI model (for example, the GPT-3 model) to generate new cooking methods and recipes. The generated recipes are sent from the server to the user's smartphone application and displayed to the user. The user can check the suggested recipes and, if they like them, can order them directly from the delivery service.

[1288] The system works with external food delivery services, and customized dishes and drinks are prepared based on the generated recipes and delivered to the user. The system uses RESTful APIs and JSON data exchange for communication.

[1289] As a concrete example, suppose the user enters the theme "Orange Drinks for Halloween Party." For this theme, the following prompt sentences are used:

[1290] User-submitted theme: Orange drink for Halloween party

[1291] Extracted keywords: Halloween, orange, party, drink

[1292] Generative AI prompt: Generate a new orange drink recipe based on these keywords.

[1293] Based on this prompt, the generative AI model generates a recipe, suggesting "mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish." This recipe is provided to the user, and if the user places an order, it is actually delivered via the delivery service.

[1294] In this way, a system is realized that allows users to receive customized food and drinks based on any theme through a delivery service.

[1295] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1296] Step 1:

[1297] The user starts the smartphone application "Custom Food" and inputs a theme of their choice. Here, the theme is "Orange Drink for Halloween Party." The input theme is sent to the server as JSON format data.

[1298] Input: "Orange drink for Halloween party"

[1299] Output: JSON data sent to the server

[1300] Step 2:

[1301] The server passes the received theme data to the analysis module, which analyzes it and extracts theme keywords. Here, natural language processing techniques (e.g., NLTK or SpaCy) are used to identify the following keywords:

[1302] Input: "Orange drink for Halloween party"

[1303] Data processing: Text analysis

[1304] Output: Keywords "Halloween, orange, party, drink"

[1305] Step 3:

[1306] The server inputs the extracted keywords into a generative AI model (e.g., GPT-3) to create a prompt for formulating the recipe. The generative AI model then generates a new recipe based on the prompt.

[1307] Enter: Keywords "Halloween, orange, party, drink"

[1308] Data Computation: Prompt input to generative AI models

[1309] Output: New recipe: "Mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish."

[1310] Step 4:

[1311] The server sends the generated recipe as JSON format data to a smartphone application, where the user receives and confirms the new recipe.

[1312] Input: New recipe: "Mix orange juice, carbonated water, cinnamon sticks, and orange slices to garnish."

[1313] Output: Send JSON data to smartphone application

[1314] Step 5:

[1315] The user checks the provided recipes, and if they like them, they place an order with the delivery service directly through the application. This order information is sent to an external delivery system, and processing begins.

[1316] Input: New recipe order information

[1317] Output: Send order data to external delivery system

[1318] Step 6:

[1319] The external delivery system prepares customized food and drinks based on the received order information and delivers them to the user.

[1320] Input: Order data

[1321] Output: Delivery to user

[1322] As a result, through this system, users can receive customized food and drinks based on any theme through a delivery service.

[1323] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1324] The present invention is a system that automatically generates new dishes and recipes based on any theme and emotion input by a user, and provides specific cooking instructions. The system includes an interface for the user to input the theme and emotion, a module for analyzing the theme and emotion, a recipe generation module using generative AI, and an interface that provides the generated recipes to the user.

[1325] System Overview

[1326] 1. User inputs theme and emotion

[1327] The user inputs a theme and the emotion they want to reflect through the interface displayed on the device. The theme specifies the direction and image of the dish and includes specific keywords and phrases. The emotion represents the user's feelings. For example, "Pokémon-style dressing" and the emotion "fun" can be input.

[1328] 2. Emotion Analysis

[1329] The device uses an emotion engine to analyze emotions based on user input, facial expressions, voice, etc. This emotion data is sent to a server and integrated with thematic analysis.

[1330] 3. Thematic Analysis

[1331] The server receives the themes and emotions submitted by the user and passes them to an analysis module. This analysis module extracts keywords and features from the input themes and identifies characteristics that incorporate related emotional elements. For example, combining "Pokémon-style dressing" with the emotion "fun" can identify a colorful, playful, and energetic flavor.

[1332] 4. Generate a recipe

[1333] The server inputs the features extracted from the analysis module into the generation AI and instructs it to generate a recipe. Based on this, the generation AI generates a specific list of ingredients and cooking instructions. For example, based on "Pokémon-style dressing" and the emotion "fun," it generates "a bright yellow curry-flavored dressing made with curry powder, mustard, and honey."

