system

Abstract

We provide the system. [Solution] A means of receiving data entered by the user, A means for generating a cooking recipe using generative artificial intelligence based on the input data, A means of presenting the generated recipe to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Preparing meals in daily households involves a variety of complex processes, such as menu planning, ingredient procurement, and determining cooking methods according to family preferences and health conditions, which impose a burden on many households. In particular, when tired or in poor health, the time and effort required to prepare meals can be a significant stress. Also, when a family member dislikes a particular ingredient or dish, or has specific nutritional requirements due to their health condition, it is not easy to devise a menu to accommodate this. These factors make it difficult to prepare a balanced meal within the family.

Means for Solving the Problems

[0005] This invention provides a system that uses generative artificial intelligence to suggest appropriate cooking recipes based on data entered by the user, in order to solve the above-mentioned problems related to daily meal preparation at home. This system collects data related to family structure, preferences, available ingredients, and physical condition through input means and analyzes this data on a server. Based on the analysis results, the generative AI generates recipes that meet the user's needs and presents these recipes to the user, thereby reducing the burden of daily meal preparation. This system aims to simplify the complicated cooking process and efficiently provide healthy and balanced meals.

[0006] A "user" refers to an individual or a family member who uses this system to receive support in preparing meals at home.

[0007] "Entered data" refers to information about family structure, preferences, food possessions, and physical condition that users provide to the system manually or using sensors.

[0008] "Generative artificial intelligence" refers to an algorithm or system that has the ability to analyze presented data and generate new information or suggestions based on the results.

[0009] A "recipe" is a set of instructions that outlines the steps and necessary ingredients for preparing a particular dish.

[0010] "Suggestion" refers to the act of providing users with guidance or recommendations for their choices, or the content of such suggestions.

[0011] A "system" is a computer system in which a series of processes and functions work together to perform a specific task. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system that uses data-driven, generative artificial intelligence to suggest appropriate recipes, thereby efficiently supporting everyday meal preparation at home. The system is built around three elements: a home terminal, a cloud-based server, and the user.

[0034] Users use a device with a dedicated application installed to input data such as their family composition, food inventory, individual preferences, and health status for the day. For example, they might input "2 adults, 1 child" for family composition, "chicken, carrots, potatoes" for food in the refrigerator, and "likes spicy food" for preferences. This data entered by the user on the device is sent to the server.

[0035] The server runs a program to analyze the received data. This program uses generative artificial intelligence to generate the optimal recipe tailored to the user based on the collected information. For example, based on the entered family composition and ingredient data, it might suggest "chicken curry stew" and generate a recipe with easy-to-follow instructions for microwave cooking.

[0036] The device displays recipe information sent from the server to the user. This allows the user to easily obtain the information needed to prepare meals for the day, reducing the burden of cooking. Furthermore, users can provide feedback through the app, enabling the system to continuously improve its performance.

[0037] This system plays a crucial role in providing menus that ensure users receive a balanced intake of necessary nutrients, especially when they are busy or feeling unwell. In this way, it makes it possible to prepare daily meals more efficiently while considering the health of the entire family.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches a dedicated application and enters information about their family composition, preferences, the contents of their refrigerator, and their physical condition for the day. This information is entered using pull-down menus and text input fields.

[0041] Step 2:

[0042] The terminal formats the data entered by the user in bulk and sends it to the server as a JSON data packet. During this process, the communication is encrypted to protect user information.

[0043] Step 3:

[0044] The server decompresses the received data packets to analyze them, identifying and separating each information element. This process verifies the integrity and completeness of the data.

[0045] Step 4:

[0046] The server activates a generative artificial intelligence algorithm based on the analyzed data. This AI searches for and filters recipes that are tailored to the type of ingredients, health condition, and preferences.

[0047] Step 5:

[0048] The server customizes the generated candidate recipes to their optimal form. This customization is based on the cooking time, cooking method, and nutritional balance of the dishes.

[0049] Step 6:

[0050] The server selects the final recipe to suggest to the user and sends it to the terminal in a documented format. The data sent includes cooking instructions, details of required ingredients, and an estimated cooking time.

[0051] Step 7:

[0052] The terminal displays recipe information received from the server on the application screen. Based on this information, the user can begin preparing the specific dish.

[0053] Step 8:

[0054] Users perform cooking and, if necessary, provide feedback on the results and their satisfaction level within the application. This feedback is sent to the server to improve the service in the future.

[0055] (Example 1)

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

[0057] In today's busy society, preparing daily meals at home is a burden for many people. Furthermore, planning nutritionally balanced meals and selecting dishes to suit specific preferences is not easy. Moreover, while providing meals that are appropriate for one's health needs is crucial amidst busy schedules, achieving this with limited information and time is difficult. This invention aims to support efficient meal preparation at home by quickly and accurately proposing optimal meal configurations based on the specific circumstances of each household, thereby addressing these challenges.

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

[0059] In this invention, the server includes means for receiving information provided by the user, means for generating meal planning procedures based on the information using generative artificial intelligence, and means for transmitting the generated procedures to the user through a display device. This makes it possible for the user to easily plan and prepare efficient and balanced meals tailored to their individual preferences and health conditions.

[0060] "Users" refer to individuals who use the system to provide information and utilize the suggestions they receive.

[0061] "Information" refers to data that the system receives from users, such as demographics, preferences, food possessions, and health status.

[0062] "Generative artificial intelligence" refers to a technology that analyzes received information and generates solutions and suggestions tailored to individual conditions.

[0063] "Meal preparation instructions" refer to a set of instructions that outlines the preparation and cooking of a meal using specific ingredients and seasonings.

[0064] A "display device" refers to equipment or screens used to visually convey information and suggestions obtained from a system to the user.

[0065] "Evaluation" refers to feedback that users provide regarding the information provided by the system, and this data is useful for improving the system.

[0066] This invention is a system designed to streamline everyday meal preparation at home. The system utilizes a terminal, a cloud-based server, and a generative AI model. It uses user-input data to automatically generate optimal meal recipes tailored to individual circumstances and preferences.

[0067] Users install a dedicated application on their home device. Through this application, users input information such as family composition, a list of ingredients they own, their preferences, and their health status. For example, they can input a family composition of "2 adults and 1 child," an inventory of ingredients such as "chicken, carrots, and potatoes," and a preference for "spicy food." This information is transmitted from the device to a server in the cloud.

[0068] The server drives a generative AI model based on the received data and generates recipes based on the provided conditions. This AI model has an algorithm that identifies the most suitable meal configuration for the user from specific information.

[0069] The generated recipe is sent to the device, including cooking instructions and necessary nutritional information. The device displays this information to the user in a visually easy-to-understand format. This allows the user to easily and quickly obtain guidance for preparing daily meals.

[0070] As a concrete example, here is an example of a prompt statement:

[0071] "Today I'm preparing a meal for a family of two adults and one child. I have chicken, carrots, and potatoes in the refrigerator. I like spicy food, so I'd appreciate some simple recipes using these ingredients."

[0072] In this way, the system provides an environment in which users can efficiently and effectively create meal plans tailored to their individual needs.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] Users launch a dedicated application on their home device and input information about their family structure, available ingredients, preferences, and health status. This input data includes details such as "2 adults, 1 child," "chicken, carrots, potatoes," and "likes spicy food." This information serves as the basis for generating the optimal recipe in subsequent processing.

[0076] Step 2:

[0077] The terminal sends information entered by the user to a cloud-based server. The data processing performed here involves the terminal formatting the input data into a standardized format and transferring it to the server using a secure communication protocol. As a result of the transmission, information that meets the user's requirements is collected on the server side.

[0078] Step 3:

[0079] The server analyzes the received data and generates the optimal meal plan using a generative AI model. First, the server sets the input family composition and preferences as parameters for the AI ​​model and designs a recipe suitable for the user while referring to the ingredient database. This data calculation generates a specific recipe suggestion, such as "Chicken Curry Stew." The generated recipe, along with detailed cooking instructions, is then passed on to the next step.

[0080] Step 4:

[0081] The terminal receives recipes sent from the server and presents them to the user in a visually appealing and user-friendly interface. Specifically, the terminal displays the cooking process of the recipe step by step, making it easy for the user to follow. The user can then prepare a meal based on this information.

[0082] Step 5:

[0083] After completing a dish, users provide feedback to the system through the application. This feedback includes aspects such as "satisfaction with the dish" and "areas for improvement," and is used to enhance the accuracy of future recipe generation. This feedback input enables the system to continuously improve.

[0084] (Application Example 1)

[0085] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] Traditional food delivery services have faced challenges in providing personalized meal suggestions based on users' preferences and health conditions, making it difficult for them to choose appropriate dishes. Furthermore, ordering meals that meet specific criteria is difficult amidst busy daily lives.

[0087] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0088] In this invention, the server includes means for receiving data entered by a user, means for constructing dish suggestions using generative artificial intelligence based on the entered data, and means for presenting the suggested dishes to the user. This enables the suggestion of dishes tailored to the user's individual needs and allows for easy ordering.

[0089] "User" refers to an entity that uses the system to receive dish suggestions or place orders.

[0090] "Data" refers to information necessary to generate cooking suggestions, such as member information, preferences, food possessions, and health status, which are entered by the user.

[0091] "Generative artificial intelligence" refers to a machine learning model or algorithm that generates optimal cooking suggestions based on data received from users.

[0092] "Cooking suggestions" refers to meal options or recipes created by generative artificial intelligence and presented to the user.

[0093] A "terminal" refers to an electronic device used by users to operate the system, receive food suggestions, and place orders.

[0094] "Available for order" refers to a state where users can proceed directly to the purchase process for the suggested dishes within the system.

[0095] The system implementing this invention provides users with an electronic device such as a smartphone as a terminal. The terminal communicates with a cloud-based server, receives user input data, and performs the necessary processing. Users can easily input data such as their family members, preferences, food possessions, and health status through an application.

[0096] First, the application installed on the device starts running. The application sends the entered data to a cloud server, which then uses generative artificial intelligence to generate personalized cooking suggestions for each user. The generated suggestions are then sent back to the device and presented to the user.

[0097] The servers are built on cloud platforms such as Amazon Web Services and Google Cloud, and can utilize APIs provided by companies like OpenAI and Google as generative artificial intelligence models. This system allows users to quickly find dishes that perfectly match their preferences and circumstances and order them on their devices.

[0098] For example, if a family with elementary school-aged children is looking for a nutritious and spicy meal, this system can suggest "Spicy Tofu Chili" as a dish option. The user can then easily confirm their order.

[0099] An example of a prompt sentence for a generative AI model is: "Female, 30s, lives in Tokyo, likes spicy food, allergy: nuts. Suggest healthy and easy-to-order menu items." This allows the generative artificial intelligence to make appropriate suggestions and broaden the user's culinary options.

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The user launches the application on their device and enters data such as member information, preferences, food possessions, and health status. The entered data is appropriately formatted within the application and prepared for transmission to the server.

[0103] Step 2:

[0104] The device sends formatted user data to a cloud-based server. The data is securely transmitted via a secure protocol and stored in a database on the server side.

[0105] Step 3:

[0106] The server inputs the received data into the generative AI model and begins analyzing the data. The generative AI model forms optimal suggestions based on the input data. In this process, data pattern analysis and contextual understanding are performed, and the cooking suggestions that best suit the user's requests are generated.

[0107] Step 4:

[0108] The AI ​​model generates a cooking suggestion on the server. This suggestion includes the dish name, cooking outline, and required ingredients, and is formatted for presentation to the user.

[0109] Step 5:

[0110] The server sends the generated recipe suggestions to the terminal. The transmitted data is received by the user's terminal, and the application reads it and prepares to display it on the user interface.

[0111] Step 6:

[0112] Users review the dish suggestions displayed on the terminal and select their preferred dishes. The selected dishes are then prepared for purchase on the terminal. During this process, they confirm the order details and enter their delivery information.

[0113] Step 7:

[0114] Once an order is confirmed, the terminal sends the order data to the server, initiating the food delivery process. The server then makes the appropriate delivery arrangements and issues instructions for cooking and delivery of the food.