[1334] 5. Providing mixing methods

[1335] The server sends the generated recipe to the terminal and provides it to the user. The terminal displays the recipe in an easy-to-understand format, guiding the user when actually cooking the dish. For example, the terminal may provide instructions for "mixing curry powder, mustard, and honey to create a dressing."

[1336] Specific examples

[1337] Below is a specific example where a user requests "Jujutsu Kaisen-style ice cream" and "surprise."

[1338] Entering themes and emotions

[1339] The user inputs the emotion "Jujutsu Kaisen-style ice cream" and "surprise" into the device, which then sends it to the server. The emotion engine analyzes facial expressions and voice to complete the emotion data.

[1340] Theme and sentiment analysis

[1341] The server analyzes the emotion of "Jujutsu Kaisen-style ice cream" and "surprise," and identifies the characteristics as "dark color," "luxury," and "surprising flavor."

[1342] Generate recipes

[1343] The server inputs the characteristics into the AI ​​generator and instructs it to generate a recipe for the ice cream. The AI ​​then generates a specific recipe for the ice cream using black sesame paste, sugar, cream, dry chocolate, and gold powder.

[1344] Providing mixing methods

[1345] The server sends the generated recipe to the terminal, which then provides the user with instructions such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on some spicy chocolate sauce and sprinkle with gold powder."

[1346] This allows users to try out new themes and emotions based dishes and preparations, broadening the scope of their cooking experience.

[1347] The processing flow will be explained below.

[1348] Step 1:

[1349] The user inputs the theme and emotion using the input interface displayed on the terminal, for example, "Pokémon-style dressing" and "fun."

[1350] Step 2:

[1351] The terminal transmits the input theme and emotion to the server, where the theme and emotion data is converted into an appropriate format and transmitted.

[1352] Step 3:

[1353] The server passes the received themes and emotions to an analysis module, which extracts thematic keywords and related emotional features.

[1354] Step 4:

[1355] The device acquires emotional data from the user's facial expressions and voice, analyzes it using an emotion engine, and then combines the analyzed emotional data with themes and sends it to the server.

[1356] Step 5:

[1357] The server receives the extracted features and emotion data from the analysis module and prepares it as input data for the generative AI. The input data format is then adjusted.

[1358] Step 6:

[1359] The server sends the prepared characteristic and emotion data to the generation AI, which then generates a specific ingredient list and cooking instructions.

[1360] Step 7:

[1361] The generative AI generates a specific list of ingredients, mixing methods, and cooking steps, and sends that data back to the server. For example, it might generate a recipe for "curry dressing made with curry powder, mustard, and honey."

[1362] Step 8:

[1363] The server formats the generated recipe data and converts it into a format that can be provided to the user.

[1364] Step 9:

[1365] The server sends the generated mixing method data to the terminal. The generated mixing method or recipe is sent in an appropriate format.

[1366] Step 10:

[1367] The device displays the generated recipe to the user, including a list of ingredients and cooking instructions.

[1368] Step 11:

[1369] Users can actually cook dishes and dressings according to the recipes provided, allowing them to enjoy a new cooking experience.

[1370] Example 2

[1371] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1372] Conventional recipe generation systems for cooking and mixing require users to specify specific recipes and ingredients, making it difficult to provide creative cooking and mixing methods that reflect the user's emotions or themes.There is a demand for a method to easily generate new cooking and mixing methods based on themes that reflect the user's emotions, thereby making the user's cooking experience richer and more enjoyable.

[1373] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1374] In this invention, the server includes a means for inputting a theme and emotion from the user, a means for analyzing the input theme and emotion and extracting related features, a generation AI means for generating a recipe based on the extracted features, and a means for providing the generated recipe to the user. This makes it possible to automatically generate new and creative dishes and recipes based on the theme and emotion input by the user.

[1375] A "user" is an entity that uses the system to input a theme and emotion and create a new dish or recipe.

[1376] "Theme" refers to keywords or phrases related to the direction or image of the dish entered by the user.

[1377] "Emotion" is input data for expressing the user's feelings or emotions.

[1378] "Means" refers to the mechanisms or methods used to achieve a particular function.

[1379] An "emotion analysis engine" is a software module that analyzes user input, facial expressions, voice, etc. to identify emotions.