[0115] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0116] This invention integrates emotion recognition functionality into a system that supports home cooking, thereby improving user satisfaction and cooking efficiency by providing suggestions more tailored to individual needs. The system generates, customizes, and presents recipes based on data provided by the user and an emotion engine.

[0117] Users input information such as family structure, preferences, contents of their refrigerator, and health status using a dedicated application on their device. The device's built-in camera and microphone are also used by an emotion engine to analyze the user's emotional state based on their tone of voice and facial expressions. Specifically, this might occur if the user says something like, "I'm feeling a little tired today," or if the camera detects signs of stress on the user's face.

[0118] Once the input data and emotional state are collected, the device sends this data to the server. The server analyzes the received information and uses generative artificial intelligence to derive an appropriate recipe. Here, taking into account the results of the emotion recognition, if, for example, the user is determined to be tired, it can suggest a recipe with a simple cooking process and minimal effort.

[0119] For example, for a user who is "hungry but tired," the server can suggest a mild-tasting recipe such as "Creamy Chicken Stew Made in the Microwave." This recipe provides nutrients in a short amount of time.

[0120] The device displays recipe information received from the server on the application screen. Users can then cook and enjoy their meals based on these suggestions. Furthermore, user feedback obtained through the app allows the system to continuously improve its suggestions and enhance the user experience.

[0121] By combining an emotion engine with generative artificial intelligence in this way, it becomes possible to provide flexible and personalized meal suggestions that reflect the user's emotional state, significantly reducing the burden associated with daily meal preparation.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The user launches a dedicated application and enters information about their family structure, preferences, refrigerator contents, and health condition into the terminal. This information can be entered via text or selection options.

[0125] Step 2:

[0126] The user further utilizes the device's camera and microphone, allowing the emotion engine to recognize their current emotional state from their facial expressions and voice. This data, including the user's tone of voice, changes in facial expressions, and gestures, is then analyzed by an emotion analysis algorithm.

[0127] Step 3:

[0128] The terminal combines the information entered by the user with the recognized emotional state and sends this as a data packet to the server. This transmission is encrypted, ensuring the protection of the information.

[0129] Step 4:

[0130] The server analyzes the received data. This analysis first involves formatting the data and identifying each data point. Then, generative artificial intelligence is used to generate a recipe suitable for the data content.

[0131] Step 5:

[0132] The server takes into account the analysis results of the emotion engine and adjusts the resulting recipes according to the user's emotional state. For example, if it is recognized that the user is feeling "I want to relax," the server will prioritize dishes that are expected to have a relaxing effect.

[0133] Step 6:

[0134] The server sends the final recipe to the user's terminal. This data includes cooking instructions, a list of required ingredients, estimated cooking time, and brief advice.

[0135] Step 7:

[0136] The device displays the received recipe information on the application screen. The user can then efficiently proceed with cooking according to this suggestion and complete the actual dish.

[0137] Step 8:

[0138] Users provide feedback on the dishes they create and the suggestions they make through the application. This feedback is sent to the server and used as data to further improve and personalize the service.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] Preparing meals at home presents a significant challenge, as it requires considering the diverse preferences and health conditions of each household member, as well as the user's emotions. For example, when a user is tired, they may need a simple yet nutritionally balanced meal, but searching for and selecting an appropriate recipe is time-consuming and laborious. Therefore, automating recipe suggestions that take the user's emotional state into account is necessary to reduce the user's burden and improve meal satisfaction.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes means for collecting information entered by the user, means for analyzing the emotional state based on the collected information, and means for generating personalized cooking recipes using generative artificial intelligence based on the analyzed emotional state and the entered information. This makes it possible to suggest recipes that take into account the user's emotional state and individual needs.

[0144] "Information entered by users" refers to data on member information, preferences, food possession status, and health status, which users provide to the system via their devices.

[0145] "Methods for analyzing emotional states" refer to technologies that analyze the user's facial expressions and voice tone collected using the camera and microphone installed in the device, and evaluate their emotions at that time.

[0146] "A method for generating personalized cooking recipes using generative artificial intelligence" is a technology that utilizes generative AI models to suggest customized dishes tailored to the user's needs, based on the information received and analyzed emotional data.

[0147] "Means of presenting generated recipes to users" refers to a function that displays the recipes generated on the server on the application screen of the terminal in a format that the user can view.

[0148] This invention is a system that supports meal preparation at home. By integrating emotion recognition and generative artificial intelligence, it aims to make meal preparation more efficient and satisfying by suggesting recipes tailored to the user's needs. This system consists of a terminal used by the user and a server that analyzes the data.

[0149] First, the user uses a dedicated application on the device to input information such as household composition, preferences, types of food in the refrigerator, and health status. The device collects this information and then uses its built-in camera and microphone to analyze the user's facial expressions and voice tone to understand their emotional state.

[0150] Next, the device sends the collected user data and emotional data to the server. The server receives this data and uses generative artificial intelligence to generate personalized recipes that take into account the emotional state and other data content. For example, if the user is tired, the system might suggest a recipe that can be easily made in a microwave.

[0151] The generated recipe is sent from the server to the terminal and displayed on the application screen. Users can follow the instructions on this screen to cook and enjoy a meal with their family. Furthermore, users can provide feedback after cooking, allowing the system to incorporate this feedback and improve the accuracy of its suggestions.

[0152] As a concrete example of its use, a user's prompt message on a given day might be, "Suggest a simple, healthy recipe using the ingredients I have in my refrigerator." This simple request is then transformed by the system into a personalized suggestion that is best suited to the user.

[0153] In this way, this system can transform the daily process of preparing meals into an easier and more enjoyable one by utilizing the latest emotion recognition technology and generative artificial intelligence.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The user launches a dedicated application on their device and inputs information about family members, preferences, food possessions, and health status. This input data includes information such as the number of family members, taste preferences, what's in the refrigerator, and whether they are health-conscious. The device converts this information into digital data and prepares it for analysis of emotional state.

[0157] Step 2:

[0158] The device uses its built-in camera and microphone to analyze the user's emotional state. Specifically, the camera captures the user's facial expressions, and the microphone collects the tone of their voice. Based on this data, the emotion engine determines the user's current emotional state and records it as emotional data. For example, a smile and a calm voice indicate a relaxed state, while a depressed expression and a weak voice indicate a tired state.

[0159] Step 3:

[0160] The terminal combines the input information and the analyzed emotional state into a single data package and sends it to the server via the internet. The transmitted data includes the information entered by the user and the emotional state analyzed by the emotion engine. This package serves as the base data for recipe generation using generative artificial intelligence.

[0161] Step 4:

[0162] The server analyzes the received data package and generates recipes using a generative AI model. Through data analysis, it selects recipes that align with the user's preferences and emotions, and customizes them as needed. For example, if the user is tired, a simple and nutritious recipe will be selected. The generated recipes are created through a different, personalized process for each user.

[0163] Step 5:

[0164] The server sends the generated recipe to the terminal. The receiving terminal displays the recipe on the application screen, allowing the user to check the cooking details. The recipe includes detailed information on the ingredients needed, the procedure, and the cooking time, providing the user with the information to start cooking immediately.

[0165] Step 6:

[0166] The user follows the instructions on the device to cook and enjoy the finished dish.

[0167] Step 7:

[0168] The terminal receives feedback from the user after cooking and sends it to the server. This feedback is used as important data to improve future recipe suggestions. The server receives the feedback and continuously improves the system's recipe generation algorithm and other suggestion methods.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0171] Modern consumers, with their diverse needs and busy lifestyles, demand efficient and personalized meal suggestions. However, traditional meal suggestion systems are based on general information and struggle to consider the emotional state and immediate preferences of individual consumers. Furthermore, real-world store environments lack effective ingredient suggestions and cooking support. Therefore, there is a growing need for a system that accurately understands the emotional state of users and suggests ingredients and cooking methods accordingly.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes means for receiving information input by a user, means for analyzing the input information and the user's emotional state, means for generating ingredient suggestions and cooking procedures using generative artificial intelligence based on the analysis results, means for displaying the generated suggestions and cooking procedures on a display device, and means for receiving user feedback and updating the generative artificial intelligence to improve the suggestions. This makes it possible to suggest appropriate ingredients and provide cooking support according to the user's emotional state even in physical stores.

[0174] A "user" is an individual who operates this system and receives ingredient suggestions and cooking assistance.

[0175] "Information" refers to data entered by users, such as family structure, preferences, food items owned, and physical condition.

[0176] "Emotional state" refers to the psychological state or mood analyzed from the user's facial expressions and voice.

[0177] "Analysis" is the process of collecting and evaluating input information and the user's emotional state as data, and making decisions based on that data.

[0178] "Generative artificial intelligence" is an AI technology that creates appropriate ingredient suggestions and cooking procedures based on input information and analyzed emotional states.

[0179] "Ingredient suggestion" is a process of selecting and recommending ingredients suitable for the user based on the analysis results.

[0180] "Cooking instructions" refer to the cooking methods and processes created by generative artificial intelligence and presented to the user.

[0181] A "display device" is a device that visually shows the generated suggestions and cooking procedures to the user.

[0182] "Feedback" refers to the opinions and evaluations that users give regarding the suggestions and services they have actually received.

[0183] "Updating" refers to the process of improving the accuracy and content of system suggestions based on feedback.

[0184] A "physical store" is an environment where consumers can directly access goods and services at a physical sales location.

[0185] This invention includes a system that provides appropriate ingredients and cooking procedures based on the user's emotional state and input information. The system is primarily implemented through in-store terminals or tablet devices.

[0186] First, users enter their information into tablets or compatible terminals installed in the store. This includes family structure, preferences, food inventory, and physical condition. Furthermore, the user's facial expressions and voice tone are collected using a camera and microphone and analyzed by an emotion engine. This analysis utilizes emotion recognition software such as Google Cloud Vision API and Microsoft Azure's Computer Vision.

[0187] The server receives information and emotional data transmitted from the terminal and performs analysis using generative artificial intelligence. This analysis selects the most suitable ingredients for the user's current emotional state and needs, and generates simple cooking instructions. Ingredient suggestions are made using generative AI (such as OpenAI's GPT model).

[0188] Next, the suggestions generated by the server are displayed in real time on the tablet or device screen. This information allows users to shop more efficiently in-store. Furthermore, feedback from users is received, and the suggestions generated by the artificial intelligence are continuously updated and improved.

[0189] For example, if a user types "I'm a little tired today" into their device and the camera detects a tired expression, the system will suggest a recipe for "tofu and spinach soup" that can be prepared quickly. An example of a prompt to the generating AI would be, "The system has detected that the user is tired. Please generate a recipe for a quick, healthy, and mild-tasting dinner."

[0190] In this way, a system is created that can enhance the shopping experience at physical stores.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The device receives information from the user as input, such as family structure, preferences, food items owned, and physical condition. It also uses its built-in camera and microphone to capture facial expressions and voice tone, processing this as part of the input. Based on this information, the emotion engine analyzes the user's emotional state and outputs it as a variable.

[0194] Step 2:

[0195] The server receives information sent from the terminal and analyzed sentiment data as input. Based on this information, a generative AI model (e.g., OpenAI's GPT model) operates to devise appropriate ingredient selections and cooking procedures. In this process, the generative AI uses prompt statements based on the input data to create appropriate output.

[0196] Step 3:

[0197] The server processes the calculation results of the generative artificial intelligence and obtains suitable ingredient names and cooking procedures as output. In this action, the server compiles specific suggestions tailored to the user's needs and prepares the final output.

[0198] Step 4:

[0199] The terminal receives suggested ingredients and cooking instructions from the server. The terminal displays this information on its screen for the user to review. This display function allows the user to quickly identify the suggested ingredients and proceed with shopping and cooking smoothly.

[0200] Step 5:

[0201] The user actually procures and cooks based on the suggested information and provides feedback on the results. When the terminal receives this feedback, the server uses the data to update the generative artificial intelligence, improving the accuracy of future suggestions.

[0202] Step 6:

[0203] The server analyzes the new feedback data and uses it to retrain or adjust the generated AI model. This improves the overall functionality of the system and enables more adaptive suggestions in the future.

[0204] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0205] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0206] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0210] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0211] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0212] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0213] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0214] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0215] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0216] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0217] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0218] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0219] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0220] This invention provides a system that uses data-driven, generative artificial intelligence to suggest appropriate recipes, thereby efficiently supporting everyday meal preparation at home. The system is built around three elements: a home terminal, a cloud-based server, and the user.

[0221] Users use a device with a dedicated application installed to input data such as their family composition, food inventory, individual preferences, and health status for the day. For example, they might input "2 adults, 1 child" for family composition, "chicken, carrots, potatoes" for food in the refrigerator, and "likes spicy food" for preferences. This data entered by the user on the device is sent to the server.

[0222] The server runs a program to analyze the received data. This program uses generative artificial intelligence to generate the optimal recipe tailored to the user based on the collected information. For example, based on the entered family composition and ingredient data, it might suggest "chicken curry stew" and generate a recipe with easy-to-follow instructions for microwave cooking.

[0223] The device displays recipe information sent from the server to the user. This allows the user to easily obtain the information needed to prepare meals for the day, reducing the burden of cooking. Furthermore, users can provide feedback through the app, enabling the system to continuously improve its performance.

[0224] This system plays a crucial role in providing menus that ensure users receive a balanced intake of necessary nutrients, especially when they are busy or feeling unwell. In this way, it makes it possible to prepare daily meals more efficiently while considering the health of the entire family.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The user launches a dedicated application and enters information about their family composition, preferences, the contents of their refrigerator, and their physical condition for the day. This information is entered using pull-down menus and text input fields.

[0228] Step 2:

[0229] The terminal formats the data entered by the user in bulk and sends it to the server as a JSON data packet. During this process, the communication is encrypted to protect user information.

[0230] Step 3:

[0231] The server decompresses the received data packets to analyze them, identifying and separating each information element. This process verifies the integrity and completeness of the data.

[0232] Step 4:

[0233] The server activates a generative artificial intelligence algorithm based on the analyzed data. This AI searches for and filters recipes that are tailored to the type of ingredients, health condition, and preferences.

[0234] Step 5:

[0235] The server customizes the generated candidate recipes to their optimal form. This customization is based on the cooking time, cooking method, and nutritional balance of the dishes.

[0236] Step 6:

[0237] The server selects the final recipe to suggest to the user and sends it to the terminal in a documented format. The data sent includes cooking instructions, details of required ingredients, and an estimated cooking time.

[0238] Step 7:

[0239] The terminal displays recipe information received from the server on the application screen. Based on this information, the user can begin preparing the specific dish.

[0240] Step 8:

[0241] Users perform cooking and, if necessary, provide feedback on the results and their satisfaction level within the application. This feedback is sent to the server to improve the service in the future.

[0242] (Example 1)

[0243] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0244] In today's busy society, preparing daily meals at home is a burden for many people. Furthermore, planning nutritionally balanced meals and selecting dishes to suit specific preferences is not easy. Moreover, while providing meals that are appropriate for one's health needs is crucial amidst busy schedules, achieving this with limited information and time is difficult. This invention aims to support efficient meal preparation at home by quickly and accurately proposing optimal meal configurations based on the specific circumstances of each household, thereby addressing these challenges.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0246] In this invention, the server includes means for receiving information provided by the user, means for generating meal planning procedures based on the information using generative artificial intelligence, and means for transmitting the generated procedures to the user through a display device. This makes it possible for the user to easily plan and prepare efficient and balanced meals tailored to their individual preferences and health conditions.

[0247] "Users" refer to individuals who use the system to provide information and utilize the suggestions they receive.

[0248] "Information" refers to data that the system receives from users, such as demographics, preferences, food possessions, and health status.

[0249] "Generative artificial intelligence" refers to a technology that analyzes received information and generates solutions and suggestions tailored to individual conditions.

[0250] "Meal preparation instructions" refer to a set of instructions that outlines the preparation and cooking of a meal using specific ingredients and seasonings.

[0251] A "display device" refers to equipment or screens used to visually convey information and suggestions obtained from a system to the user.

[0252] "Evaluation" refers to feedback that users provide regarding the information provided by the system, and this data is useful for improving the system.

[0253] This invention is a system designed to streamline everyday meal preparation at home. The system utilizes a terminal, a cloud-based server, and a generative AI model. It uses user-input data to automatically generate optimal meal recipes tailored to individual circumstances and preferences.

[0254] Users install a dedicated application on their home device. Through this application, users input information such as family composition, a list of ingredients they own, their preferences, and their health status. For example, they can input a family composition of "2 adults and 1 child," an inventory of ingredients such as "chicken, carrots, and potatoes," and a preference for "spicy food." This information is transmitted from the device to a server in the cloud.

[0255] The server drives a generative AI model based on the received data and generates recipes based on the provided conditions. This AI model has an algorithm that identifies the most suitable meal configuration for the user from specific information.

[0256] The generated recipe is sent to the device, including cooking instructions and necessary nutritional information. The device displays this information to the user in a visually easy-to-understand format. This allows the user to easily and quickly obtain guidance for preparing daily meals.

[0257] As a concrete example, here is an example of a prompt statement:

[0258] "Today I'm preparing a meal for a family of two adults and one child. I have chicken, carrots, and potatoes in the refrigerator. I like spicy food, so I'd appreciate some simple recipes using these ingredients."

[0259] In this way, the system provides an environment in which users can efficiently and effectively create meal plans tailored to their individual needs.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] Users launch a dedicated application on their home device and input information about their family structure, available ingredients, preferences, and health status. This input data includes details such as "2 adults, 1 child," "chicken, carrots, potatoes," and "likes spicy food." This information serves as the basis for generating the optimal recipe in subsequent processing.

[0263] Step 2:

[0264] The terminal sends information entered by the user to a cloud-based server. The data processing performed here involves the terminal formatting the input data into a standardized format and transferring it to the server using a secure communication protocol. As a result of the transmission, information that meets the user's requirements is collected on the server side.

[0265] Step 3:

[0266] The server analyzes the received data and generates the optimal meal plan using a generative AI model. First, the server sets the input family composition and preferences as parameters for the AI ​​model and designs a recipe suitable for the user while referring to the ingredient database. This data calculation generates a specific recipe suggestion, such as "Chicken Curry Stew." The generated recipe, along with detailed cooking instructions, is then passed on to the next step.

[0267] Step 4:

[0268] The terminal receives recipes sent from the server and presents them to the user in a visually appealing and user-friendly interface. Specifically, the terminal displays the cooking process of the recipe step by step, making it easy for the user to follow. The user can then prepare a meal based on this information.

[0269] Step 5:

[0270] After completing a dish, users provide feedback to the system through the application. This feedback includes aspects such as "satisfaction with the dish" and "areas for improvement," and is used to enhance the accuracy of future recipe generation. This feedback input enables the system to continuously improve.

[0271] (Application Example 1)

[0272] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] Traditional food delivery services have faced challenges in providing personalized meal suggestions based on users' preferences and health conditions, making it difficult for them to choose appropriate dishes. Furthermore, ordering meals that meet specific criteria is difficult amidst busy daily lives.

[0274] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0275] In this invention, the server includes means for receiving data entered by a user, means for constructing dish suggestions using generative artificial intelligence based on the entered data, and means for presenting the suggested dishes to the user. This enables the suggestion of dishes tailored to the user's individual needs and allows for easy ordering.

[0276] "User" refers to an entity that uses the system to receive dish suggestions or place orders.

[0277] "Data" refers to information necessary to generate cooking suggestions, such as member information, preferences, food possessions, and health status, which are entered by the user.

[0278] "Generative artificial intelligence" refers to a machine learning model or algorithm that generates optimal cooking suggestions based on data received from users.

[0279] "Cooking suggestions" refers to meal options or recipes created by generative artificial intelligence and presented to the user.

[0280] A "terminal" refers to an electronic device used by users to operate the system, receive food suggestions, and place orders.

[0281] "Available for order" refers to a state where users can proceed directly to the purchase process for the suggested dishes within the system.

[0282] The system implementing this invention provides users with an electronic device such as a smartphone as a terminal. The terminal communicates with a cloud-based server, receives user input data, and performs the necessary processing. Users can easily input data such as their family members, preferences, food possessions, and health status through an application.

[0283] First, the application installed on the terminal operates. The application sends the input data to the cloud server, and the server uses generative artificial intelligence based on this to generate proposals for the most suitable dishes for each user. The generated proposals are sent back to the terminal and presented to the user.

[0284] The server is built on a cloud platform such as Amazon Web Services or Google Cloud, and as a generative artificial intelligence model, it can utilize APIs provided by companies such as OpenAI and Google. With this system, users can quickly find dishes that suit their preferences and situations and order those dishes on the terminal.

[0285] As a specific example, in a family with elementary school children who are looking for nutritious and spicy dishes, this system can propose "Spicy Tofu Chili" as a candidate for dishes. And the user can easily confirm the order as it is.

[0286] As an example of a prompt sentence for the generative AI model, there is a sentence like "A 30-year-old woman living in Tokyo, likes spicy dishes, allergy: nuts. Propose a healthy and easily orderable menu." This enables the generative artificial intelligence to make appropriate proposals and expand the options for users to choose from when it comes to dishes.

[0287] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The user launches the application on the terminal and inputs data such as family member information, preferences, available foods, and health status. The input data is appropriately formatted within the application and prepared for transmission to the server.

[0290] Step 2:

[0291] The device sends formatted user data to a cloud-based server. The data is securely transmitted via a secure protocol and stored in a database on the server side.

[0292] Step 3:

[0293] The server inputs the received data into the generative AI model and begins analyzing the data. The generative AI model forms optimal suggestions based on the input data. In this process, data pattern analysis and contextual understanding are performed, and the cooking suggestions that best suit the user's requests are generated.

[0294] Step 4:

[0295] The AI ​​model generates a cooking suggestion on the server. This suggestion includes the dish name, cooking outline, and required ingredients, and is formatted for presentation to the user.

[0296] Step 5:

[0297] The server sends the generated recipe suggestions to the terminal. The transmitted data is received by the user's terminal, and the application reads it and prepares to display it on the user interface.

[0298] Step 6:

[0299] Users review the dish suggestions displayed on the terminal and select their preferred dishes. The selected dishes are then prepared for purchase on the terminal. During this process, they confirm the order details and enter their delivery information.

[0300] Step 7:

[0301] Once an order is confirmed, the terminal sends the order data to the server, initiating the food delivery process. The server then makes the appropriate delivery arrangements and issues instructions for cooking and delivery of the food.

[0302] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0303] This invention integrates emotion recognition functionality into a system that supports home cooking, thereby improving user satisfaction and cooking efficiency by providing suggestions more tailored to individual needs. The system generates, customizes, and presents recipes based on data provided by the user and an emotion engine.

[0304] Users input information such as family structure, preferences, contents of their refrigerator, and health status using a dedicated application on their device. The device's built-in camera and microphone are also used by an emotion engine to analyze the user's emotional state based on their tone of voice and facial expressions. Specifically, this might occur if the user says something like, "I'm feeling a little tired today," or if the camera detects signs of stress on the user's face.

[0305] Once the input data and emotional state are collected, the device sends this data to the server. The server analyzes the received information and uses generative artificial intelligence to derive an appropriate recipe. Here, taking into account the results of the emotion recognition, if, for example, the user is determined to be tired, it can suggest a recipe with a simple cooking process and minimal effort.

[0306] For example, for a user who is "hungry but tired," the server can suggest a mild-tasting recipe such as "Creamy Chicken Stew Made in the Microwave." This recipe provides nutrients in a short amount of time.

[0307] The device displays recipe information received from the server on the application screen. Users can then cook and enjoy their meals based on these suggestions. Furthermore, user feedback obtained through the app allows the system to continuously improve its suggestions and enhance the user experience.