[1380] The "theme analysis module" is a software module that extracts keywords and features from the input theme and integrates related emotional elements to identify characteristics.

[1381] "Generative AI" is a model that uses artificial intelligence technology to generate new dishes and recipes based on identified themes and emotions.

[1382] "Formulation" refers to the specific ingredient list and cooking steps for creating a dish or drink.

[1383] A "server" is a computer system that receives input from a user, performs analysis, and provides the generated formula.

[1384] A "terminal" is a device (such as a smartphone, tablet, or PC) that allows a user to input a theme and emotion, and receive and display the generated recipe.

[1385] The present invention is a system that automatically generates new dishes and recipes based on any theme and emotion input by a user, and provides specific cooking instructions. The system includes an interface for the user to input the theme and emotion, a module for analyzing the theme and emotion, a recipe generation module using generative AI, and an interface that provides the generated recipes to the user.

[1386] The system is programmed using the following hardware and software:

[1387] Hardware: User devices (smartphones, tablets, PCs), servers

[1388] Software: Sentiment analysis engine, theme analysis module, generative AI, user interface

[1389] The system operates as follows:

[1390] 1. User inputs theme and emotion:

[1391] Users input their desired theme and emotion through the device interface, such as "Pokémon-style dressing" or "fun" into the input form displayed on the app screen of their smartphone or tablet, and then press the send button.

[1392] 2. Emotion Analysis:

[1393] The device passes the user's input data, voice, facial expressions, etc. to the emotion analysis engine for analysis. For example, the device's camera captures the user's facial expression and identifies the emotion based on the image data. Text data entered by the user is also analyzed, and the analysis results are sent to the server.

[1394] 3. Thematic Analysis:

[1395] The server receives the theme and emotion data sent by the user and sends it to the theme analysis module. The theme analysis module extracts keywords and features from the input theme and integrates them with the emotion analysis results. Specifically, from the data "Pokémon-style dressing" and "fun," it identifies characteristics such as "colorful," "playful," and "encouraging."

[1396] 4. Generate formulation:

[1397] The server inputs the extracted features from the analysis module into the generative AI, which then generates a specific recipe, including a list of ingredients and cooking instructions. For example, it creates a recipe for a bright yellow curry-flavored dressing made with curry powder, mustard, and honey.

[1398] 5. Providing formulation methods:

[1399] The server sends the generated recipe to the device and provides it to the user. The device displays the recipe on the screen, allowing the user to follow the instructions to cook the dish. For example, the smartphone app screen might show instructions for mixing curry powder, mustard, and honey to create a dressing.

[1400] Specific examples

[1401] If a user requests "Jujutsu Kaisen-style ice cream" and "surprise":

[1402] 1. User inputs theme and emotion:

[1403] The user enters "Jujutsu Kaisen-style ice cream" and "surprise" into the device and sends it.

[1404] 2. Emotion Analysis:

[1405] The device uses an emotion analysis engine to analyze the input content and supplementary data and transmits them to the server.

[1406] 3. Thematic and emotional analysis:

[1407] The server receives this and passes it to an analysis module, which identifies characteristics such as "dark color," "luxury," and "surprising flavor."

[1408] 4. Generate formulation:

[1409] The server suggests ingredients to the AI ​​to use, such as black sesame paste, sugar, cream, dry chocolate, and gold powder, and generates a specific recipe.

[1410] 5. Providing formulation methods:

[1411] The server sends the generated recipe to the device, which then displays instructions to the user, such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder."

[1412] This allows users to easily generate their own dishes and recipes based on new themes and emotions by entering prompts such as "Theme: Jujutsu Kaisen-style ice cream; Emotion: Surprise" or "Theme: Harry Potter-style dessert; Emotion: Magic."

[1413] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1414] Step 1:

[1415] User inputs theme and sentiment

[1416] The user inputs a theme and emotion using the device interface. For example, they input the words "Pokémon-style dressing" and "fun" into the smartphone app screen and press the send button.

[1417] The input theme and emotion data (e.g., "Pokémon-style dressing" and "fun") are received by the device, and the device proceeds to the next step.

[1418] Step 2:

[1419] Perform sentiment analysis

[1420] Based on the theme and emotion data received by the device, emotions are analyzed using an emotion analysis engine.

[1421] Specifically, the device's camera captures the user's facial expression, and the emotion analysis engine analyzes the image data. The input text data is also processed by the emotion analysis engine. The analysis results (e.g., cheerfulness, degree of enjoyment) are displayed on the device and sent to the server.

[1422] Step 3:

[1423] Conduct a theme analysis

[1424] The server receives the theme and emotion data sent from the terminal and passes it to the theme analysis module.

[1425] The theme analysis module extracts key keywords and characteristics from the input theme and combines them with the sentiment analysis results. For example, from the data of "Pokémon-style dressing" and "fun," it identifies the characteristics of colorful and energetic. This characteristic data is then input into the generative AI.

[1426] Step 4:

[1427] Generate a recipe

[1428] The server inputs the characteristic data sent from the theme analysis module into the generation AI and instructs it to generate a specific synthesis method.

[1429] Based on the characteristic data, the generative AI generates a specific list of ingredients and cooking instructions. For example, it might generate a recipe for a bright yellow curry-flavored dressing made with curry powder, mustard, and honey. This generated recipe data is then returned to the server.

[1430] Step 5:

[1431] Providing users with compounding methods

[1432] The server transmits the generated formulation method data to the user's terminal.

[1433] The device then displays the received recipe on a user interface. For example, a recipe for mixing curry powder, mustard, and honey to create a dressing is displayed on the smartphone screen. This allows the user to follow the displayed recipe to prepare the dish.

[1434] Specific examples

[1435] If the user enters "Jujutsu Kaisen style ice cream" and "surprise", the processing steps are:

[1436] Step 1:

[1437] User inputs theme and sentiment

[1438] The user enters "Jujutsu Kaisen-style ice cream" and "surprise" into the device's interface and presses the send button.

[1439] Step 2:

[1440] Perform sentiment analysis

[1441] The device passes the input theme and emotion to the emotion analysis engine for analysis. After facial expression capture and text data analysis, the emotion analysis results are sent to the server (e.g., surprise, elation).

[1442] Step 3:

[1443] Conduct a theme analysis

[1444] The server passes the received theme and emotion data to the theme analysis module, which extracts keywords and characteristics. Specific analysis results, such as "dark color," "luxury," and "surprising flavor," are identified and sent to the generation AI.

[1445] Step 4:

[1446] Generate a recipe

[1447] The server inputs the analysis results into the AI ​​generator and instructs it to generate a specific recipe. The AI ​​generator generates a recipe using black sesame paste, sugar, cream, dry chocolate, and gold powder and sends it back to the server.

[1448] Step 5:

[1449] Providing users with compounding methods

[1450] The server sends the generated recipe to the terminal, which then displays instructions to the user, such as "Mix black sesame paste, sugar, and cream to make ice cream, then pour on a spicy chocolate sauce and sprinkle with gold powder."

[1451] This allows users to create and cook their own unique dishes and recipes based on new themes and emotions.

[1452] (Application example 2)

[1453] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1454] Conventional systems have struggled to automatically generate personalized recipes based on a user's preferences and emotions and instantly deliver meals based on those recipes. Therefore, there is a need for a system that allows users to easily enjoy meals that match their specific emotions or themes. There is also a need for technology that can link the generation of recipes using AI with the actual delivery of ingredients.

[1455] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1456] In this invention, the server includes means for inputting a theme and emotion from a user, means for analyzing the input theme and emotion and extracting related features, means for generating a recipe based on the extracted features, means for providing the generated recipe to the user, and means for supplying and delivering items based on the generated recipe. This enables automatic generation of personalized dishes according to the user's emotion or theme, and actual delivery of the dishes based on the dishes.

[1457] A "theme" is a keyword or phrase that indicates the direction or image of the dish specified by the user.

[1458] "Emotions" are data that indicate the feelings and emotional states of the user, and are analyzed together with themes.

[1459] "Parsing" is the process of extracting relevant features from the input themes and sentiments.

[1460] "Features" are thematic and emotional characteristics or elements extracted through analysis.

[1461] "Generator" is a module that uses artificial intelligence and algorithms to generate a recipe based on the analyzed characteristics.

[1462] "Recipe" refers to a specific ingredient list and cooking instructions that are automatically generated by the generating means.

[1463] The "means for providing" is an interface for displaying or communicating the generated recipe to the user.

[1464] "Product supply" is the process of providing actual ingredients and dishes to the user based on the created recipe.

[1465] "Delivery" refers to the act of transporting the supplied item to a location designated by the user.