[0308] By combining the emotion engine and generative artificial intelligence in this way, it becomes possible to provide flexible and personalized meal suggestions that reflect the user's emotional state, significantly reducing the burden associated with daily meal preparation.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] The user launches a dedicated application and inputs information about family composition, preferences, ingredients in the refrigerator, and physical condition into the terminal. These can be input through text or selection options.

[0312] Step 2:

[0313] The user further uses the camera or microphone of the terminal, and the emotion engine recognizes the current emotional state from facial expressions and voice. This data is judged by an emotion analysis algorithm based on the user's tone of voice, changes in facial expressions, and gestures.

[0314] Step 3:

[0315] The terminal combines the information input by the user and the recognized emotional state, and transmits this as a data packet to the server. This transmission is encrypted to ensure the protection of information.

[0316] Step 4:

[0317] The server analyzes the received data. The analysis includes a process of first formatting the data and identifying each data point. Then, using generative artificial intelligence, a recipe for a dish suitable for the data content is generated.

[0318] Step 5:

[0319] The server takes into account the analysis results of the emotion engine and adjusts the resulting recipes according to the user's emotional state. For example, if it is recognized that the user is feeling "I want to relax," the server will prioritize dishes that are expected to have a relaxing effect.

[0320] Step 6:

[0321] The server sends the final recipe to the user's terminal. This data includes cooking instructions, a list of required ingredients, estimated cooking time, and brief advice.

[0322] Step 7:

[0323] The device displays the received recipe information on the application screen. The user can then efficiently proceed with cooking according to this suggestion and complete the actual dish.

[0324] Step 8:

[0325] Users provide feedback on the dishes they create and the suggestions they make through the application. This feedback is sent to the server and used as data to further improve and personalize the service.

[0326] (Example 2)

[0327] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0328] Preparing meals at home presents a significant challenge, as it requires considering the diverse preferences and health conditions of each household member, as well as the user's emotions. For example, when a user is tired, they may need a simple yet nutritionally balanced meal, but searching for and selecting an appropriate recipe is time-consuming and laborious. Therefore, automating recipe suggestions that take the user's emotional state into account is necessary to reduce the user's burden and improve meal satisfaction.

[0329] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0330] In this invention, the server includes means for collecting information entered by the user, means for analyzing the emotional state based on the collected information, and means for generating personalized cooking recipes using generative artificial intelligence based on the analyzed emotional state and the entered information. This makes it possible to suggest recipes that take into account the user's emotional state and individual needs.

[0331] "Information entered by users" refers to data on member information, preferences, food possession status, and health status, which users provide to the system via their devices.

[0332] "Methods for analyzing emotional states" refer to technologies that analyze the user's facial expressions and voice tone collected using the camera and microphone installed in the device, and evaluate their emotions at that time.

[0333] "A method for generating personalized cooking recipes using generative artificial intelligence" is a technology that utilizes generative AI models to suggest customized dishes tailored to the user's needs, based on the information received and analyzed emotional data.

[0334] "Means of presenting generated recipes to users" refers to a function that displays the recipes generated on the server on the application screen of the terminal in a format that the user can view.

[0335] This invention is a system that supports meal preparation at home. By integrating emotion recognition and generative artificial intelligence, it aims to make meal preparation more efficient and satisfying by suggesting recipes tailored to the user's needs. This system consists of a terminal used by the user and a server that analyzes the data.

[0336] First, the user uses a dedicated application on the device to input information such as household composition, preferences, types of food in the refrigerator, and health status. The device collects this information and then uses its built-in camera and microphone to analyze the user's facial expressions and voice tone to understand their emotional state.

[0337] Next, the device sends the collected user data and emotional data to the server. The server receives this data and uses generative artificial intelligence to generate personalized recipes that take into account the emotional state and other data content. For example, if the user is tired, the system might suggest a recipe that can be easily made in a microwave.

[0338] The generated recipe is sent from the server to the terminal and displayed on the application screen. Users can follow the instructions on this screen to cook and enjoy a meal with their family. Furthermore, users can provide feedback after cooking, allowing the system to incorporate this feedback and improve the accuracy of its suggestions.

[0339] As a concrete example of its use, a user's prompt message on a given day might be, "Suggest a simple, healthy recipe using the ingredients I have in my refrigerator." This simple request is then transformed by the system into a personalized suggestion that is best suited to the user.

[0340] In this way, this system can transform the daily process of preparing meals into an easier and more enjoyable one by utilizing the latest emotion recognition technology and generative artificial intelligence.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The user launches a dedicated application on their device and inputs information about family members, preferences, food possessions, and health status. This input data includes information such as the number of family members, taste preferences, what's in the refrigerator, and whether they are health-conscious. The device converts this information into digital data and prepares it for analysis of emotional state.

[0344] Step 2:

[0345] The device uses its built-in camera and microphone to analyze the user's emotional state. Specifically, the camera captures the user's facial expressions, and the microphone collects the tone of their voice. Based on this data, the emotion engine determines the user's current emotional state and records it as emotional data. For example, a smile and a calm voice indicate a relaxed state, while a depressed expression and a weak voice indicate a tired state.

[0346] Step 3:

[0347] The terminal combines the input information and the analyzed emotional state into a single data package and sends it to the server via the internet. The transmitted data includes the information entered by the user and the emotional state analyzed by the emotion engine. This package serves as the base data for recipe generation using generative artificial intelligence.

[0348] Step 4:

[0349] The server analyzes the received data package and generates recipes using a generative AI model. Through data analysis, it selects recipes that align with the user's preferences and emotions, and customizes them as needed. For example, if the user is tired, a simple and nutritious recipe will be selected. The generated recipes are created through a different, personalized process for each user.

[0350] Step 5:

[0351] The server sends the generated recipe to the terminal. The receiving terminal displays the recipe on the application screen, allowing the user to check the cooking details. The recipe includes detailed information on the ingredients needed, the procedure, and the cooking time, providing the user with the information to start cooking immediately.

[0352] Step 6:

[0353] The user follows the instructions on the device to cook and enjoy the finished dish.

[0354] Step 7:

[0355] The terminal receives feedback from the user after cooking and sends it to the server. This feedback is used as important data to improve future recipe suggestions. The server receives the feedback and continuously improves the system's recipe generation algorithm and other suggestion methods.

[0356] (Application Example 2)

[0357] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0358] Modern consumers, with their diverse needs and busy lifestyles, demand efficient and personalized meal suggestions. However, traditional meal suggestion systems are based on general information and struggle to consider the emotional state and immediate preferences of individual consumers. Furthermore, real-world store environments lack effective ingredient suggestions and cooking support. Therefore, there is a growing need for a system that accurately understands the emotional state of users and suggests ingredients and cooking methods accordingly.

[0359] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0360] In this invention, the server includes means for receiving information input by a user, means for analyzing the input information and the user's emotional state, means for generating ingredient suggestions and cooking procedures using generative artificial intelligence based on the analysis results, means for displaying the generated suggestions and cooking procedures on a display device, and means for receiving user feedback and updating the generative artificial intelligence to improve the suggestions. This makes it possible to suggest appropriate ingredients and provide cooking support according to the user's emotional state even in physical stores.

[0361] A "user" is an individual who operates this system and receives ingredient suggestions and cooking assistance.

[0362] "Information" refers to data entered by users, such as family structure, preferences, food items owned, and physical condition.

[0363] "Emotional state" refers to the psychological state or mood analyzed from the user's facial expressions and voice.

[0364] "Analysis" is the process of collecting and evaluating input information and the user's emotional state as data, and making decisions based on that data.

[0365] "Generative artificial intelligence" is an AI technology that creates appropriate ingredient suggestions and cooking procedures based on input information and analyzed emotional states.

[0366] "Ingredient suggestion" is a process of selecting and recommending ingredients suitable for the user based on the analysis results.

[0367] "Cooking instructions" refer to the cooking methods and processes created by generative artificial intelligence and presented to the user.

[0368] A "display device" is a device that visually shows the generated suggestions and cooking procedures to the user.

[0369] "Feedback" refers to the opinions and evaluations that users give regarding the suggestions and services they have actually received.

[0370] "Updating" refers to the process of improving the accuracy and content of system suggestions based on feedback.

[0371] A "physical store" is an environment where consumers can directly access goods and services at a physical sales location.

[0372] This invention includes a system that provides appropriate ingredients and cooking procedures based on the user's emotional state and input information. The system is primarily implemented through in-store terminals or tablet devices.

[0373] First, users enter their information into tablets or compatible terminals installed in the store. This includes family structure, preferences, food inventory, and physical condition. Furthermore, the user's facial expressions and voice tone are collected using a camera and microphone and analyzed by an emotion engine. This analysis utilizes emotion recognition software such as Google Cloud Vision API and Microsoft Azure's Computer Vision.

[0374] The server receives information and emotional data transmitted from the terminal and performs analysis using generative artificial intelligence. This analysis selects the most suitable ingredients for the user's current emotional state and needs, and generates simple cooking instructions. Ingredient suggestions are made using generative AI (such as OpenAI's GPT model).

[0375] Next, the suggestions generated by the server are displayed in real time on the tablet or device screen. This information allows users to shop more efficiently in-store. Furthermore, feedback from users is received, and the suggestions generated by the artificial intelligence are continuously updated and improved.

[0376] For example, if a user types "I'm a little tired today" into their device and the camera detects a tired expression, the system will suggest a recipe for "tofu and spinach soup" that can be prepared quickly. An example of a prompt to the generating AI would be, "The system has detected that the user is tired. Please generate a recipe for a quick, healthy, and mild-tasting dinner."

[0377] In this way, a system is created that can enhance the shopping experience at physical stores.

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The device receives information from the user as input, such as family structure, preferences, food items owned, and physical condition. It also uses its built-in camera and microphone to capture facial expressions and voice tone, processing this as part of the input. Based on this information, the emotion engine analyzes the user's emotional state and outputs it as a variable.

[0381] Step 2:

[0382] The server receives information sent from the terminal and analyzed sentiment data as input. Based on this information, a generative AI model (e.g., OpenAI's GPT model) operates to devise appropriate ingredient selections and cooking procedures. In this process, the generative AI uses prompt statements based on the input data to create appropriate output.

[0383] Step 3:

[0384] The server processes the calculation results of the generative artificial intelligence and obtains suitable ingredient names and cooking procedures as output. In this action, the server compiles specific suggestions tailored to the user's needs and prepares the final output.

[0385] Step 4:

[0386] The terminal receives suggested ingredients and cooking instructions from the server. The terminal displays this information on its screen for the user to review. This display function allows the user to quickly identify the suggested ingredients and proceed with shopping and cooking smoothly.

[0387] Step 5:

[0388] The user actually procures and cooks based on the suggested information and provides feedback on the results. When the terminal receives this feedback, the server uses the data to update the generative artificial intelligence, improving the accuracy of future suggestions.

[0389] Step 6:

[0390] The server analyzes the new feedback data and uses it to retrain or adjust the generated AI model. This improves the overall functionality of the system and enables more adaptive suggestions in the future.

[0391] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0392] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0393] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0394] [Third Embodiment]

[0395] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0396] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0397] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0398] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0399] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0400] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0401] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0402] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0403] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0404] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0405] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0406] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0407] This invention provides a system that uses data-driven, generative artificial intelligence to suggest appropriate recipes, thereby efficiently supporting everyday meal preparation at home. The system is built around three elements: a home terminal, a cloud-based server, and the user.

[0408] Users use a device with a dedicated application installed to input data such as their family composition, food inventory, individual preferences, and health status for the day. For example, they might input "2 adults, 1 child" for family composition, "chicken, carrots, potatoes" for food in the refrigerator, and "likes spicy food" for preferences. This data entered by the user on the device is sent to the server.

[0409] The server runs a program to analyze the received data. This program uses generative artificial intelligence to generate the optimal recipe tailored to the user based on the collected information. For example, based on the entered family composition and ingredient data, it might suggest "chicken curry stew" and generate a recipe with easy-to-follow instructions for microwave cooking.

[0410] The device displays recipe information sent from the server to the user. This allows the user to easily obtain the information needed to prepare meals for the day, reducing the burden of cooking. Furthermore, users can provide feedback through the app, enabling the system to continuously improve its performance.

[0411] This system plays a crucial role in providing menus that ensure users receive a balanced intake of necessary nutrients, especially when they are busy or feeling unwell. In this way, it makes it possible to prepare daily meals more efficiently while considering the health of the entire family.

[0412] The following describes the processing flow.

[0413] Step 1:

[0414] The user launches a dedicated application and enters information about their family composition, preferences, the contents of their refrigerator, and their physical condition for the day. This information is entered using pull-down menus and text input fields.

[0415] Step 2:

[0416] The terminal formats the data entered by the user in bulk and sends it to the server as a JSON data packet. During this process, the communication is encrypted to protect user information.

[0417] Step 3:

[0418] The server decompresses the received data packets to analyze them, identifying and separating each information element. This process verifies the integrity and completeness of the data.

[0419] Step 4:

[0420] The server activates a generative artificial intelligence algorithm based on the analyzed data. This AI searches for and filters recipes that are tailored to the type of ingredients, health condition, and preferences.

[0421] Step 5:

[0422] The server customizes the generated candidate recipes to their optimal form. This customization is based on the cooking time, cooking method, and nutritional balance of the dishes.

[0423] Step 6:

[0424] The server selects the final recipe to suggest to the user and sends it to the terminal in a documented format. The data sent includes cooking instructions, details of required ingredients, and an estimated cooking time.

[0425] Step 7:

[0426] The terminal displays recipe information received from the server on the application screen. Based on this information, the user can begin preparing the specific dish.

[0427] Step 8:

[0428] Users perform cooking and, if necessary, provide feedback on the results and their satisfaction level within the application. This feedback is sent to the server to improve the service in the future.

[0429] (Example 1)

[0430] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0431] In today's busy society, preparing daily meals at home is a burden for many people. Furthermore, planning nutritionally balanced meals and selecting dishes to suit specific preferences is not easy. Moreover, while providing meals that are appropriate for one's health needs is crucial amidst busy schedules, achieving this with limited information and time is difficult. This invention aims to support efficient meal preparation at home by quickly and accurately proposing optimal meal configurations based on the specific circumstances of each household, thereby addressing these challenges.

[0432] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0433] In this invention, the server includes means for receiving information provided by the user, means for generating meal planning procedures based on the information using generative artificial intelligence, and means for transmitting the generated procedures to the user through a display device. This makes it possible for the user to easily plan and prepare efficient and balanced meals tailored to their individual preferences and health conditions.

[0434] "Users" refer to individuals who use the system to provide information and utilize the suggestions they receive.

[0435] "Information" refers to data that the system receives from users, such as demographics, preferences, food possessions, and health status.

[0436] "Generative artificial intelligence" refers to a technology that analyzes received information and generates solutions and suggestions tailored to individual conditions.

[0437] "Meal preparation instructions" refer to a set of instructions that outlines the preparation and cooking of a meal using specific ingredients and seasonings.

[0438] A "display device" refers to equipment or screens used to visually convey information and suggestions obtained from a system to the user.

[0439] "Evaluation" refers to feedback that users provide regarding the information provided by the system, and this data is useful for improving the system.

[0440] This invention is a system designed to streamline everyday meal preparation at home. The system utilizes a terminal, a cloud-based server, and a generative AI model. It uses user-input data to automatically generate optimal meal recipes tailored to individual circumstances and preferences.

[0441] Users install a dedicated application on their home device. Through this application, users input information such as family composition, a list of ingredients they own, their preferences, and their health status. For example, they can input a family composition of "2 adults and 1 child," an inventory of ingredients such as "chicken, carrots, and potatoes," and a preference for "spicy food." This information is transmitted from the device to a server in the cloud.

[0442] The server drives a generative AI model based on the received data and generates recipes based on the provided conditions. This AI model has an algorithm that identifies the most suitable meal configuration for the user from specific information.

[0443] The generated recipe is sent to the device, including cooking instructions and necessary nutritional information. The device displays this information to the user in a visually easy-to-understand format. This allows the user to easily and quickly obtain guidance for preparing daily meals.

[0444] As a concrete example, here is an example of a prompt statement:

[0445] "Today I'm preparing a meal for a family of two adults and one child. I have chicken, carrots, and potatoes in the refrigerator. I like spicy food, so I'd appreciate some simple recipes using these ingredients."

[0446] In this way, the system provides an environment in which users can efficiently and effectively create meal plans tailored to their individual needs.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] Users launch a dedicated application on their home device and input information about their family structure, available ingredients, preferences, and health status. This input data includes details such as "2 adults, 1 child," "chicken, carrots, potatoes," and "likes spicy food." This information serves as the basis for generating the optimal recipe in subsequent processing.

[0450] Step 2:

[0451] The terminal sends information entered by the user to a cloud-based server. The data processing performed here involves the terminal formatting the input data into a standardized format and transferring it to the server using a secure communication protocol. As a result of the transmission, information that meets the user's requirements is collected on the server side.

[0452] Step 3:

[0453] The server analyzes the received data and generates the optimal meal plan using a generative AI model. First, the server sets the input family composition and preferences as parameters for the AI ​​model and designs a recipe suitable for the user while referring to the ingredient database. This data calculation generates a specific recipe suggestion, such as "Chicken Curry Stew." The generated recipe, along with detailed cooking instructions, is then passed on to the next step.

[0454] Step 4:

[0455] The terminal receives recipes sent from the server and presents them to the user in a visually appealing and user-friendly interface. Specifically, the terminal displays the cooking process of the recipe step by step, making it easy for the user to follow. The user can then prepare a meal based on this information.

[0456] Step 5:

[0457] After completing a dish, users provide feedback to the system through the application. This feedback includes aspects such as "satisfaction with the dish" and "areas for improvement," and is used to enhance the accuracy of future recipe generation. This feedback input enables the system to continuously improve.

[0458] (Application Example 1)

[0459] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0460] Traditional food delivery services have faced challenges in providing personalized meal suggestions based on users' preferences and health conditions, making it difficult for them to choose appropriate dishes. Furthermore, ordering meals that meet specific criteria is difficult amidst busy daily lives.

[0461] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0462] In this invention, the server includes means for receiving data entered by a user, means for constructing dish suggestions using generative artificial intelligence based on the entered data, and means for presenting the suggested dishes to the user. This enables the suggestion of dishes tailored to the user's individual needs and allows for easy ordering.

[0463] "User" refers to an entity that uses the system to receive dish suggestions or place orders.

[0464] "Data" refers to information necessary to generate cooking suggestions, such as member information, preferences, food possessions, and health status, which are entered by the user.

[0465] "Generative artificial intelligence" refers to a machine learning model or algorithm that generates optimal cooking suggestions based on data received from users.

[0466] "Cooking suggestions" refers to meal options or recipes created by generative artificial intelligence and presented to the user.

[0467] A "terminal" refers to an electronic device used by users to operate the system, receive food suggestions, and place orders.

[0468] "Available for order" refers to a state where users can proceed directly to the purchase process for the suggested dishes within the system.

[0469] The system implementing this invention provides users with an electronic device such as a smartphone as a terminal. The terminal communicates with a cloud-based server, receives user input data, and performs the necessary processing. Users can easily input data such as their family members, preferences, food possessions, and health status through an application.

[0470] First, the application installed on the device starts running. The application sends the entered data to a cloud server, which then uses generative artificial intelligence to generate personalized cooking suggestions for each user. The generated suggestions are then sent back to the device and presented to the user.

[0471] The servers are built on cloud platforms such as Amazon Web Services and Google Cloud, and can utilize APIs provided by companies like OpenAI and Google as generative artificial intelligence models. This system allows users to quickly find dishes that perfectly match their preferences and circumstances and order them on their devices.

[0472] For example, if a family with elementary school-aged children is looking for a nutritious and spicy meal, this system can suggest "Spicy Tofu Chili" as a dish option. The user can then easily confirm their order.

[0473] An example of a prompt sentence for a generative AI model is: "Female, 30s, lives in Tokyo, likes spicy food, allergy: nuts. Suggest healthy and easy-to-order menu items." This allows the generative artificial intelligence to make appropriate suggestions and broaden the user's culinary options.

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] The user launches the application on their device and enters data such as member information, preferences, food possessions, and health status. The entered data is appropriately formatted within the application and prepared for transmission to the server.

[0477] Step 2:

[0478] The device sends formatted user data to a cloud-based server. The data is securely transmitted via a secure protocol and stored in a database on the server side.

[0479] Step 3:

[0480] The server inputs the received data into the generative AI model and begins analyzing the data. The generative AI model forms optimal suggestions based on the input data. In this process, data pattern analysis and contextual understanding are performed, and the cooking suggestions that best suit the user's requests are generated.

[0481] Step 4:

[0482] The AI ​​model generates a cooking suggestion on the server. This suggestion includes the dish name, cooking outline, and required ingredients, and is formatted for presentation to the user.

[0483] Step 5:

[0484] The server sends the generated recipe suggestions to the terminal. The transmitted data is received by the user's terminal, and the application reads it and prepares to display it on the user interface.

[0485] Step 6:

[0486] Users review the dish suggestions displayed on the terminal and select their preferred dishes. The selected dishes are then prepared for purchase on the terminal. During this process, they confirm the order details and enter their delivery information.

[0487] Step 7:

[0488] Once an order is confirmed, the terminal sends the order data to the server, initiating the food delivery process. The server then makes the appropriate delivery arrangements and issues instructions for cooking and delivery of the food.

[0489] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0490] This invention integrates emotion recognition functionality into a system that supports home cooking, thereby improving user satisfaction and cooking efficiency by providing suggestions more tailored to individual needs. The system generates, customizes, and presents recipes based on data provided by the user and an emotion engine.

[0491] Users input information such as family structure, preferences, contents of their refrigerator, and health status using a dedicated application on their device. The device's built-in camera and microphone are also used by an emotion engine to analyze the user's emotional state based on their tone of voice and facial expressions. Specifically, this might occur if the user says something like, "I'm feeling a little tired today," or if the camera detects signs of stress on the user's face.

[0492] Once the input data and emotional state are collected, the device sends this data to the server. The server analyzes the received information and uses generative artificial intelligence to derive an appropriate recipe. Here, taking into account the results of the emotion recognition, if, for example, the user is determined to be tired, it can suggest a recipe with a simple cooking process and minimal effort.

[0493] For example, for a user who is "hungry but tired," the server can suggest a mild-tasting recipe such as "Creamy Chicken Stew Made in the Microwave." This recipe provides nutrients in a short amount of time.

[0494] The device displays recipe information received from the server on the application screen. Users can then cook and enjoy their meals based on these suggestions. Furthermore, user feedback obtained through the app allows the system to continuously improve its suggestions and enhance the user experience.

[0495] By combining an emotion engine with generative artificial intelligence in this way, it becomes possible to provide flexible and personalized meal suggestions that reflect the user's emotional state, significantly reducing the burden associated with daily meal preparation.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The user launches a dedicated application and enters information about their family structure, preferences, refrigerator contents, and health condition into the terminal. This information can be entered via text or selection options.

[0499] Step 2:

[0500] The user further utilizes the device's camera and microphone, allowing the emotion engine to recognize their current emotional state from their facial expressions and voice. This data, including the user's tone of voice, changes in facial expressions, and gestures, is then analyzed by an emotion analysis algorithm.

[0501] Step 3:

[0502] The terminal combines the information entered by the user with the recognized emotional state and sends this as a data packet to the server. This transmission is encrypted, ensuring the protection of the information.

[0503] Step 4:

[0504] The server analyzes the received data. This analysis first involves formatting the data and identifying each data point. Then, generative artificial intelligence is used to generate a recipe suitable for the data content.

[0505] Step 5:

[0506] The server takes into account the analysis results of the emotion engine and adjusts the resulting recipes according to the user's emotional state. For example, if it is recognized that the user is feeling "I want to relax," the server will prioritize dishes that are expected to have a relaxing effect.

[0507] Step 6:

[0508] The server sends the final recipe to the user's terminal. This data includes cooking instructions, a list of required ingredients, estimated cooking time, and brief advice.