[1466] This invention is a system that automatically generates new recipes based on any theme and emotion entered by a user and provides specific cooking procedures. To this end, the system includes the following components and processes:

[1467] Components:

[1468] 1. User devices: Includes smartphones, tablets, etc.

[1469] 2. Emotion Analysis Module: Includes software for analyzing emotions, such as Face API.

[1470] 3. Backend server: A server that uses the Django framework to perform analysis and generation.

[1471] 4. Generative AI models: Use generative AI such as GPT-4.

[1472] 5. Delivery system: Use the API of partner delivery services (e.g. Uber Eats API).

[1473] 6. Database: Includes databases such as PostgreSQL.

[1474] Explanation of program operation:

[1475] In this system, users first input their desired theme and emotion on the interface using a smartphone or tablet. The theme and emotion data is collected from the device. The emotion analysis module uses Face API to scan the user's emotions in real time and determine the primary emotion.

[1476] These inputs are then sent to a backend server, which uses the Django framework to parse thematic and sentiment keywords and extract relevant features, which are then used as prompts for a generative AI model (GPT-4).

[1477] GPT-4 generates a specific recipe based on the prompts, including a list of ingredients and specific cooking steps. The generated recipe is sent from the server to the user's device for review and selection.

[1478] The food is prepared at a partner restaurant based on the recipe selected by the user, and then delivered to the user using the Uber Eats API. This entire process is designed to provide users with a personalized cooking experience.

[1479] Example explanation:

[1480] For example, if a user inputs the theme and emotion "Christmas dinner" and "happiness," this data will be analyzed by the emotion analysis module and sent to the backend server. The server will use the analysis module to extract the features of "Christmas dinner" and "happiness," and send the following prompt to GPT-4: "Generate a new food recipe based on theme: Christmas dinner, emotion: happiness."

[1481] The recipe generated by GPT-4 specifically includes the ingredients "roast chicken, cranberry sauce, and mashed potatoes," and cooking instructions are generated based on those ingredients. This recipe is provided to the user, and after the user confirms it, the food is prepared at a partner restaurant and delivered via the Uber Eats API.

[1482] This system allows users to enjoy cooking that is optimized to their own theme and emotion.

[1483] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1484] Step 1:

[1485] The user inputs a theme and emotion of their choice into the device. The user inputs a theme (e.g., "Christmas dinner") and emotion (e.g., "happy") through the interface on their smartphone or tablet. This becomes the initial input for generating user input data.

[1486] Step 2:

[1487] The device uses an emotion analysis module to scan the user's emotions and determine the primary emotion. Using Face API or similar, it analyzes emotions from the user's facial expressions and obtains complementary emotion data. The user's facial expression data is given as input, and the analyzed emotion data is obtained as output.

[1488] Step 3:

[1489] The terminal sends theme and emotion data to the backend server. The input is the theme and emotion data entered by the user, which is sent to the backend server via a RESTful API using Django. The output is the data received by the server.

[1490] Step 4:

[1491] The server analyzes the received themes and emotions and extracts related features. Using the Django framework, the server analyzes themes and emotions for keywords and extracts related features (e.g., for "Christmas dinner," it extracts "celebratory," "warm," and "special"). The input is themes and emotions, and the output is feature data.

[1492] Step 5:

[1493] The server inputs the extracted features as a prompt to the generative AI model (GPT-4) to generate a recipe. The server generates a prompt, "Generate a new cooking recipe based on theme: Christmas dinner, emotion: happiness," and sends it to GPT-4. The input is the prompt, and the output is the generated recipe data.

[1494] Step 6:

[1495] The server provides the generated recipe to the user. The server sends the generated recipe data (e.g., "roast chicken, cranberry sauce, and mashed potatoes") to the terminal. The input is the generated recipe data, and the output is the display data on the user terminal.

[1496] Step 7:

[1497] The user checks and selects the provided recipe. The user checks the provided recipe through the terminal interface and selects a specific dish. The input is the provided recipe data, and the output is the selected recipe data.

[1498] Step 8:

[1499] The server instructs the partner restaurant to prepare the dish based on the selected recipe. Based on the selected recipe data, the server notifies the partner restaurant of the necessary ingredients and cooking procedures. The input is the selected recipe data, and the output is cooking instruction data for the partner restaurant.

[1500] Step 9:

[1501] The partner restaurant prepares the food based on the recipe. The partner restaurant prepares the food based on the recipe data provided by the server. The input is cooking instruction data, and the output is the actual food.