[0509] Step 7:

[0510] The device displays the received recipe information on the application screen. The user can then efficiently proceed with cooking according to this suggestion and complete the actual dish.

[0511] Step 8:

[0512] Users provide feedback on the dishes they create and the suggestions they make through the application. This feedback is sent to the server and used as data to further improve and personalize the service.

[0513] (Example 2)

[0514] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] Preparing meals at home presents a significant challenge, as it requires considering the diverse preferences and health conditions of each household member, as well as the user's emotions. For example, when a user is tired, they may need a simple yet nutritionally balanced meal, but searching for and selecting an appropriate recipe is time-consuming and laborious. Therefore, automating recipe suggestions that take the user's emotional state into account is necessary to reduce the user's burden and improve meal satisfaction.

[0516] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0517] In this invention, the server includes means for collecting information entered by the user, means for analyzing the emotional state based on the collected information, and means for generating personalized cooking recipes using generative artificial intelligence based on the analyzed emotional state and the entered information. This makes it possible to suggest recipes that take into account the user's emotional state and individual needs.

[0518] "Information entered by users" refers to data on member information, preferences, food possession status, and health status, which users provide to the system via their devices.

[0519] "Methods for analyzing emotional states" refer to technologies that analyze the user's facial expressions and voice tone collected using the camera and microphone installed in the device, and evaluate their emotions at that time.

[0520] "A method for generating personalized cooking recipes using generative artificial intelligence" is a technology that utilizes generative AI models to suggest customized dishes tailored to the user's needs, based on the information received and analyzed emotional data.

[0521] "Means of presenting generated recipes to users" refers to a function that displays the recipes generated on the server on the application screen of the terminal in a format that the user can view.

[0522] This invention is a system that supports meal preparation at home. By integrating emotion recognition and generative artificial intelligence, it aims to make meal preparation more efficient and satisfying by suggesting recipes tailored to the user's needs. This system consists of a terminal used by the user and a server that analyzes the data.

[0523] First, the user uses a dedicated application on the device to input information such as household composition, preferences, types of food in the refrigerator, and health status. The device collects this information and then uses its built-in camera and microphone to analyze the user's facial expressions and voice tone to understand their emotional state.

[0524] Next, the device sends the collected user data and emotional data to the server. The server receives this data and uses generative artificial intelligence to generate personalized recipes that take into account the emotional state and other data content. For example, if the user is tired, the system might suggest a recipe that can be easily made in a microwave.

[0525] The generated recipe is sent from the server to the terminal and displayed on the application screen. Users can follow the instructions on this screen to cook and enjoy a meal with their family. Furthermore, users can provide feedback after cooking, allowing the system to incorporate this feedback and improve the accuracy of its suggestions.

[0526] As a concrete example of its use, a user's prompt message on a given day might be, "Suggest a simple, healthy recipe using the ingredients I have in my refrigerator." This simple request is then transformed by the system into a personalized suggestion that is best suited to the user.

[0527] In this way, this system can transform the daily process of preparing meals into an easier and more enjoyable one by utilizing the latest emotion recognition technology and generative artificial intelligence.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] The user launches a dedicated application on their device and inputs information about family members, preferences, food possessions, and health status. This input data includes information such as the number of family members, taste preferences, what's in the refrigerator, and whether they are health-conscious. The device converts this information into digital data and prepares it for analysis of emotional state.

[0531] Step 2:

[0532] The device uses its built-in camera and microphone to analyze the user's emotional state. Specifically, the camera captures the user's facial expressions, and the microphone collects the tone of their voice. Based on this data, the emotion engine determines the user's current emotional state and records it as emotional data. For example, a smile and a calm voice indicate a relaxed state, while a depressed expression and a weak voice indicate a tired state.

[0533] Step 3:

[0534] The terminal combines the input information and the analyzed emotional state into a single data package and sends it to the server via the internet. The transmitted data includes the information entered by the user and the emotional state analyzed by the emotion engine. This package serves as the base data for recipe generation using generative artificial intelligence.

[0535] Step 4:

[0536] The server analyzes the received data package and generates recipes using a generative AI model. Through data analysis, it selects recipes that align with the user's preferences and emotions, and customizes them as needed. For example, if the user is tired, a simple and nutritious recipe will be selected. The generated recipes are created through a different, personalized process for each user.

[0537] Step 5:

[0538] The server sends the generated recipe to the terminal. The receiving terminal displays the recipe on the application screen, allowing the user to check the cooking details. The recipe includes detailed information on the ingredients needed, the procedure, and the cooking time, providing the user with the information to start cooking immediately.

[0539] Step 6:

[0540] The user follows the instructions on the device to cook and enjoy the finished dish.

[0541] Step 7:

[0542] The terminal receives feedback from the user after cooking and sends it to the server. This feedback is used as important data to improve future recipe suggestions. The server receives the feedback and continuously improves the system's recipe generation algorithm and other suggestion methods.

[0543] (Application Example 2)

[0544] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0545] Modern consumers, with their diverse needs and busy lifestyles, demand efficient and personalized meal suggestions. However, traditional meal suggestion systems are based on general information and struggle to consider the emotional state and immediate preferences of individual consumers. Furthermore, real-world store environments lack effective ingredient suggestions and cooking support. Therefore, there is a growing need for a system that accurately understands the emotional state of users and suggests ingredients and cooking methods accordingly.

[0546] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0547] In this invention, the server includes means for receiving information input by a user, means for analyzing the input information and the user's emotional state, means for generating ingredient suggestions and cooking procedures using generative artificial intelligence based on the analysis results, means for displaying the generated suggestions and cooking procedures on a display device, and means for receiving user feedback and updating the generative artificial intelligence to improve the suggestions. This makes it possible to suggest appropriate ingredients and provide cooking support according to the user's emotional state even in physical stores.

[0548] A "user" is an individual who operates this system and receives ingredient suggestions and cooking assistance.

[0549] "Information" refers to data entered by users, such as family structure, preferences, food items owned, and physical condition.

[0550] "Emotional state" refers to the psychological state or mood analyzed from the user's facial expressions and voice.

[0551] "Analysis" is the process of collecting and evaluating input information and the user's emotional state as data, and making decisions based on that data.

[0552] "Generative artificial intelligence" is an AI technology that creates appropriate ingredient suggestions and cooking procedures based on input information and analyzed emotional states.

[0553] "Ingredient suggestion" is a process of selecting and recommending ingredients suitable for the user based on the analysis results.

[0554] "Cooking instructions" refer to the cooking methods and processes created by generative artificial intelligence and presented to the user.

[0555] A "display device" is a device that visually shows the generated suggestions and cooking procedures to the user.

[0556] "Feedback" refers to the opinions and evaluations that users give regarding the suggestions and services they have actually received.

[0557] "Updating" refers to the process of improving the accuracy and content of system suggestions based on feedback.

[0558] A "physical store" is an environment where consumers can directly access goods and services at a physical sales location.

[0559] This invention includes a system that provides appropriate ingredients and cooking procedures based on the user's emotional state and input information. The system is primarily implemented through in-store terminals or tablet devices.

[0560] First, users enter their information into tablets or compatible terminals installed in the store. This includes family structure, preferences, food inventory, and physical condition. Furthermore, the user's facial expressions and voice tone are collected using a camera and microphone and analyzed by an emotion engine. This analysis utilizes emotion recognition software such as Google Cloud Vision API and Microsoft Azure's Computer Vision.

[0561] The server receives information and emotional data transmitted from the terminal and performs analysis using generative artificial intelligence. This analysis selects the most suitable ingredients for the user's current emotional state and needs, and generates simple cooking instructions. Ingredient suggestions are made using generative AI (such as OpenAI's GPT model).

[0562] Next, the suggestions generated by the server are displayed in real time on the tablet or device screen. This information allows users to shop more efficiently in-store. Furthermore, feedback from users is received, and the suggestions generated by the artificial intelligence are continuously updated and improved.

[0563] For example, if a user types "I'm a little tired today" into their device and the camera detects a tired expression, the system will suggest a recipe for "tofu and spinach soup" that can be prepared quickly. An example of a prompt to the generating AI would be, "The system has detected that the user is tired. Please generate a recipe for a quick, healthy, and mild-tasting dinner."

[0564] In this way, a system is created that can enhance the shopping experience at physical stores.

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The device receives information from the user as input, such as family structure, preferences, food items owned, and physical condition. It also uses its built-in camera and microphone to capture facial expressions and voice tone, processing this as part of the input. Based on this information, the emotion engine analyzes the user's emotional state and outputs it as a variable.

[0568] Step 2:

[0569] The server receives information sent from the terminal and analyzed sentiment data as input. Based on this information, a generative AI model (e.g., OpenAI's GPT model) operates to devise appropriate ingredient selections and cooking procedures. In this process, the generative AI uses prompt statements based on the input data to create appropriate output.

[0570] Step 3:

[0571] The server processes the calculation results of the generative artificial intelligence and obtains suitable ingredient names and cooking procedures as output. In this action, the server compiles specific suggestions tailored to the user's needs and prepares the final output.

[0572] Step 4:

[0573] The terminal receives suggested ingredients and cooking instructions from the server. The terminal displays this information on its screen for the user to review. This display function allows the user to quickly identify the suggested ingredients and proceed with shopping and cooking smoothly.

[0574] Step 5:

[0575] The user actually procures and cooks based on the suggested information and provides feedback on the results. When the terminal receives this feedback, the server uses the data to update the generative artificial intelligence, improving the accuracy of future suggestions.

[0576] Step 6:

[0577] The server analyzes the new feedback data and uses it to retrain or adjust the generated AI model. This improves the overall functionality of the system and enables more adaptive suggestions in the future.

[0578] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0579] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0580] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0581] [Fourth Embodiment]

[0582] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0583] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0584] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0585] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0586] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0587] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0588] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0589] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0590] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0591] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0592] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0593] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0594] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0595] This invention provides a system that uses data-driven, generative artificial intelligence to suggest appropriate recipes, thereby efficiently supporting everyday meal preparation at home. The system is built around three elements: a home terminal, a cloud-based server, and the user.

[0596] Users use a device with a dedicated application installed to input data such as their family composition, food inventory, individual preferences, and health status for the day. For example, they might input "2 adults, 1 child" for family composition, "chicken, carrots, potatoes" for food in the refrigerator, and "likes spicy food" for preferences. This data entered by the user on the device is sent to the server.

[0597] The server runs a program to analyze the received data. This program uses generative artificial intelligence to generate the optimal recipe tailored to the user based on the collected information. For example, based on the entered family composition and ingredient data, it might suggest "chicken curry stew" and generate a recipe with easy-to-follow instructions for microwave cooking.

[0598] The device displays recipe information sent from the server to the user. This allows the user to easily obtain the information needed to prepare meals for the day, reducing the burden of cooking. Furthermore, users can provide feedback through the app, enabling the system to continuously improve its performance.

[0599] This system plays a crucial role in providing menus that ensure users receive a balanced intake of necessary nutrients, especially when they are busy or feeling unwell. In this way, it makes it possible to prepare daily meals more efficiently while considering the health of the entire family.

[0600] The following describes the processing flow.

[0601] Step 1:

[0602] The user launches a dedicated application and enters information about their family composition, preferences, the contents of their refrigerator, and their physical condition for the day. This information is entered using pull-down menus and text input fields.

[0603] Step 2:

[0604] The terminal formats the data entered by the user in bulk and sends it to the server as a JSON data packet. During this process, the communication is encrypted to protect user information.

[0605] Step 3:

[0606] The server decompresses the received data packets to analyze them, identifying and separating each information element. This process verifies the integrity and completeness of the data.

[0607] Step 4:

[0608] The server activates a generative artificial intelligence algorithm based on the analyzed data. This AI searches for and filters recipes that are tailored to the type of ingredients, health condition, and preferences.

[0609] Step 5:

[0610] The server customizes the generated candidate recipes to their optimal form. This customization is based on the cooking time, cooking method, and nutritional balance of the dishes.

[0611] Step 6:

[0612] The server selects the final recipe to suggest to the user and sends it to the terminal in a documented format. The data sent includes cooking instructions, details of required ingredients, and an estimated cooking time.