[1502] Step 10:

[1503] The cooked food is delivered to the user using a delivery service. The server uses the Uber Eats API to deliver the food to the user's specified location. The input is delivery instruction data, and the output is the arrival of the food at the user's location.

[1504] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1505] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1506] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1507] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1508] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1509] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1510] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1511] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1512] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1513] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1514] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1515] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1516] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1517] 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.

[1518] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1519] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1520] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1521] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1522] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1523] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1524] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1525] The following is further disclosed regarding the above embodiment.

[1526] (Claim 1)

[1527] A means for inputting an arbitrary theme from a user;

[1528] means for analyzing the input topic and extracting relevant features;

[1529] a generating means for generating a recipe based on the extracted features;

[1530] means for providing the generated recipe to a user;

[1531] A system including:

[1532] (Claim 2)

[1533] 10. The system of claim 1, wherein the generating means generates the formulation using artificial intelligence.

[1534] (Claim 3)

[1535] 2. The system of claim 1, wherein the analyzing means includes means for extracting thematic keywords and identifying related features.

[1536] "Example 1"

[1537] (Claim 1)

[1538] A means for inputting an arbitrary theme from a user;

[1539] A means for transmitting the input theme to a server;

[1540] a means for passing the received theme data to an analysis module and extracting features from the themes;

[1541] A means for the server to input a prompt sentence to the generative AI model based on the extracted features to generate a compounding method;

[1542] A means for transmitting the generated blending method data to a user's terminal by the server;

[1543] a means for providing the received compounding method to the user in an easy-to-understand format;

[1544] A system including:

[1545] (Claim 2)

[1546] 2. The system of claim 1, wherein the generating means includes a terminal that displays the generated recipe to a user in an easy-to-understand format.

[1547] (Claim 3)

[1548] 10. The system of claim 1, wherein the server includes means for using an analysis module to extract thematic keywords and identify associated features.

[1549] "Application Example 1"

[1550] (Claim 1)

[1551] A means for inputting an arbitrary theme from a user;

[1552] means for analyzing the input topic and extracting relevant features;

[1553] a generating means for generating a recipe based on the extracted features;

[1554] means for providing the generated recipe to a user;

[1555] a means for interfacing with an external system to provide customized food products based on the generated formula;

[1556] A system including:

[1557] (Claim 2)

[1558] 10. The system of claim 1, wherein the generating means generates the formulation using artificial intelligence.

[1559] (Claim 3)

[1560] 2. The system of claim 1, wherein the analyzing means includes means for extracting thematic keywords and identifying related features.

[1561] "Example 2: Combining Emotion Engines"

[1562] (Claim 1)

[1563] A means for inputting an arbitrary theme and emotion from a user;

[1564] means for analyzing the input themes and sentiments and extracting relevant features;

[1565] a generation AI means for generating a formula based on the extracted features;

[1566] means for providing the generated recipe to a user;

[1567] A system including:

[1568] (Claim 2)

[1569] 10. The system of claim 1, wherein the generating AI means generates the formulation using an artificial intelligence model.

[1570] (Claim 3)

[1571] 10. The system of claim 1, wherein said analyzing means includes means for extracting thematic and emotional keywords and identifying associated features.

[1572] "Application example 2 when combining emotion engines"

[1573] (Claim 1)

[1574] A means for inputting an arbitrary theme and emotion from a user;

[1575] means for analyzing the input themes and sentiments and extracting relevant features;

[1576] a generating means for generating a recipe based on the extracted features;

[1577] means for providing the generated recipe to a user;

[1578] means for supplying and delivering the articles based on the generated formula;

[1579] A system including:

[1580] (Claim 2)

[1581] 10. The system of claim 1, wherein the generating means generates the formulation using artificial intelligence.

[1582] (Claim 3)

[1583] 10. The system of claim 1, wherein said analyzing means includes means for extracting thematic and emotional keywords and identifying associated features. [Explanation of symbols]

[1584] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting an arbitrary theme from a user; means for analyzing the input topic and extracting relevant features; a generating means for generating a recipe based on the extracted features; means for providing the generated recipe to a user; A system including:

2. The system of claim 1 , wherein the generating means generates the formulation using artificial intelligence.

3. 2. The system of claim 1, wherein said analyzing means includes means for extracting thematic keywords and identifying related features.

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