[0613] Step 7:

[0614] The terminal displays recipe information received from the server on the application screen. Based on this information, the user can begin preparing the specific dish.

[0615] Step 8:

[0616] Users perform cooking and, if necessary, provide feedback on the results and their satisfaction level within the application. This feedback is sent to the server to improve the service in the future.

[0617] (Example 1)

[0618] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0619] In today's busy society, preparing daily meals at home is a burden for many people. Furthermore, planning nutritionally balanced meals and selecting dishes to suit specific preferences is not easy. Moreover, while providing meals that are appropriate for one's health needs is crucial amidst busy schedules, achieving this with limited information and time is difficult. This invention aims to support efficient meal preparation at home by quickly and accurately proposing optimal meal configurations based on the specific circumstances of each household, thereby addressing these challenges.

[0620] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0621] In this invention, the server includes means for receiving information provided by the user, means for generating meal planning procedures based on the information using generative artificial intelligence, and means for transmitting the generated procedures to the user through a display device. This makes it possible for the user to easily plan and prepare efficient and balanced meals tailored to their individual preferences and health conditions.

[0622] "Users" refer to individuals who use the system to provide information and utilize the suggestions they receive.

[0623] "Information" refers to data that the system receives from users, such as demographics, preferences, food possessions, and health status.

[0624] "Generative artificial intelligence" refers to a technology that analyzes received information and generates solutions and suggestions tailored to individual conditions.

[0625] "Meal preparation instructions" refer to a set of instructions that outlines the preparation and cooking of a meal using specific ingredients and seasonings.

[0626] A "display device" refers to equipment or screens used to visually convey information and suggestions obtained from a system to the user.

[0627] "Evaluation" refers to feedback that users provide regarding the information provided by the system, and this data is useful for improving the system.

[0628] This invention is a system designed to streamline everyday meal preparation at home. The system utilizes a terminal, a cloud-based server, and a generative AI model. It uses user-input data to automatically generate optimal meal recipes tailored to individual circumstances and preferences.

[0629] Users install a dedicated application on their home device. Through this application, users input information such as family composition, a list of ingredients they own, their preferences, and their health status. For example, they can input a family composition of "2 adults and 1 child," an inventory of ingredients such as "chicken, carrots, and potatoes," and a preference for "spicy food." This information is transmitted from the device to a server in the cloud.

[0630] The server drives a generative AI model based on the received data and generates recipes based on the provided conditions. This AI model has an algorithm that identifies the most suitable meal configuration for the user from specific information.

[0631] The generated recipe is sent to the device, including cooking instructions and necessary nutritional information. The device displays this information to the user in a visually easy-to-understand format. This allows the user to easily and quickly obtain guidance for preparing daily meals.

[0632] As a concrete example, here is an example of a prompt statement:

[0633] "Today I'm preparing a meal for a family of two adults and one child. I have chicken, carrots, and potatoes in the refrigerator. I like spicy food, so I'd appreciate some simple recipes using these ingredients."

[0634] In this way, the system provides an environment in which users can efficiently and effectively create meal plans tailored to their individual needs.

[0635] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0636] Step 1:

[0637] Users launch a dedicated application on their home device and input information about their family structure, available ingredients, preferences, and health status. This input data includes details such as "2 adults, 1 child," "chicken, carrots, potatoes," and "likes spicy food." This information serves as the basis for generating the optimal recipe in subsequent processing.

[0638] Step 2:

[0639] The terminal sends information entered by the user to a cloud-based server. The data processing performed here involves the terminal formatting the input data into a standardized format and transferring it to the server using a secure communication protocol. As a result of the transmission, information that meets the user's requirements is collected on the server side.

[0640] Step 3:

[0641] The server analyzes the received data and generates the optimal meal plan using a generative AI model. First, the server sets the input family composition and preferences as parameters for the AI ​​model and designs a recipe suitable for the user while referring to the ingredient database. This data calculation generates a specific recipe suggestion, such as "Chicken Curry Stew." The generated recipe, along with detailed cooking instructions, is then passed on to the next step.

[0642] Step 4:

[0643] The terminal receives recipes sent from the server and presents them to the user in a visually appealing and user-friendly interface. Specifically, the terminal displays the cooking process of the recipe step by step, making it easy for the user to follow. The user can then prepare a meal based on this information.

[0644] Step 5:

[0645] After completing a dish, users provide feedback to the system through the application. This feedback includes aspects such as "satisfaction with the dish" and "areas for improvement," and is used to enhance the accuracy of future recipe generation. This feedback input enables the system to continuously improve.

[0646] (Application Example 1)

[0647] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0648] Traditional food delivery services have faced challenges in providing personalized meal suggestions based on users' preferences and health conditions, making it difficult for them to choose appropriate dishes. Furthermore, ordering meals that meet specific criteria is difficult amidst busy daily lives.

[0649] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0650] In this invention, the server includes means for receiving data entered by a user, means for constructing dish suggestions using generative artificial intelligence based on the entered data, and means for presenting the suggested dishes to the user. This enables the suggestion of dishes tailored to the user's individual needs and allows for easy ordering.

[0651] "User" refers to an entity that uses the system to receive dish suggestions or place orders.

[0652] "Data" refers to information necessary to generate cooking suggestions, such as member information, preferences, food possessions, and health status, which are entered by the user.

[0653] "Generative artificial intelligence" refers to a machine learning model or algorithm that generates optimal cooking suggestions based on data received from users.

[0654] "Cooking suggestions" refers to meal options or recipes created by generative artificial intelligence and presented to the user.

[0655] A "terminal" refers to an electronic device used by users to operate the system, receive food suggestions, and place orders.

[0656] "Available for order" refers to a state where users can proceed directly to the purchase process for the suggested dishes within the system.

[0657] The system implementing this invention provides users with an electronic device such as a smartphone as a terminal. The terminal communicates with a cloud-based server, receives user input data, and performs the necessary processing. Users can easily input data such as their family members, preferences, food possessions, and health status through an application.

[0658] First, the application installed on the device starts running. The application sends the entered data to a cloud server, which then uses generative artificial intelligence to generate personalized cooking suggestions for each user. The generated suggestions are then sent back to the device and presented to the user.

[0659] The servers are built on cloud platforms such as Amazon Web Services and Google Cloud, and can utilize APIs provided by companies like OpenAI and Google as generative artificial intelligence models. This system allows users to quickly find dishes that perfectly match their preferences and circumstances and order them on their devices.

[0660] For example, if a family with elementary school-aged children is looking for a nutritious and spicy meal, this system can suggest "Spicy Tofu Chili" as a dish option. The user can then easily confirm their order.

[0661] An example of a prompt sentence for a generative AI model is: "Female, 30s, lives in Tokyo, likes spicy food, allergy: nuts. Suggest healthy and easy-to-order menu items." This allows the generative artificial intelligence to make appropriate suggestions and broaden the user's culinary options.

[0662] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0663] Step 1:

[0664] The user launches the application on their device and enters data such as member information, preferences, food possessions, and health status. The entered data is appropriately formatted within the application and prepared for transmission to the server.

[0665] Step 2:

[0666] The device sends formatted user data to a cloud-based server. The data is securely transmitted via a secure protocol and stored in a database on the server side.

[0667] Step 3:

[0668] The server inputs the received data into the generative AI model and begins analyzing the data. The generative AI model forms optimal suggestions based on the input data. In this process, data pattern analysis and contextual understanding are performed, and the cooking suggestions that best suit the user's requests are generated.

[0669] Step 4:

[0670] The AI ​​model generates a cooking suggestion on the server. This suggestion includes the dish name, cooking outline, and required ingredients, and is formatted for presentation to the user.

[0671] Step 5:

[0672] The server sends the generated recipe suggestions to the terminal. The transmitted data is received by the user's terminal, and the application reads it and prepares to display it on the user interface.

[0673] Step 6:

[0674] Users review the dish suggestions displayed on the terminal and select their preferred dishes. The selected dishes are then prepared for purchase on the terminal. During this process, they confirm the order details and enter their delivery information.

[0675] Step 7:

[0676] Once an order is confirmed, the terminal sends the order data to the server, initiating the food delivery process. The server then makes the appropriate delivery arrangements and issues instructions for cooking and delivery of the food.

[0677] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0678] This invention integrates emotion recognition functionality into a system that supports home cooking, thereby improving user satisfaction and cooking efficiency by providing suggestions more tailored to individual needs. The system generates, customizes, and presents recipes based on data provided by the user and an emotion engine.

[0679] Users input information such as family structure, preferences, contents of their refrigerator, and health status using a dedicated application on their device. The device's built-in camera and microphone are also used by an emotion engine to analyze the user's emotional state based on their tone of voice and facial expressions. Specifically, this might occur if the user says something like, "I'm feeling a little tired today," or if the camera detects signs of stress on the user's face.

[0680] Once the input data and emotional state are collected, the device sends this data to the server. The server analyzes the received information and uses generative artificial intelligence to derive an appropriate recipe. Here, taking into account the results of the emotion recognition, if, for example, the user is determined to be tired, it can suggest a recipe with a simple cooking process and minimal effort.

[0681] For example, for a user who is "hungry but tired," the server can suggest a mild-tasting recipe such as "Creamy Chicken Stew Made in the Microwave." This recipe provides nutrients in a short amount of time.

[0682] The device displays recipe information received from the server on the application screen. Users can then cook and enjoy their meals based on these suggestions. Furthermore, user feedback obtained through the app allows the system to continuously improve its suggestions and enhance the user experience.

[0683] By combining an emotion engine with generative artificial intelligence in this way, it becomes possible to provide flexible and personalized meal suggestions that reflect the user's emotional state, significantly reducing the burden associated with daily meal preparation.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] The user launches a dedicated application and enters information about their family structure, preferences, refrigerator contents, and health condition into the terminal. This information can be entered via text or selection options.

[0687] Step 2:

[0688] The user further utilizes the device's camera and microphone, allowing the emotion engine to recognize their current emotional state from their facial expressions and voice. This data, including the user's tone of voice, changes in facial expressions, and gestures, is then analyzed by an emotion analysis algorithm.

[0689] Step 3:

[0690] The terminal combines the information entered by the user with the recognized emotional state and sends this as a data packet to the server. This transmission is encrypted, ensuring the protection of the information.

[0691] Step 4:

[0692] The server analyzes the received data. This analysis first involves formatting the data and identifying each data point. Then, generative artificial intelligence is used to generate a recipe suitable for the data content.

[0693] Step 5:

[0694] The server takes into account the analysis results of the emotion engine and adjusts the resulting recipes according to the user's emotional state. For example, if it is recognized that the user is feeling "I want to relax," the server will prioritize dishes that are expected to have a relaxing effect.

[0695] Step 6:

[0696] The server sends the final recipe to the user's terminal. This data includes cooking instructions, a list of required ingredients, estimated cooking time, and brief advice.

[0697] Step 7:

[0698] The device displays the received recipe information on the application screen. The user can then efficiently proceed with cooking according to this suggestion and complete the actual dish.

[0699] Step 8:

[0700] Users provide feedback on the dishes they create and the suggestions they make through the application. This feedback is sent to the server and used as data to further improve and personalize the service.

[0701] (Example 2)

[0702] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] Preparing meals at home presents a significant challenge, as it requires considering the diverse preferences and health conditions of each household member, as well as the user's emotions. For example, when a user is tired, they may need a simple yet nutritionally balanced meal, but searching for and selecting an appropriate recipe is time-consuming and laborious. Therefore, automating recipe suggestions that take the user's emotional state into account is necessary to reduce the user's burden and improve meal satisfaction.

[0704] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0705] In this invention, the server includes means for collecting information entered by the user, means for analyzing the emotional state based on the collected information, and means for generating personalized cooking recipes using generative artificial intelligence based on the analyzed emotional state and the entered information. This makes it possible to suggest recipes that take into account the user's emotional state and individual needs.

[0706] "Information entered by users" refers to data on member information, preferences, food possession status, and health status, which users provide to the system via their devices.

[0707] "Methods for analyzing emotional states" refer to technologies that analyze the user's facial expressions and voice tone collected using the camera and microphone installed in the device, and evaluate their emotions at that time.

[0708] "A method for generating personalized cooking recipes using generative artificial intelligence" is a technology that utilizes generative AI models to suggest customized dishes tailored to the user's needs, based on the information received and analyzed emotional data.

[0709] "Means of presenting generated recipes to users" refers to a function that displays the recipes generated on the server on the application screen of the terminal in a format that the user can view.

[0710] This invention is a system that supports meal preparation at home. By integrating emotion recognition and generative artificial intelligence, it aims to make meal preparation more efficient and satisfying by suggesting recipes tailored to the user's needs. This system consists of a terminal used by the user and a server that analyzes the data.

[0711] First, the user uses a dedicated application on the device to input information such as household composition, preferences, types of food in the refrigerator, and health status. The device collects this information and then uses its built-in camera and microphone to analyze the user's facial expressions and voice tone to understand their emotional state.

[0712] Next, the device sends the collected user data and emotional data to the server. The server receives this data and uses generative artificial intelligence to generate personalized recipes that take into account the emotional state and other data content. For example, if the user is tired, the system might suggest a recipe that can be easily made in a microwave.

[0713] The generated recipe is sent from the server to the terminal and displayed on the application screen. Users can follow the instructions on this screen to cook and enjoy a meal with their family. Furthermore, users can provide feedback after cooking, allowing the system to incorporate this feedback and improve the accuracy of its suggestions.

[0714] As a concrete example of its use, a user's prompt message on a given day might be, "Suggest a simple, healthy recipe using the ingredients I have in my refrigerator." This simple request is then transformed by the system into a personalized suggestion that is best suited to the user.

[0715] In this way, this system can transform the daily process of preparing meals into an easier and more enjoyable one by utilizing the latest emotion recognition technology and generative artificial intelligence.

[0716] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0717] Step 1:

[0718] The user launches a dedicated application on their device and inputs information about family members, preferences, food possessions, and health status. This input data includes information such as the number of family members, taste preferences, what's in the refrigerator, and whether they are health-conscious. The device converts this information into digital data and prepares it for analysis of emotional state.

[0719] Step 2:

[0720] The device uses its built-in camera and microphone to analyze the user's emotional state. Specifically, the camera captures the user's facial expressions, and the microphone collects the tone of their voice. Based on this data, the emotion engine determines the user's current emotional state and records it as emotional data. For example, a smile and a calm voice indicate a relaxed state, while a depressed expression and a weak voice indicate a tired state.

[0721] Step 3:

[0722] The terminal combines the input information and the analyzed emotional state into a single data package and sends it to the server via the internet. The transmitted data includes the information entered by the user and the emotional state analyzed by the emotion engine. This package serves as the base data for recipe generation using generative artificial intelligence.

[0723] Step 4:

[0724] The server analyzes the received data package and generates recipes using a generative AI model. Through data analysis, it selects recipes that align with the user's preferences and emotions, and customizes them as needed. For example, if the user is tired, a simple and nutritious recipe will be selected. The generated recipes are created through a different, personalized process for each user.

[0725] Step 5:

[0726] The server sends the generated recipe to the terminal. The receiving terminal displays the recipe on the application screen, allowing the user to check the cooking details. The recipe includes detailed information on the ingredients needed, the procedure, and the cooking time, providing the user with the information to start cooking immediately.

[0727] Step 6:

[0728] The user follows the instructions on the device to cook and enjoy the finished dish.

[0729] Step 7:

[0730] The terminal receives feedback from the user after cooking and sends it to the server. This feedback is used as important data to improve future recipe suggestions. The server receives the feedback and continuously improves the system's recipe generation algorithm and other suggestion methods.

[0731] (Application Example 2)

[0732] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0733] Modern consumers, with their diverse needs and busy lifestyles, demand efficient and personalized meal suggestions. However, traditional meal suggestion systems are based on general information and struggle to consider the emotional state and immediate preferences of individual consumers. Furthermore, real-world store environments lack effective ingredient suggestions and cooking support. Therefore, there is a growing need for a system that accurately understands the emotional state of users and suggests ingredients and cooking methods accordingly.

[0734] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0735] In this invention, the server includes means for receiving information input by a user, means for analyzing the input information and the user's emotional state, means for generating ingredient suggestions and cooking procedures using generative artificial intelligence based on the analysis results, means for displaying the generated suggestions and cooking procedures on a display device, and means for receiving user feedback and updating the generative artificial intelligence to improve the suggestions. This makes it possible to suggest appropriate ingredients and provide cooking support according to the user's emotional state even in physical stores.

[0736] A "user" is an individual who operates this system and receives ingredient suggestions and cooking assistance.

[0737] "Information" refers to data entered by users, such as family structure, preferences, food items owned, and physical condition.

[0738] "Emotional state" refers to the psychological state or mood analyzed from the user's facial expressions and voice.

[0739] "Analysis" is the process of collecting and evaluating input information and the user's emotional state as data, and making decisions based on that data.

[0740] "Generative artificial intelligence" is an AI technology that creates appropriate ingredient suggestions and cooking procedures based on input information and analyzed emotional states.

[0741] "Ingredient suggestion" is a process of selecting and recommending ingredients suitable for the user based on the analysis results.

[0742] "Cooking instructions" refer to the cooking methods and processes created by generative artificial intelligence and presented to the user.

[0743] A "display device" is a device that visually shows the generated suggestions and cooking procedures to the user.

[0744] "Feedback" refers to the opinions and evaluations that users give regarding the suggestions and services they have actually received.

[0745] "Updating" refers to the process of improving the accuracy and content of system suggestions based on feedback.

[0746] A "physical store" is an environment where consumers can directly access goods and services at a physical sales location.

[0747] This invention includes a system that provides appropriate ingredients and cooking procedures based on the user's emotional state and input information. The system is primarily implemented through in-store terminals or tablet devices.

[0748] First, users enter their information into tablets or compatible terminals installed in the store. This includes family structure, preferences, food inventory, and physical condition. Furthermore, the user's facial expressions and voice tone are collected using a camera and microphone and analyzed by an emotion engine. This analysis utilizes emotion recognition software such as Google Cloud Vision API and Microsoft Azure's Computer Vision.

[0749] The server receives information and emotional data transmitted from the terminal and performs analysis using generative artificial intelligence. This analysis selects the most suitable ingredients for the user's current emotional state and needs, and generates simple cooking instructions. Ingredient suggestions are made using generative AI (such as OpenAI's GPT model).

[0750] Next, the suggestions generated by the server are displayed in real time on the tablet or device screen. This information allows users to shop more efficiently in-store. Furthermore, feedback from users is received, and the suggestions generated by the artificial intelligence are continuously updated and improved.

[0751] For example, if a user types "I'm a little tired today" into their device and the camera detects a tired expression, the system will suggest a recipe for "tofu and spinach soup" that can be prepared quickly. An example of a prompt to the generating AI would be, "The system has detected that the user is tired. Please generate a recipe for a quick, healthy, and mild-tasting dinner."

[0752] In this way, a system is created that can enhance the shopping experience at physical stores.

[0753] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0754] Step 1:

[0755] The device receives information from the user as input, such as family structure, preferences, food items owned, and physical condition. It also uses its built-in camera and microphone to capture facial expressions and voice tone, processing this as part of the input. Based on this information, the emotion engine analyzes the user's emotional state and outputs it as a variable.

[0756] Step 2:

[0757] The server receives information sent from the terminal and analyzed sentiment data as input. Based on this information, a generative AI model (e.g., OpenAI's GPT model) operates to devise appropriate ingredient selections and cooking procedures. In this process, the generative AI uses prompt statements based on the input data to create appropriate output.

[0758] Step 3:

[0759] The server processes the results of the generative artificial intelligence calculations and obtains suitable ingredient names and cooking procedures as output. In this action, the server compiles specific suggestions tailored to the user's needs and prepares the final output.

[0760] Step 4:

[0761] The terminal receives suggested ingredients and cooking instructions from the server. The terminal displays this information on its screen for the user to review. This display function allows the user to quickly identify the suggested ingredients and proceed with shopping and cooking smoothly.

[0762] Step 5:

[0763] The user actually procures and cooks based on the suggested information and provides feedback on the results. When the terminal receives this feedback, the server uses the data to update the generative artificial intelligence, improving the accuracy of future suggestions.

[0764] Step 6:

[0765] The server analyzes the new feedback data and uses it to retrain or adjust the generated AI model. This improves the overall functionality of the system and enables more adaptive suggestions in the future.

[0766] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0767] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0768] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0769] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0770] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0771] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0772] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0773] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0774] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0775] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0776] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0777] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0778] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0779] 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.

[0780] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0781] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0782] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0783] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0784] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0785] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0786] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0787] The following is further disclosed regarding the embodiments described above.

[0788] (Claim 1)

[0789] A means of receiving data entered by the user,

[0790] A means for generating a cooking recipe using generative artificial intelligence based on the input data,

[0791] A means of presenting the generated recipe to the user,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, characterized in that the input data relates to family structure, preferences, food possessions, and physical condition.

[0795] (Claim 3)

[0796] The system according to claim 1, characterized in that the generative artificial intelligence analyzes the input data and customizes recipes to meet the user's needs.

[0797] "Example 1"

[0798] (Claim 1)

[0799] Means for receiving information provided by users,

[0800] Based on this information, a means for generating meal planning procedures using generative artificial intelligence,

[0801] A means for communicating the generated procedure to the user through a display device,

[0802] A means of receiving user feedback and improving system performance,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, characterized in that the provided information relates to population composition, preferences, food possessions, and health status.

[0806] (Claim 3)

[0807] The system according to claim 1, characterized in that the generative artificial intelligence analyzes the provided information and adjusts the meal composition to suit the user's requirements.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] A means of receiving data entered by the user,

[0811] A means for constructing cooking suggestions using generative artificial intelligence based on the input data,

[0812] A means of presenting the proposed dish to the user,

[0813] A means to enable users to order suggested dishes on their devices,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, characterized in that the input data relates to the members, preferences, food possessions, and health status.

[0817] (Claim 3)

[0818] The system according to claim 1, characterized in that the generative artificial intelligence analyzes the input data and adjusts the dishes according to the user's requests.

[0819] "Example 2 of combining an emotion engine"

[0820] (Claim 1)

[0821] Means for collecting information entered by users,

[0822] A means for analyzing emotional states based on the collected information,

[0823] A means for generating personalized cooking recipes using generative artificial intelligence based on the analyzed emotional state and the input information,

[0824] A means of presenting the generated recipe to the user,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, characterized in that the collected information relates to member information, preferences, food possession status, and health status.

[0828] (Claim 3)

[0829] The system according to claim 1, characterized in that the generative artificial intelligence customizes a recipe to meet the user's needs, taking into account the analyzed emotional state.

[0830] "Application example 2 when combining with an emotional engine"

[0831] (Claim 1)

[0832] A means of receiving information entered by the user,

[0833] A means for analyzing the input information and the user's emotional state,

[0834] A means for suggesting ingredients and generating cooking procedures using generative artificial intelligence based on the analysis results,

[0835] A means for displaying the generated proposal content and cooking procedure on a display device,

[0836] A means of receiving user feedback and updating generative artificial intelligence to improve the suggestions,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, characterized in that the input information relates to family structure, preferences, food possessions, and physical condition, and further relates to analyzing the user's emotional state from their facial expressions and voice.

[0840] (Claim 3)

[0841] The system according to claim 1, characterized in that the generative artificial intelligence analyzes the input information and the user's emotional state, and customizes the ingredients and cooking procedure according to the user's state. [Explanation of symbols]

[0842] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving data entered by the user, A means for generating a cooking recipe using generative artificial intelligence based on the input data, A means of presenting the generated recipe to the user, A system that includes this.

2. The system according to claim 1, characterized in that the input data relates to family structure, preferences, food possessions, and physical condition.

3. The system according to claim 1, characterized in that the generative artificial intelligence analyzes the input data and customizes recipes to meet the user's needs.

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